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Efficient phase estimation for the classification of digitally phase modulated signals using the crossWVD: a performance evaluation and comparison with the Stransform
EURASIP Journal on Advances in Signal Processing volume 2012, Article number: 65 (2012)
Abstract
This article presents a novel algorithm based on the crossWignerVille Distribution (XWVD) for optimum phase estimation within the class of phase shift keying signals. The proposed method is a special case of the general class of cross timefrequency distributions, which can represent the phase information for digitally phase modulated signals, unlike the quadratic timefrequency distributions. An adaptive window kernel is proposed where the window is adjusted using the localized lag autocorrelation function to remove most of the undesirable duplicated terms. The method is compared with the Stransform, a hybrid between the shorttime Fourier transform and wavelet transform that has the property of preserving the phase of the signals as well as other key signal characteristics. The peak of the timefrequency representation is used as an estimator of the instantaneous information bearing phase. It is shown that the adaptive windowed XWVD (AWXWVD) is an optimum phase estimator as it meets the CramerRao Lower Bound (CRLB) at signaltonoise ratio (SNR) of 5 dB for both binary phase shift keying and quadrature phase shift keying. The 8 phase shift keying signal requires a higher threshold of about 7 dB. In contrast, the Stransform never meets the CRLB for all range of SNR and its performance depends greatly on the signal's frequency. On the average, the difference in the phase estimate error between the Stransform estimate and the CRLB is approximately 20 dB. In terms of symbol error rate, the AWXWVD outperforms the Stransform and it has a performance comparable to the conventional detector. Thus, the AWXWVD is the preferred phase estimator as it clearly outperforms the Stransform.
1. Phase shift keying signals and the problem of phase estimation
Phase shift keying (PSK) is commonly used [1] due to better noise immunity and bandwidth efficiency compared to amplitude shift keying (ASK) and frequency shift keying (FSK) modulations [2]. This is reflected in current wireless communication technologies such as 3G, CDMA, WiMax, WiFi, and the 4G technologies that employ PSK modulation [3]. In addition, digital phase modulation is also used in HF data communication such as in PACTOR II/III, CLOVER 2000, STANAG 4285, and MIL STD 188110A/B format [4]. The instantaneous information bearing phase (IIBphase) in the class of PSK signal represents the transmitted symbol, the signal symbol duration, and class of PSK modulation scheme used. This information is useful to classify and demodulate signals.
1.1. Phase estimation and signal demodulation
Several phase estimation methods are proposed for PSK signal demodulation, interference cancellation, coherent communication over timevarying channels, and direction of arrival estimation [5–12]. Such phase estimation methods can be classified as coherent and noncoherent detections [13]. The coherent detector is often referred to as a maximum likelihood detector [13]. The term noncoherent refers to a detection scheme where the reference signal is not necessary to be in phase with the received signal. One of the earliest contributions for the phase estimation of binary phase shift keying (BPSK) signal is an optimum phase estimator which derives a reference signal from the received data itself using Costas loop [5]. In [6], an open loop phase estimation method for burst transmission is proposed. The phaselocked loop (PLL) method used in conventional timedivision multiple access system is inefficient due to the very long acquisition time. This problem is resolved using the new method proposed in this article which yields an identical performance with the PLL method. However, the frequency uncertainty problem degrades the performance of the estimator. In order to overcome this degradation, an improved algorithm which includes the frequency and phase offset is proposed in [7, 8]. By estimating the frequency and phase offset, the performance degradation caused by the frequency offset in [6] is eliminated. The work reported in [9–11] proposed a carrier phase estimator for orthogonal frequency division multiple access systems based on the expectationmaximization algorithm to overcome the computational burden of the likelihood function. This method is actually equivalent to the maximum likelihood phase estimation using an iterative method without any prior knowledge of the phase. Two practical MPSK phase detector structures for carrier synchronization PLLs were reported in [12]. These two new nondataaided phase detector structures are known as the selfnormalizing modification of the M thorder nonlinearity detector and the adaptive gain detector [12]. Both detectors show improvement in phase error variance due to automatic gain control circuit imperfections.
1.2. Phase estimation and signal classification
All the abovementioned methods aimed to develop an optimal phase estimator solely for signal demodulation without estimation of instantaneous parameters of the signals. The Costas loop and PLL are crucial for carrier recovery and synchronization in the demodulation of the class of PSK signals [5–8]. However, our applications focused on the analysis and classification of signals for spectrum monitoring. The main objective of such a system [14] is to determine the signal parameters such as the carrier frequency, signal power, modulation type, modulation parameters, symbol rate, and data format which are then used as input to a classifier network. This system is used by the military for intelligence gathering [15] and by the regulatory bodies [16] for verifying conformance to spectrum allocation. Recently, similar requirements were identified for spectrum sensing in cognitive radio [17] to determine channel occupancy and dynamically allocate channels to the various users. Spectrum monitoring systems also use data demodulation [14], but with modems tailored for the specific modulation type and data format.
Since PSK signals are timevarying in phase, timefrequency analysis [[1]8, p. 9] can be used to estimate the signal's instantaneous parameters. The development of signal dependent kernels for timefrequency distribution (TFD) applicable to the class of ASK and FSK signal was proposed in [19]. Further enhancement in [20] improved the timefrequency representation (TFR) by estimating the kernel parameters using the localized lag autocorrelation (LLAC) function. Recent study has proven that the quadratic TFD [21, 22] is capable to analyze and classify the class of ASK and FSK signals at very low signaltonoise ratio (SNR) conditions (2 dB). However, the loss of the phase information in the bilinear product computation makes it impractical to completely represent the PSK class of signals. Since PSK signals are characterized by the phase, cross timefrequency distributions (XTFD) based method is proposed as it is capable of representing the signal phase information [23]. Just like the quadratic TFD which suffers from the effect of cross terms, there are unwanted terms known as "duplicated terms"^{a} which are present in the XTFD. Preliminary work on the XTFD shows that a fixed window is insufficient to generate an accurate IIBphase estimation [23], thus justifying the need for an adaptive window.
This article presents a timefrequency analysis solution to the optimum phase estimation of PSK class of signals and then evaluates its performance. Signals tested are BPSK, QPSK, and 8PSK signals. The first method is based on the localized adaptive windowed cross WignerVille distribution (AWXWVD). In this method, the adaptation of the window width is based on the LLAC function of the signals of interest. For comparison, a second method is selected that is based on the Stransform [24]. It is an invertible timefrequency spectral localization technique that combines elements of the Wavelet transform (WT) and the shorttime Fourier transform (STFT). This Stransform is selected for comparison as it has the property of preserving the phase of a signal as well as retaining other key characteristics such as energy localization and instantaneous frequency [24].
This correspondence is organized as follows. Section 2 first describes the signal models used in this article and introduces the general representations of the quadratic TFDs, XTFD, and Stransform. Section 3 presents the general equations for cross bilinear product in timelag domain for both autoterms and duplicated terms together with the LLAC algorithm for estimating the adaptive window for the PSK class signals. Next, we present the method for IIBphase using the peak of the AWXWVD and Stransform. The CramerRao lower bound (CRLB) which is used for bench marking purposes is discussed in the following subsection. Section 4 presents the discrete time implementation of both method and the performance comparison of the AWXWVD with the Stransform in the presence of noise. The criteria of comparison are based on the TFR, constellation diagram, mainlobe width (MLW) and the phase estimate variance. Then, a comparison in terms of the computational complexity and symbol error rate is given. Conclusions are given in the following section. Throughout this article, we use the following terminology: TFDs represent the mathematical formulations for distributing the signal energy in both time and frequency; the actual representations obtained are called TFRs.
2. PSK Signals Model and TFRs
This section first introduces the model and the parameters for the PSK signals. It then describes the timefrequency analysis techniques used to represent and analyze the signals.
2.1. Signal model
Communication signals are timevarying and are mainly characterized by instantaneous parameters such as the instantaneous amplitude for ASK signals, instantaneous frequency (IF) for FSK signals, and the IIBphase for PSK signals. This section extends the concepts of IF to IIBphase for digitally phase modulated signals and describes the signal parameters used for analysis. A comprehensive review of IF estimation from the peak of the TFD is given in [25, 26]. A timevarying signal corresponding instantaneous phase is represented as
where f is the frequency of the signal and θ is the constant initial phase of the signal. The IF is obtained by taking the first derivative of the instantaneous phase.
The instantaneous phase given in [25] has a timevarying frequency and the phase is constant for all time. In contrast, for a digitally phase modulated signal the phase term is also timevarying. If we extend Equation (1) to represent a phase modulated signal, the instantaneous phase then becomes
where φ(t) is the IIBphase which is very crucial in defining digitally phase modulated signals as it contains information of the transmitted data. This article evaluates the comparative performance of the AWXWVD as an estimator of the IIBphase for BPSK, QPSK, and 8PSK signals and then compares the results with the Stransform as both methods claimed to provide accurate phase representation. Note that this study does not include the class of quadrature amplitude modulation signals (QAM). Even though this signal has IIBphase, its timevarying amplitude characteristic is not suitable for the adaptation algorithm described in this article (see Section 3.1.2). The algorithm is developed based on the assumption of constant amplitude signal such as the class of PSK signals.
In this article, the analytical form of the signal is used to minimize the effect of cross terms in the TFR [27]. Even though signals are real in practice, the analytical form of the signal can be generated using an FIR Hilbert filter [28]. Thus, an arbitrary digital phase modulated signal may be expressed as
where k represents the order of the binary sequence transmitted, A represents the signal amplitude, f_{ k } is the subcarrier frequency, φ_{ k } represents the information bearing phase, and T_{ b } is the symbol duration of the signals. The variables A and f_{ k } are constant as the signals considered are PSK signals. For simplification of notation, in this article, the box function ∏(t) is defined as
Figure 1 shows the time representations of the BPSK, QPSK, and 8PSK signals defined in Equation (4). The signal parameters are given in Table 1 and the sampling frequency is assumed to be 1 Hz. The analysis methods proposed in this article is applicable to communication applications in all frequency bands as long as they meet the Nyquist sampling theorem. Due to the frequency dependency of the Stransform, the analysis signals consist of both high and lowfrequency components so as to compare the performance of the AWXWVD and Stransform for phase estimation.
The received noisy signal can be modeled as
where z(t) is the noiseless PSK signal and v(t) is the complexvalued additive white Gaussian noise. The noise has independent and identically distributed real and imaginary parts with total variance {\sigma}_{v}^{\text{2}} and zero mean [[18], p. 437].
2.2. TFDs, cross TFDs, and Stransform
The quadratic TFD is a useful technique to analyze timevarying signals, but the resulting TFR does not represent phase directly. Due to the need to estimate IIBphase in PSK signals, the XTFD and the Stransform are introduced for this purpose as both can represent phase in the timefrequency domain.
2.2.1. Quadratic TFDs and cross TFDs
The quadratic TFD [[18], p. 66] uses the bilinear product of the signal of interest to generate a TFR. To represent the phase information in the timefrequency domain, the cross bilinear product in the XTFD is calculated using TFDs from both signal of interest and reference signal. The resulting formulation for the XTFD can be expressed as follows
where G(t, τ) is the timelag kernel function that can also be represented in the Dopplerlag domain as described in [18, 29]. The cross bilinear product K_{ zr } (t, τ) is given as
where z(t) is the analytical signal of interest and r(t) is the reference signal. The cross bilinear product is the instantaneous cross correlation function (ICF) between the signal of interest and the reference signal. Similar to the signal of interest, the reference signal can be defined as
But it does not contain IIBphase. A box function is used in the representation of the reference signal to keep track of the location of interaction between the signals of interest with the reference signal in the timelag representation. Similar study presented in [30, 31] on the use of XWVD for IF estimate of linear FM signals requires a reference signal identical to the signal of interest. However, this is not necessary for this application since the reference signal required is a pure sinusoid with the same frequency as the signal of interest. Hence, any power spectrum estimation method [[32], p. 214] can be used to determine the frequency of the received signal. From there, a pure sinusoid reference signal of the same frequency is generated. This article assumes that the signal of interest is in perfect synchronization with the reference signal. In practical applications, the presence of phase synchronization error introduces an offset in the IIBphase. This phase offset could be compensated using a PLL or Costas loop [33] to generate the reference signal. Furthermore, the computation of the XTFD is done based on a segment of received signal. Combining the features of the PLL and Costas loop is only possible if the XTFD is computed iteratively one sample at a time.
In the general formulation of the quadratic TFD [[18], p. 68], the various TFD such as the WignerVille distribution (WVD), ChoiWilliams distribution, spectrogram, Bdistribution, and other distributions can be defined by their respective timelag kernels. The choice of this kernel function can help minimize cross terms in the TFR. A separable kernel allows the flexibility to separately control the smoothing in the time and frequency domain [18, Section 5.7]. The kernel function for the windowed WVD (WWVD) is an example of a separable kernel. It performs smoothing only in the frequency direction to reduce the effect of the cross terms. Similar to the WWVD, the kernel function for the windowed XWVD (WXWVD) is defined as
Since the time component is a delta function, this kernel is independent of the Doppler variable and only a function of lag. The kernel is known as Dopplerindependent kernel [[18], p. 71], a special case of separable kernel. It is shown that such kernel applies onedimensional filtering and is adapted to only a particular kind of monocomponent signals such as nonlinear FM signals [[18], p. 214].Windowing is performed in the lag direction before taking the Fourier transform. Thus, the choice of separable kernel in Equation (10) causes smoothing only in the frequency direction.
By substituting Equation (10) into Equation (7), the WXWVD can be represented as
The lag window function w(τ) can be one of the window functions typically used in filter design or spectrum analysis.
2.2.2. The Stransform
The Stransform is a spectral localization technique which is very much similar to the WT and STFT [24]. It can be considered as a special case of the STFT by replacing the window function with a frequencydependent Gaussian window [24]. It is also related to the WT as it can be derived from the WT with a specific mother wavelet multiplied by the phase factor. The Gaussian window of the Stransform is scaled so that the window width is inversely proportional to the frequency, and its height is scaled linearly to the frequency. Due to the behavior of the window scaling, it possesses good time resolution for highfrequency components and good frequency resolution for lowfrequency components. This transform has successfully been applied for resolving problems in the field of geophysics [34], power quality analysis [35], and medicine [36]. The original formulation for the Stransform of a signal, z(t), is given as [24]
The frequencydependent Gaussian window g(t, f) is given as [24]
where f is the signal frequency and τ in Equation (12) denotes the position of the midpoint of the window. The window spread or standard deviation depends on f. Based on the characteristics of the Gaussian distribution, a window width of 6/f ensures that 99.72% of the signal values are enclosed within the window function [37]. Therefore, the window width is given as 6/f and height is given by the term \leftf\right/\sqrt{2\pi}. The term \leftf\right/\sqrt{2\pi} is also a normalizing factor which ensures that S (t, f) converges to Z(f) when averaged over time [36], as shown below.
Proof
Thus, the Stransform is invertible and the original signal can be recovered by taking the inverse Fourier transform of the above equation, resulting in the following expression of z(t).
3. Phase estimation methodology
This section describes the characteristics of the cross bilinear product in the timelag representation and outlines the derivation of the AWXWVD. The adaptation method used to set up the localized lag adaptive window is then discussed. Next, the method used for phase estimation from the peak of the TFR is presented.
3.1. AWXWVD
The Stransform can be applied directly to the class of PSK signals to obtain the TFR without any modification in the algorithm. However, this is not the case with the XTFD where interference due to duplicated terms is introduced in the TFR [23]. Previous study defined methods to determine optimum windows for TFDs [38, 39] that can reduce cross terms. A window matching algorithm [39] is used to determine the optimum window for a TFD at all time instant. The algorithm iteratively evaluates the localized energy distribution to minimize the error between successive window estimates. The concept of timefrequency coherence is introduced in [38] where the XWVD and WVD for each signal components are used in its computation. The required window function is estimated based on the autoregressive moving average modeling and Karhunen Loeve expansion. In this PSK communication application, the cross bilinear product has a certain pattern that can be utilized in computing the optimum window. Therefore, the adaptive window is designed based on the characteristics of the cross bilinear product. The resulting distribution, the AWXWVD, can generate an accurate TFR and the subsequent IIBphase estimate.
3.1.1. The cross bilinear product
The cross bilinear product consists of autoterms and duplicated terms. The duplicated terms carry the same information as the autoterms but shifted in time and lag domain; therefore, it can cause interference to the autoterms [23]. In order to obtain an accurate XTFR, the autoterms must be preserved and the duplicated terms must be removed or attenuated. The autoterms and duplicated terms for any PSK class signal can be expressed as
where K_{∏}(t, τ) is the instantaneous autocorrelation function of the box function given as
The proofs for Equations (16) and (17) are given in Appendix 1.
In practical digital communication applications, the amplitude of the signal might not be ideally constant due to channel impairments such as multipath fading, attenuation by the propagation channel and any kind of amplification performed by the circuits at both the transmitter and receiver [13]. Therefore, the variable A is retained throughout the derivation of the cross bilinear product. Other than that, signals that combine amplitude and phase modulations such as QAM can also be used provided a suitable adaptation algorithm for the XTFD is designed. The variation in the amplitude, A, caused by the transmitted binary data will correspond to the variation in the energy represented in the XTFR.
Figure 2 shows the graphical representation of the above cross bilinear product. All the autoterms lie along the τ = 0 axis, whereas the duplicated terms are shifted in both time and lag. Therefore, the terms labeled as K_{zr 1,2}, K_{zr 1,3}, and K_{zr 1,4}are the duplicated terms for the autoterm, K_{zr1,1}, which are centered at τ = 0 axis. The same label applies to the rest of the autoterms and duplicated terms. The following examples illustrate the problem caused by the duplicated terms to the estimation of IIBphase. There is no interference observed in the IIBphase estimate if there are only autoterms present. For instance, at time t = T_{ b }/2 the cross bilinear product evaluated is given as
Only the autoterms with the IIBphase of φ_{1} exist. When both the autoterms and duplicated terms are present, there will be more than one phase term. This is observed at t = 3T_{ b }/2 where the cross bilinear product is represented as
The interaction of the autoterms and duplicated terms can be visualized as the addition of multiple vector components which result in a new vector component with different magnitude and phase. Instead of IIBphase of φ_{2} which is caused by the autoterms, the resulting IIBphase consists of the interaction between all the phase terms φ_{1} and φ_{3} caused by the duplicated terms.
Since all autoterms lie along the τ = 0 axis, a fixed width lag window was used in [23] to preserve the autoterms and partially remove the duplicated terms that cause distortion in the IIBphase represented on the XTFR. However, success is limited because the duplicated terms are not completely removed resulting in a distorted IIBphase estimate. To resolve this problem, the fixed lag window w (τ) in Equation (11) is replaced with a timedependent window function w (t, τ) and the resulting new TFD, known as the AWXWVD, is given as
The adjustment of this timedependent window width is based on the computation of the LLAC function at every time instant to separate the auto terms and duplicated terms. This is equivalent to use a separable kernel to reduce all cross terms as shown in [21, 22]. An analysis window centered at τ = 0 is used as a reference to perform the similarity test using the LLAC function. This similarity test detects the variation of the signal in the lag direction at every time instant and estimates the window width. The timedependent window function can be implemented using one of the common windows used in digital filter design and spectrum estimation. In this application, a rectangular window is used and it can be defined as
where τ_{ g } (t) is the timedependent window width defined within 0 ≤ t ≤ T, and T is the signal duration. Since the cross bilinear product is asymmetric, the window width in the positive lag and negative lag must be estimated accordingly. The desired τ_{ g } (t) in the positive lag (or in the negative lag direction) is selected if
where ς is the time instant in lag and R_{ KK }(t, ς) is the amplitude of the LLAC which will be discussed in the following section. Note that the rectangular window was used for simplicity as we observed that the proposed methodology performance is not affected significantly by the choice of the window shape.
3.1.2. Adaptation algorithm
The LLAC [20] of the kernel, K, is a function of time and lag and it can be defined as
where w_{ a } (τ) is the analysis window, τ is the lag instant, and ς is the lag running variable. The possible range for the normalized LLAC amplitude is
A higher value of the amplitude of the LLAC function implies that the similarity is high and vice versa. The miscorrelation in the signal is indicated by a drastic drop in the amplitude of the LLAC function. The LLAC function will give a value approaching unity at lag instant, ς = 0.
The analysis window is a parameter of the LLAC. Its selection is important to ensure that the LLAC can detect the variation along the lag axis based on the condition specified in Equation (23) as to estimate the timedependent window width. The analysis window is defined as
where τ_{ a } is the analysis window width. In this article, the analysis window width is chosen experimentally as τ_{ a } = 10 s based on the sampling frequency of 1 Hz. The LLAC is applied to the signal x(t) and evaluated for the normalized frequencies of 1/32, 1/16, 1/8, and1/4 Hz. In this evaluation, the signal is defined as follows (with similar characteristic to the cross bilinear product in lag)
Table 2 shows the minimum values of the LLAC evaluated in time. An analysis window of τ_{ a } = 10 s is sufficient to determine the timedependent window width for frequencies ranged from 1/32 to 1/4 Hz. Thus, the analysis width is valid for the test signals as specified in Section 2. In the case of PSK signals, two consecutives symbols may be different from each other depending on the transmitted data. By applying the LLAC on the cross bilinear product, the resulting adaptive window resembles the shape of a parallelogram as shown in Figure 3.
3.2. IIBphase estimation from the peak of TFDs
By extending the approach used for IF estimation from the peak of WVD presented in [26], the IIBphase is estimated frrm the peak of the AWXWVD and Stransform as outlined in the following sections.
3.2.1. IIBphase estimation using the AWXWVD
The IF can be estimated from the peak of the TFD for all time instants as shown below [[18], p. 429]
where \widehat{f}\left(t\right) is the estimated frequency. The peak of the TFD is detected and the location is used as the frequency estimate. In this application, the peak of the XTFD for the AWXWVD is detected for all time instants and it is used to estimate the phase. Since the peak value is complex, the IIBphase may be expressed as the inverse tangent of the imaginary and real component, that is
The detailed derivation of the above equation is given in Appendix 3.
3.2.2. IIBphase estimation using the Stransform
For the Stransform, the IIBphase estimation from the peak of the TFD, however, is not as straightforward as the AWXWVD. The phase term in the frequency representation introduced by the time shift window has to be compensated in the actual IIBphase estimate. The relationship between the time delay and phase shift is presented in [40], where the authors utilized this property to generate the analytical signal as an alternative to the Hilbert transform. By applying this concept, the estimated IIBphase using Stransform can be represented as
The detailed derivation for IIBphase estimation using Stransform is given in Appendix 4.
3.3. Comparison to CRLB
This section compares the performance of both AWXWVD and Stransform as a phase estimator with the CRLB which is often used as a benchmark [41], as it gives the theoretical lower limit to the variance of any unbiased parameter estimator [42]. The CRLB derived in [43, 44] uses a likelihood function on a known signal in the presence of additive white noise for the digitally phase modulated signal.
In terms of SNR, the CRLB for BPSK and QPSK signals can be represented, respectively, as [43]
where N is the average window width, γ is the SNR, F_{B} and F_{Q} are, respectively, the ratio of the CRLB for random BPSK and QPSK signals to the CRLB for an unmodulated carrier of the same power. At high SNR, the value of F_{B} and F_{Q} is equivalent to one; so, the same bound applies for both the BPSK and QPSK signals [43]. The value of F_{B} and F_{Q} differs at low SNR and is obtained from the results presented in [43]. In [44], the authors extended the study presented in [43] and derived the CRLB for 8PSK signal with random phase. It is shown that the CRLB for MPSK signal for moderate to low SNR is given as [44]
The variance of the actual IIBphase estimator for both AWXWVD and Stransform method can be represented as
where N is the total number of samples, {\widehat{\varphi}}_{n} is the estimated phase at every time sample n, and \stackrel{\u0304}{\varphi} is the actual IIBphase.
3.4. PSK signal detection algorithm
In addition to the estimation of modulation parameters, the IIBphase estimate derived from the XTFR can also be used as a demodulator for the class of PSK signals. The detection is performed by first estimating the IIBphase, φ(t) from the peak of the TFD. The average IIBphase within a symbol duration can be estimated as follows
For a BPSK signal, the symbols are detected based on a set of decision rule [45] that are defined as
where s_{BPSK} is the estimated binary data. The decision boundary is defined based on the signal parameters shown in Table 1. Similarly, the same approach described for BPSK is extended to QPSK and 8PSK. The decision rule for QPSK and 8PSK signals are defined, respectively, as
4. Implementation, results, and discussions
This section discusses the implementation and realization of the TFDs as well as the performance comparison between the AWXWVD and Stransform from several measures. First, comparison is made in terms of the TFR plot, the slice of the TFR, the IIBphase, the instantaneous energy, and the constellation diagram. Then, comparison in terms of the MLW is discussed. Next, the performance of the AWXWVD and Stransform as a phase estimator is benchmarked to the CRLB. This is followed by the evaluation of the symbol error rate performance of the AWXWVD, Stransform, and conventional detector. Finally, a comparison is made in terms of the computational complexity between the AWXWVD, Stransform, and conventional detector.
4.1. Discretetime formulation and implementation
The discrete time formulation of the TFDs is needed for implementation on digital systems; and this applies for both the discrete forms of the AWXWVD and Stransform. In [[18], p. 235], the windowed discrete WVD (DWVD) of a continuoustime signal z(t) is expressed as
where M is a positive integers representing the window length in samples, n is the discrete time samples, m is the discrete lag samples, and k is the discrete frequency. Thus, by using Equation (21), the discrete AWXWVD can be expressed as
Using the same notation as above, the discrete Stransform [24] can be represented as
The discrete time representation of the Stransform is similar to the spectrogram. However, there is a tradeoff between the time and frequency resolution for the Stransform as the window width is frequency dependent.
4.2. Results
Figures 4 and 5 show the TFR, TFR slice, IIBphase, instantaneous energy, and constellation plots for QPSK2 signals at SNR of 10 dB using the AWXWVD and Stransform, respectively. The two TFRs show at which frequency the signal exists, but for the Stransform there are distortions in the TFR at every symbol transition. The high contrast region in the TFR of the Stransform indicates that there are lowdensity components other than the signal component. However, this is not present in the TFR of the AWXWVD. The TFR slice is normalized to the peak value of the TFR and observed in frequency for time n = 100 samples. From the TFR slice, it is shown that the AWXWVD gives better frequency concentration compared to the STransform. This is because the MLW of the TFR slice for the Stransform appears to be much wider than AWXWVD. As shown in Equation (12), the Stransform's window width is frequency dependent where the window is wider for lowfrequency signal and narrower for highfrequency signal. This implies that the Stransform at higher frequency gives worse frequency resolution and wider MLW. Results confirming this statement are presented in Table 3. Besides the MLW of the TFR slice, there is also a difference in the average side lobe level. The side lobe level is higher for the Stransform at about 0.18, while this level is lower at 0.05 for the AWXWVD. This explains the appearance of the high contrast region on the TFR of the Stransform.
The IIBphase plot shows that the AWXWVD gives better accuracy for the IIBphase estimate. For the Stransform, distortion is observed in the IIBphase estimate at the phase transition regions which is absent in the AWXWVD. The sliding window in the Stransform causes distortion in the IIBphase at the symbol transition region due to the interaction between adjacent symbols. Since digitally phase modulated signals have constant amplitude, their instantaneous energy should also be constant at all times. However, due to noise, the amplitude of the signal appears to vary. This is reflected as variation in the magnitude of the instantaneous energy for AWXWVD and Stransform. A significant drop is observed in the instantaneous energy for the Stransform at every symbol transition. Similar to the phase, this drop is caused by the interactions between the adjacent symbols within the sliding window. Since the AWXWVD produces accurate instantaneous energy and IIBphase estimates, the constellation diagram generated shows almost no variation from the original points and is better compared to the Stransform. Table 3 shows the MLW estimated at SNR of 6and 10 dB using both methods. In general, the SNR has no significant effect in the MLW obtained for both methods. However, the effect of signal frequency is more significant for the Stransform compared to the AWXWVD. For instance, the MLW for both BPSK1 and BPSK2 with the AWXWVD based estimate is the same at 0.012 Hz. However, for the Stransform the MLW is larger for BPSK2 than BPSK1 with a difference of 0.028 Hz. The scaling of the Gaussian window results in a broader MLW for higherfrequency signal. The signal modulation level has no significant effect on the MLW. This is shown by the MLW measured for BPSK, QPSK, and 8PSK signals where there are only minor differences. These results imply that the AWXWVD gives better IIBphase estimation results compared to the Stransform as the performance of an estimator is associated with the MLW [[46], p. 50].
4.3. Variance comparison with the CRLB
In order to evaluate the performance of the AWXWVD method, we compare it with the Stransform. Both methods are benchmarked with the CRLB for phase estimate. It is assumed that there is perfect synchronization between the received and reference signals. We consider the signal is corrupted by a zero mean additive white Gaussian noise channel with variance σ^{2}. In simulations, the received signal is generated by adding the noiseless signal, z(t), and the additive white Gaussian noise, v(t), as given in Equation (6) where the SNR is varied at 1 dB step from 0 to 10 dB. Monte Carlo simulations based on 1,000 realizations of the predefined signals are conducted for each value of SNR. The estimated IIBphase is obtained from the peak of the XTFD as described in Section 3.2. Assuming the actual IIBphase of the signal is known, the MSE is computed using Equation (34). Figures 6, 7, and 8 show the results of the estimated IIBphase variance for BPSK, QPSK, and 8PSK signals, respectively. In general, for both BPSK and QPSK signals, the variance of IIBphase estimate using the AWXWVD lies close to the CRLB. The AWXWVD estimate meets the CRLB at SNR ≥ 5 dB for both BPSK1 and BPSK2 signals. Figure 7 shows that the cutoff point for both QPSK1 and QPSK2 signals are the same as the BPSK signals, at SNR of 5 dB. However, the variances in the IIBphase estimate for QPSK signals are higher compared to the BPSK signals at the same SNR. A higher cutoff point is recorded for the 8PSK estimates where both 8PSK1 and 8PSK2 signals meet the CRLB at SNR ≥ 7 dB. In general, the AWXWVD outperformed the Stransform as a phase estimator and the Stransform estimates never meet the CRLB for all signals even at SNR of 10 dB. As expected, the performance of the Stransform deteriorates greatly for higher frequency signals due to broader MLW. This is in contrast with the AWXWVD where it maintains reasonably insignificant changes in the variance of IIBphase estimates for all range of frequency. Thus, the AWXWVD is more robust to noise for phase estimation compared to the Stransform. The result obtained in this article is in line with [30] where the XWVD is shown to be more robust to noise compared to the WVD.
4.4. Symbol error rate performance
In this section, the symbol error rate performance of the AWXWVD and Stransform is compared with the conventional detector defined in [[13], p. 188] for the class of PSK signals. It is based on the matched filter structure, where the reference signal must have the same parameters as and be in phase with the signal of interest. In general, an increment in the number of bits per symbol will increase the throughput at the expense of downgrading the symbol error rate. The formulation for the symbol error rate for coherently detected multiple phase modulation signals is given as [[13], p. 229]
where P_{E}(M) is the symbol error rate, the function Q(x) is the complementary error function, E_{s} is the energy per symbol, N_{0} is the noise power, and M is the size of symbol set. Detection of the PSK signals is done based on the IIBphase estimate and the sets of rules defined in Section 3.4. All the defined test signals of 10,000 symbols are evaluated under AWGN channel. The results presented in Figures 9, 10, and 11 show the symbol error rate performance as a function of SNR for BPSK, QPSK, and 8PSK signals. BPSK signal required a SNR of about 5 dB to achieve a symbol error rate of 10^{3} for the AWXWVD method. Using the same method, a higher SNR is observed for QPSK signal, approximately 8 dB. To achieve the same performance, the conventional detector needs an SNR of 7 and 8 dB for BPSK and QPSK signals, respectively. For 8PSK signal, it is shown that to achieve a symbol error rate of 10^{3}, SNR of 10 dB is required for AWXWVD. As for the conventional detector, it gives the same performance at SNR of 11 dB. From the symbol error rate plot, for all the test signals, it is observed that the advantage of the AWXWVD over the conventional detector is at the low SNR range where it gives lower error rate. In general, the symbol error rate for the AWXWVD is much lower compared to the Stransform. The Stransform could not provide symbol error rate ≤ 10^{3} even at SNR of 12 dB for all signals. Thus, it is impractical to use it as a detector for the class of PSK signals.
4.5. Computation complexity
The number of computations for implementing the AWXWVD, the Stransform, and conventional detectors is discussed in this section to compare the practicality of the proposed method for signal analysis and classification applications as used in spectrum monitoring and cognitive radio [14–17]. The computation complexity of the AWXWVD is similar to the smooth WWVD due to the similarity in the computation of the bilinear product. To implement the AWXWVD, the number of computation required in terms of the number of multiplication is given below [22]. For the sake of clarity in terminology, in the paragraph below, N is the signal length, N_{ τ } is the lag window length, N_{ A } is the length of the analysis window, and N_{ w } is the average length of adaptive lag window.

1.
Computation of the cross bilinear product to obtain its timelag representation requires N_{ τ }N multiplications. Ideally, the number of computation for the cross bilinear product is N^{2} where the lag and time durations are equal to N samples. To maintain equal frequency resolution for N > 512 samples, the duration in lag is maintained at N_{ τ } = 512 samples. By limiting the duration in lag, excessive computation of the cross bilinear product is avoided.

2.
The LLAC uses an analysis window of N_{ A } which slides along the lag axis at every lag sample for a total of N_{ τ } samples. Since there are N time instances, the total number of multiplications for the computation of localized lag autocorrelation function is N_{ A }N_{ τ }N.

3.
The LLAC will determine the separation interval between the autoterms and duplicated terms based on the average lag window width N_{ w }. For N time samples, the total number of multiplications to setup the adaptive lag window based on the average lag window width N_{ w } is N_{ w }N.

4.
To get the XTFR, the Fourier transform of the windowed cross bilinear product is calculated in the lag direction with (N_{ τ } log_{2} N_{ τ }) multiplications. For signal length N, the total number of multiplications 0.5N(N_{ τ } log_{2} N_{ τ }).
Therefore, the total of multiplication required to compute the AWXWVD is N(N_{ τ } + N_{ A }N_{ τ } + N_{ w } + 0.5N_{ τ } log_{2} N_{ τ }).
The Stransform is very much similar to the spectrogram, except that the window for the Stransform is frequency dependent. Hence, the number of computation required in terms of number of multiplication results from [47]:

1.
The product of the frequencydependent Gaussian window function and the signal of interest to obtain its localized spectrum which requires N multiplications.

2.
The Fourier transform of the timelag representation to obtain the TFR requires 0.5N (N_{ τ } log_{2} N_{ τ }) multiplications.
Thus, the total number of multiplication required to implement the Stransform is N(1 + 0.5N_{ τ } log_{2} N_{ τ }).
The number of computation in terms of the number of multiplication required to implement the conventional detector is [13]:

1.
Mixing of the incoming signal with two sinusoid signals with 90° phase difference requires 2N multiplications.

2.
Low pass filtering of the signal to obtain the inphase and quadrature phase component of the signal require 2N multiplications.
Therefore, the total number of multiplications required to implement the conventional detector is 4N.
In this application, the length of the signal evaluated is 640 samples points and the lag window length N_{ τ } is set to 512 sample points. The analysis window length N_{ A } used in the AWXWVD is 10 samples points and the average length of adaptive lag window N_{ w } is 80 samples points. The number of multiplications required for the AWXWVD, Stransform, and conventional detector per symbol is summarized in Table 4.
In terms of the number of multiplications, the AWXWVD requires approximately 4 times more computations compared to the Stransform and 2,000 times more for the conventional detector. Although there is a significant additional number of a computation for the AWXWVD, recent advances in digital electronics as well as decimation procedures can take care of them; in addition, the performance in terms of the IIBphase estimates enables more efficient signal parameters estimation in the proposed area of applications. These parameters can be used to classify a signal from a set of reference parameters. If necessary, we can use the IIBphase estimate to detect PSK signals at low SNR conditions where the conventional detector failed. For higher SNR conditions, it is not necessary to use a technique which is computationally intensive when the symbol error rate is low. Thus, we can setup the conventional detector using the parameters estimated from the IIBphase to detect PSK signal. So, we conclude that, with the enhancement of current computer processing combined with appropriate decimation procedures, the realtime implementation of AWXWVD is feasible with the use of multiple processors or parallel processing [48] and the design proposed in [49].
5. Conclusions
A performance comparison between the AWXWVD and Stransform estimators of IIBphase shows that the AWXWVD is superior to the Stransform for classifying PSK signals. Results show that the mean square error of the phase estimate using AWXWVD is on the average lower by 20 dB. The Stransform has a frequencydependent window width which performs poorly as a phase estimator for highfrequency signal components. Since peak detection is used for the estimation of the IIBphase for both methods, the frequency resolution and MLW contribute to the estimation accuracy. The AWXWVD maintains the frequency resolution through the window adaptation and yields better accuracy for the IIBphase estimate. It also meets the CRLB at moderate SNR for all the defined signals unlike the Stransform that never meets the bound even at high SNR. For symbol error rate performance, the AWXWVD is also better compared to the Stransform and it is comparable to the conventional detector at the cost of higher number of computations. Thus, this article has proven that the AWXWVD is an effective phase estimator for digitally phase modulated signals and can be used for similar applications involving timevarying signals. This study suggests new research directions to pursue in the future, such as replacing the Stransform by a modified Stransform that incorporates an adaptive mechanism; replacing the Stransform by the cross Stransform; using separable kernels in defining a XTFD; and investigate the effect of window shape in Equation (40) on the performance of the phase estimator. These advances can be used in a wide range of signal processing applications from Telecommunications to Biomedicine including EEG and Fetal Movement signals analysis and processing, where timefrequency peak detectors can provide additional features for classification improvement.
Endnote
^{a}The terminology "duplicated terms" is used in this article instead of the cross terms which is typically used in TFA. This is because, in the proposed method, these terms carry the same information as the autoterms but are shifted in both time and lag. The duplicated terms are caused by the cross bilinear product between the k th and l th symbol of the signal of interest and the reference signal, where k ≠ l.
Appendix 1. Derivation of the autoterms
Derivation of the autoterms will be discussed in this section. Cross bilinear product can be seen as the results of the cross correlation function between the signal of interest and a reference signal. For discussion purposes, a PSK signal of N symbols length with normalized amplitude is evaluated. The signal has the same frequency and the difference between each symbol is the phase that is determined by the binary information present. The N symbol length PSK signal can be represented as
The reference signal with respect to the signal of interest is given as
Substituting Equations (A.1) and (A.2) into Equation (8) then the cross bilinear product is given as
Here, the autoterms are defined as the cross bilinear product between the signal of interest and the reference signal at the same time instant the box function overlaps a copy of itself. For instance, the ICF of the first symbol with the reference signal at the same time instant, where k = l = = 1, can be represented as
For the second symbol, the ICF of the signal and reference signal occurring at the same time, where k = l = 2, is given as
The cross bilinear product assembled a rhombic shape and the IAF of the box function given in Equation (18) has a maximum value when they overlap by a copy of itself. This condition applies when the shift in lag is zero. To simplify the notation of the IAF of the box function, the beginning point of each IAF of the box function is determined. For example,
(a) {K}_{\mathrm{\Pi},1,1}\left(t,\tau \right)=\mathrm{\Pi}\left(t+\frac{\tau}{2}\right)\mathrm{\Pi}\left(t\frac{\tau}{2}\right)
Then substituting τ = 0 into the box function,
From Equations (A.6) and (A.7), the IAF of the box function can be represented as K_{∏} (t, τ) where this bilinear product begin at t = 0 and τ = 0.
(b) {K}_{\mathrm{\Pi},2,2}\left(t,\tau \right)=\mathrm{\Pi}\left(t{T}_{b}+\frac{\tau}{2}\right)\mathrm{\Pi}\left(t{T}_{b}\frac{\tau}{2}\right)
Substitute τ= 0 into the box function,
\mathrm{\Pi}\left(t{T}_{b}+\frac{0}{2}\right)\mathrm{\Pi}\left(t{T}_{b}\frac{0}{2}\right)=\mathrm{\Pi}\left(t{T}_{b}\right)\mathrm{\Pi}\left(t{T}_{b}\right) (A.9)
Then, the IAF for box function for the second autoterm can be represented as K_{∏} (tT_{ b }, τ) where it is shifted by t = T_{ b } in the time domain and shifted by τ = 0 in lag domain.
From Equations (A.6) to (A.9), a general representation for the autoterms is given as
The above equation shows that all the autoterms are located along the time axis at τ = 0 and carry the IIBphase for each symbol. Each individual autoterm has a rhombic shape and begins at t = kT_{ b }.
Appendix 2. Derivation of the duplicated terms
In this section, the derivation of the duplicated terms is discussed using the same test signal defined in Appendix 1. The duplicated terms are the cross bilinear product between the k th and l th symbol of the signal of interest and the reference signal, where k ≠ l. The cross bilinear product between the first and second symbols are given as
It is observed that this term has the same frequency and IIBphase content as the K_{zr,1,1}(t, τ) autoterm but is shifted in both time and lag.
This term is the duplicated term of the second autoterm which resulted from the cross correlation function of the second symbol of the received signal and the first symbol of the reference signal. From Equations (B.1) and (B.2), the general formulation if the duplicated terms can be represented as
The above indicates that the signal power, frequencies, and IIBphase for the duplicated terms are the same as the autoterms except that they are shifted in both time and lag. It has a rhombic shape and it begins at t=\frac{\left(k+l2\right){T}_{b}}{2} and τ = (l  k) T_{ b }
Appendix 3. Phase estimation from the peak of XWVD
This section represents the derivations for phase estimation from the TFR generated by the XWVD. Similar to Appendix 1, the derivations consider a 4symbol digital phase modulation signal that is defined in Equation (A.1). The derivation is first presented for the AWXWVD followed by the Stransform. With reference to Appendix 1 and Section 3, it is assumed that the adaptation algorithm has completely preserved the autoterms and part of the duplicated terms that do not cause distortion in the IIBphase estimate. The explanation is simplified by considering the cross bilinear product for at t = 3/2T_{ b } and t = 5/2T_{ b } and the cross bilinear product evaluated over lag over these time instants are
where the box function is defined as
To obtain the XTFR, the Fourier transform is evaluated in lag according to Equation (21) and the resulting XTFR at these time instants are
The results show that cross TFR at both time instants is maximum at the frequency of f_{1} but the peak values are determine by the IIBphase of φ_{2} and φ_{3}. Instead of using the peak of TFR to determine the IF as described in [22, 25, 28], the peak value is used to estimate the IIBphase. Since the peak value is complex, the IIBphase for both time instances can be estimated as follows
By extending this formulation to all time instants, the IIBphase estimate is
The above indicates that the accuracy of the IIBphase estimate depends greatly on the XTFR. Therefore, the duplicated terms must be removed to produce an optimal XTFR, in which we employ an adaptive window as a kernel function to preserve the autoterms and attenuate the duplicated terms.
Appendix 4. Phase estimation from the peak of Stransform
For the Stransform, the similar signal model described in Equation (A.1) is used and the TFR is calculated using Equation (12). Similar to the XTFD, the TFR for the signal using the Stransform is first calculated at time instants of t = 3/2T_{ b } and t = 5/2T_{ b } before the IIBphase estimate formulation is derived. At t = 3/2T_{ b }, the window function g(t) covers within the second symbol of the digital phase modulation signal and the substitution of Equation (4) into Equation (12) results in
The time shift window in the Stransform introduces a phase term in the frequency representation which is described as follows by the time shift properties of the Fourier transform
By including this effect, the TFR for the signal is obtained by evaluating Equation (D.1) is given as
where G(f  f_{1}) is the frequency representation of the window evaluated at the frequency of f_{1}. Due to the property of the Stransform, the bandwidth of the window in frequency representation will depend on frequency of the signal. Extending this for the third symbol at t = 5/2T_{ b } results in the following TFR
Similar to the XTFR, the IIBphase can be estimated from the peak of the TFR. By applying Equation (C.8), IIBphase estimates for t = 3/2T_{ b } and t = 5/2T_{ b } are
From Equations (D.5) and (D.6), it is observed that the time delay in the window function introduces phase shift in the IIBphase estimate. To overcome this problem, the phase shift effect must be compensated and the IIBphase estimate for all time is
The above equation has an additional term, 2πft which is not present in the IIBphase estimate for AWXWVD. This term removes the phase shift caused by the window function in the Stransform.
References
Sampei S, Harada H: System design issues and performance evaluations for adaptive modulation in new wireless access systems. Proc IEEE 2007, 95(12):24562471.
Hussain A: Advanced RF Engineering for Wireless Systems and Networks. John Wiley & Sons, USA,; 2005.
Krouk E, Semenov S: Modulation and Coding Techniques in Wireless Communications. John Wiley & Sons, UK; 2011.
Proesch R: Technical Handbook for Radio monitoring HF. Books on Demand GmbH, Germany; 2009.
Riter S: An optimum phase reference detector for fully modulated phaseshift keyed signals. IEEE Trans Aerosp Electron Syst 1969, 5(4):627631.
Viterbi AJ, Viterbi AM: Nonlinear estimation of PSK modulated carrier phase with application to burst digital transmission. IEEE Trans Inf Theory 1983, 29(4):543551. 10.1109/TIT.1983.1056713
David IB, Shtrikman I: Open loop frequency and phase estimation of a PSK modulated carrier. The 16th Conference of Electrical and Electronics Engineers. Israel 1989, 14.
Kim YH, Lee HS: New total carrier phase estimator in fully phase modulated signals. Electron Lett 1993, 29(22):19211922. 10.1049/el:19931279
Huq AT, Panayirci E, Georghiades CN: ML NDA carrier phase recovery for OFDM systems. In IEEE International Conference on Communications. Volume 2. Vancouver, Canada; 1999:786790.
Panayirci E, Georghiades N: Carrier phase synchronization of OFDM systems over frequency selective channels via EM algorithm. In IEEE Conference on Vehicular Technology Conference. Volume 1. Texas, USA; 1999:675679.
Panagirci E, Georghiades CH: Joint ML timing and phase estimation in OFDM systems using the EM algorithm. In IEEE International Conference on Acoustics, Speech, and Signal Processing. Volume 5. Istanbul, Turkey; 2000:29492952.
Linn Y: Robust MPSK phase detectors for carrier synchronization PLLs in coherent receivers: theory and simulations. IEEE Trans Commun 2009, 57(6):17941805.
Sklar B: Digital Communications, Fundamentals and Applications. 2nd edition. Prentice Hall, USA; 2006.
Rohde , Schwarz : Spectrum Monitoring the ITU Way. News from Rohde & Schwarz. 1997., 1(153):
Matic V, Lestar B, Tadic V: The use of digital signal processing for a modulation classification. In 11th Mediterranean Electrotechnical Conference. Cairo, Egypt; 2002:126130.
Martine C, Adrian F, Robert WJ: Radio spectrum management: overview and trends. In ITU Workshop on Market Mechanisms for Spectrum Management. Geneva, Switzerland; 2007:122.
Yucek T, Arslan H: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 2009, 11(1):116130.
Boashash B: Time Frequency Signal Analysis and Processing: A Comprehensive Reference. Elsevier, UK; 2003.
Sha'ameri AZ, Boashash B, Ismail I: Design of signal dependent kernel functions for digital modulated signals. In Fourth International Symposium on Signal Processing and Its Applications. Volume 2. Gold Coast, Australia; 1996:527528.
Sha'ameri AZ, Boashash B: The lag windowed WignerVille distribution: an analysis method for HF data communication signals. Jurnal Teknologi, Universiti Teknologi Malaysia 1999, 30: 3354.
Tan JL, Sha'ameri AZ: Adaptive optimal kernel smoothwindowed WignerVille Bispectrum for digital communication signals. Signal Process 2011, 91(4):931937. 10.1016/j.sigpro.2010.09.012
Tan JL, Sha'ameri AZ: Adaptive optimal kernel smoothwindowed WignerVille distribution for digital communication signal. EURASIP J Adv Signal Process 2008, 2008: 408341. 17 10.1155/2008/408341
Chee YM, Sha'ameri AZ: Use of the cross timefrequency distribution for the analysis of the class of PSK signals. In IEEE International Conference on Computer and Communication Engineering. Volume 1. Kuala Lumpur, Malaysia; 2010:15.
Stockwell RG, Mansinha L, Lowe RP: Localization of the complex spectrum: the Stransform. IEEE Trans Signal Process 1996, 44(4):9981001. 10.1109/78.492555
Boashash B: Estimating & interpreting the instantaneous frequency of a signalpart I: fundamentals. Proc IEEE 1992, 80(4):519538.
Boashash B: Estimating & interpreting the instantaneous frequency of a signalpart II: fundamentals. Proc IEEE 1992, 80(4):540568. 10.1109/5.135378
Boashash B: Note on the use of the Wigner distribution for time frequency signal analysis. IEEE Trans Acoust Speech Signal Process 1988, 36(9):15181521. 10.1109/29.90380
Reilly A, Frazer G, Boashash B: Analytic signal generationtips and traps. IEEE Trans Signal Process 1994, 42(11):32413245. 10.1109/78.330385
Hlawatsch F, BoudreauxBartels GF: Linear and quadratic timefrequency signal representations. IEEE Signal Process Mag 1992, 9(2):2167.
Boashash B, O'Shea P: Use of the cross WignerVille distribution for estimation of instantaneous frequency. IEEE Trans Signal Process 1993, 41(3):14391445. 10.1109/78.205752
O'Shea P, Boashash B: Instantaneous frequency estimation using the cross WignerVille distribution with application to nonstationary transient detection. In Proceeding of the International Conference on Acoustics, Speech, and Signal Processing. Volume 5. Albuquerque, USA; 1990:28872890.
Jan J: Digital Signal Filtering, Analysis and Restoration. Institution of Electrical Engineers, UK; 2000.
Best RE: PhaseLocked Loops: Design, Simulation and Applications. 5th edition. McGrawHill, USA; 2003.
Pinnegar CR, Mansinha L: The Stransform with windows of arbitrary and varying shape. Geophysics 2003, 68(1):381385. 10.1190/1.1543223
Nguyen T, Liao Y: Power quality disturbance classification utilizing Stransform and binary feature matrix method. Electr Power Syst Res 2009, 79(4):569575. 10.1016/j.epsr.2008.08.007
Assous S, Humeau A, Tartas M, Abraham P, L'Huillier JP: Stransform applied to laser Doppler flowmetry reactive hyperemia signals. IEEE Trans Biomed Eng 2006, 53(6):10321037. 10.1109/TBME.2005.863843
Papoulis A, Pillai SU: Probability, Random Variables and Stochastic Process. 4th edition. McGrawHill, USA; 2002.
White LB, Boashash B: Cross spectral analysis of nonstationary processes. IEEE Trans Inf Theory 1990, 36(4):830835. 10.1109/18.53742
Jones G, Boashash B: Generalized instantaneous parameters and window matching in the timefrequency plane. IEEE Trans Signal Process 1997, 45(5):12641275. 10.1109/78.575699
Hussain ZM, Boashash B: Hilbert transformer and time delay: statistical comparison in the presence of Gaussian noise. IEEE Trans Signal Process 2002, 50(3):501508. 10.1109/78.984723
Drake J: CramerRao bounds for MPSK packets with random phase. In Proceedings of the Fifth International Symposium on Signal Processing and Its Applications. Volume 2. Queensland, Australia; 1999:725728.
Barbier A, Colavolpe G: On the CramerRao bound for carrier frequency estimation in the presence of phase noise. IEEE Trans Wirel Commun 2007, 6(2):575582.
Cowley WG: Phase and frequency estimation for PSK packets: bounds and algorithms. IEEE Trans Commun 1996, 44(1):2628. 10.1109/26.476092
Tavares GN, Tavares LA, Piedade MS: Improved CramerRao lower bounds for phase and frequency estimation with MPSK signals. IEEE Trans Commun 2001, 49(12):20832087. 10.1109/26.974254
Srinath MD, Rajasekaran PK, Viswanathan R: Introduction to Statistical Signal Processing with Applications. Prentice Hall, NJ; 1996.
Kay SM: Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall, Englewood Cliffs; 1993:5053.
Abdullah AR: Timefrequency analysis for power quality monitoring, PhD Dissertation, Universiti Teknologi Malaysia. 2011.
Bhujade MR: Parellel Computing. 2nd edition. New Age Science, UK; 2009.
Boashash B, Black P: An efficient realtime implementation of the WignerVille distribution. IEEE Trans Acoust Speech Signal Process 1987, 35(11):16111618. 10.1109/TASSP.1987.1165070
Acknowledgements
The authors would like to thank Ministry of Higher Education (MOHE) Malaysia, for its financial support and Universiti Teknologi Malaysia, for providing the resources for this research. In addition, one of the authors would like to thank the Qatar National Research Fund under its National Priorities Research Program award numbers NPRP 094652174 and NPRP 096262243 for funding his work. In addition, the authors would like to thank the reviewers for their helpful comments and suggestions.
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Mei, C.Y., Sha'ameri, A.Z. & Boashash, B. Efficient phase estimation for the classification of digitally phase modulated signals using the crossWVD: a performance evaluation and comparison with the Stransform. EURASIP J. Adv. Signal Process. 2012, 65 (2012). https://doi.org/10.1186/16876180201265
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DOI: https://doi.org/10.1186/16876180201265
Keywords
 adaptive windowed cross WignerVille distribution
 optimum phase estimator
 instantaneous information bearing phase
 Phase Shift Keying; Stransform
 CramerRao lower bound
 timefrequency analysis