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Remotelysensed TOA interpretation of synthetic UWB based on neural networks
EURASIP Journal on Advances in Signal Processing volume 2012, Article number: 185 (2012)
Abstract
Because of the good penetration into many common materials and inherent fine resolution, UltraWideband (UWB) signals are widely used in remote sensing applications. Typically, accurate Time of Arrival (TOA) estimation of the UWB signals is very important. In order to improve the precision of the TOA estimation, a new threshold selection algorithm using Artificial Neural Networks (ANN) is proposed which is based on a joint metric of the skewness and maximum slope after Energy Detection (ED). The best threshold based on the signaltonoise ratio (SNR) is investigated and the effects of the integration period and channel model are examined. Simulation results are presented which show that for the IEEE802.15.4a channel models CM1 and CM2, the proposed ANN algorithm provides better precision and robustness in both high and low SNR environments than other EDbased algorithms.
Introduction
As a new wireless communications technology, UltraWideband (UWB) has generated considerable research interest due to the many potential applications. One of the most promising areas is remote sensing [1, 2]. For example, Defense Research and Development Canada (DRDC) Ottawa has conducted numerous experiments on indoor throughwall imaging, snow penetration, standoff remote sensing of human subjects, and mine detection using highresolution UWB signals [1]. In [2], UWB propagation channel characterization was performed to test the feasibility of using UWB technology in underground mining to monitor and communicate with remote sensors.
UWB technology offers many advantages for remote sensing [1]. First, some frequency components may be able to penetrate obstacles to provide a LineOfSight (LOS) signal. Second, the transmission of very short pulses makes high time resolution (subnanosecond to nanosecond) possible. Third, the wide signal bandwidth means a very low power spectral density, which reduces interference to other Radio Frequency systems.
Among the potential applications, precision ranging or Time of Arrival (TOA) estimation is the most important for remote sensing. However, this is a very challenging problem due to the severe environments encountered, e.g., thermal noise, multipath fading, reflection interference, and intersymbol interference. The TOA estimation problem has extensively been studied [3–6]. There are two approaches applicable to UWB technology, a Matched Filter (MF) [3] (such as a Rake or correlation receiver) with a high sampling rate and highprecision correlation, or an Energy Detector (ED) [4–6] with a lower sampling rate and low complexity. An MF is the optimal technique for TOA estimation, where a correlator template is matched exactly to the received signal. However, an UWB receiver operating at the Nyquist sampling rate makes it very difficult to align with the multipath components of the received signal [7]. In addition, an MF requires a priori estimation of the channel, including the timing, amplitude, and phase of each multipath component of the impulse [7]. Because of the high sampling rates and channel estimation, an MF may not be practical in many applications. As opposed to a more complex MF, an ED is a noncoherent approach to TOA estimation. It consists of a squarelaw device, followed by an integrator, sampler, and a decision mechanism. The TOA estimate is made by comparing the integrator output with a threshold and choosing the first sample to exceed the threshold. It is a practical solution as it directly yields an estimate of the start of the received signal. An ED is thus a low complexity, low sampling rate receiver that can be employed without the need for a priori channel estimation.
The major challenge with ED is the selection of an appropriate threshold based on the received signal samples. Threshold selection for different signaltonoise ratios (SNRs) has been investigated via simulation. In [4], a normalized threshold selection technique for TOA estimation of UWB signals was proposed which uses exponential and linear curve fitting of the kurtosis of the received samples. In [5], an approach based on the minimum and maximum sample energy was introduced. These approaches have limited TOA precision, as the strongest path is not necessarily the first arriving path.
Neural networks (NNs) have extensively been used in signal processing applications. The weights between the input and output layers can be adjusted to minimize the error between the input and output. Because of the complexity of wireless environments, it is difficult to derive a closedform expression to estimate the TOA. On the other hand, an artificial neural network (ANN) can provide a very flexible mapping based on the training input. The ANN here intends to solve a regression problem being J the input and optimal threshold the output.
In this article, we consider the relationship between the SNR and the statistics of the integrator output including skewness, maximum slope, kurtosis and standard deviation. A metric based on skewness and maximum slope is then used as the ANN input. A back propagation (BP) NN is used which is a feed forward NN. It approximates the relationship between the joint metric and the optimal threshold by using a nonlinear continuum rational function. Performance results are presented which show that in the IEEE 802.15.4a channel models CM1 and CM2, this ANN provides robust estimates with high precision for both high and low SNRs.
The remainder of this article is organized as follows. In the following section, the system model is presented. Section “TOA estimation based on ED” discusses TOA estimation algorithms based on ED. Section “Statistical characteristics of the signal energy” considers the statistical characteristics of the energy values, and a joint metric based on skewness and maximum slope is proposed. In Section “Optimal normalized threshold with respect to J”, the relationship between the joint metric and optimal normalized threshold is established. Section “Threshold selection using an ANN based on skewness and maximum slope” introduces a novel TOA estimation algorithm based on an ANN. Some performance results are presented in Section “Performance results and discussion”, and Section “Conclusions” concludes the article.
System model
IEEE 802.15.4a [8] is the first international standard that specifies a wireless physical layer to enable precise TOA estimation and wireless ranging. It includes channel models for indoor residential, indoor office, industrial, outdoor, and open outdoor environments, usually with a distinction between LOS and nonLOS (NLOS) properties. In this article, a Pulse Position Modulation Time Hopping UWB (PPMTHUWB) signal [9] is employed for transmission between the transmitter and receiver.
UWB signal
PPMTHUWB signals are very short in time, typically a few nanoseconds, and can be expressed as
where i and T_{ f } are the frame index and frame duration, respectively. The time hopping TH is provided by a pseudorandom integervalued sequence c_{ i }, which differs for each user to allow for multiple access communications. T_{ c } is the chip time, and the PPM time shift is ϵ, with the data a_{ i } either 0 or 1. If a_{ i } =1, the signal is shifted in time, otherwise there is no PPM shift. The pulse is given by p(t). For example, the second derivative Gaussian pulse is given by
where α is the shape factor and f(t) is the Gaussian pulse. A smaller value of α results in a shorter pulse duration and thus a larger bandwidth.
Multipath fading channel
Because of the multipath channel between the transmitter and receiver, the received signal can be expressed as
where N is the number of received multipath components, α_{ n } and τ_{ n } denote the amplitude and delay of the n th path, respectively, and n(t) is additive white Gaussian noise with zero mean and twosided power spectral density N_{0}/2. Equation (3) can be rewritten as
where s(t) is the transmitted signal, and h(t) is the channel impulse response given by
where X is a lognormal random variable representing the amplitude gain of the channel, N_{ c } is the number of observed clusters, K(n) is the number of multipath components received within the n th cluster, α_{ nk } is the coefficient of the k th component of the n th cluster, T_{ n } is the TOA of the n th cluster and τ_{ nk } is the delay of the k th component within the n th cluster.
Energy detector
As shown in Figure 1, after the Low Noise Amplifier, the received signal is squared, and then input to an integrator with integration period T_{ b }. Because of the interframe leakage due to multipath signals, the integration duration is set to 3T_{f}/2 [4], so the number of signal values for ED is N_{b} = (3T_{f})/(2T_{b}). The integrator output can then be expressed as
where n = 1, 2, …, N_{b} is the sample index with respect to the start of the integration period and N_{ s } is the number of pulses per symbol. Here, N_{ s } is set to 1, so the integrator output is
If z n is the integration of noise only, it has a centralized Chisquare distribution, while it has a noncentralized Chisquare distribution if a signal is present. The mean and variance of the noise and signal values are given by [4]
respectively, where E_{ n } is the signal energy within the n th integration period and F is the number of degrees of freedom given by F = 2BT_{ b }+ 1. B is the signal bandwidth.
TOA estimation based on ED
TOA estimation algorithms
There are many TOA estimation algorithms based on ED which can be used to determine the start of a received signal, as shown in Figure 2. The simplest one is Maximum Energy Selection (MES), which chooses the maximum energy value to be the start of the signal. The TOA is estimated as the center of the corresponding integration period
However, as shown in Figure 2, the maximum energy value is not always the first [3], especially in NLOS environments. Often the first energy value z$\widehat{n}$ is located before the maximum zn_{max}, i.e., $\widehat{n}$≤ n_{max}. Thus, Threshold Crossing (TC) TOA estimation has been proposed where the received energy values are compared to an appropriate threshold ξ. In this case, the TOA estimate is given by
It is difficult to determine an appropriate threshold ξ directly, so a normalized threshold ξ_{norm} is usually employed with
The TOA estimate is then obtained using Equation (11). The problem in this case becomes one of how to set the threshold, i.e., how to establish the relationship between the received energy values and ξ_{norm}. There are two main methods in the literature, curve fitting and fixed threshold (FT). In [4], a normalized threshold selection technique for TOA estimation of UWB signals was proposed which uses exponential and linear curve fitting of the kurtosis of the received samples. A simpler approach is the FT algorithm where the threshold is set to a fixed value, for example ξ_{norm} = 0.4. If ξ_{norm} is set to 1, the algorithm is the same as MES. In this article, an ANN algorithm is employed to obtain the normalized threshold based on the signal energy statistics.
TOA estimation error
In [5], the mean absolute error (MAE) of TCbased TOA estimation was analyzed, and closed form error expressions derived. The MAE can be used to evaluate the quality of an algorithm, and is defined as
where t_{ n } is the n th actual propagation time,${\widehat{t}}_{n}$ is the n th TOA estimate, and N is the number of TOA estimates.
Statistical characteristics of the signal energy
In this section, the skewness, maximum slope, kurtosis and standard deviation of the energy values are analyzed.
Kurtosis
The kurtosis is calculated using the second and fourthorder moments and is given by
where $\overline{x}$ is the mean, and σ is the standard deviation. The kurtosis for a standard normal distribution is three. For this reason, k is often redefined as K = k  3 (referred to as excess kurtosis), so that the standard normal distribution has a kurtosis of zero. Positive kurtosis indicates a “peaked” distribution, while negative kurtosis indicates a “flat” distribution. For noise only (or for a low SNR) and sufficiently large F (degrees of freedom of the Chisquare distribution), z[n] has a Gaussian distribution and K = 0. On the other hand, as the SNR increases, K tends to increase.
In [4], the normalized threshold with respect to the kurtosis and the corresponding MAE were investigated. To model this relationship, a double exponential function was used for T_{b} = 4 ns, and a linear function for T_{b}=1 ns with K as the xcoordinate and ξ_{best} as the ycoordinate. The resulting expressions are
and
The model coefficients were obtained using data from both the CM1 and CM2 channels.
Skewness
The skewness is given by
where $\overline{x}$ is the mean, and σ is the standard deviation of the energy values. The skewness for a normal distribution is zero, in fact any symmetric data will have a skewness of zero. Negative values of skewness indicate that the data are skewed left, while positive values indicate data that are skewed right. Skewed left indicates that the left tail is long relative to the right tail, while skewed right indicates the opposite. For noise only (or very low SNRs), and sufficiently large F, S ≈ 0. As the SNR increases, S tends to increase.
In [6], exponential functions were fit to the skewness results for T_{ b } = 1 ns and T_{ b } = 4 ns, with S as the xcoordinate and ξ_{best} as the ycoordinate. The resulting functions are
Maximum slope
Kurtosis and skewness cannot account for delay or propagation time, so the slope of the energy values is considered as an alternative measure. These values are divided into (N_{b}  M_{b} + 1) groups, with M_{ b } values in each group. The slope for each group is calculated using a least squares line fit. The maximum slope (M) can then be expressed as
For example, Figure 3 shows the fitted lines for eight energy values and M_{b} = 4, so there are 84 + 1 = 5 lines with 5 corresponding slopes.
Standard deviation
The standard deviation is a widely used measure of variability. It shows how much variation or “dispersion” there is from the average (mean or expected value). The standard deviation is given by
Joint metric
In order to examine the characteristics of the four statistical parameters (skewness, maximum slope, kurtosis, and standard deviation), the CM1 (residential LOS) and CM2 (residential NLOS) channel models from the IEEE802.15.4a standard are employed. For each SNR value, 1,000 channel realizations were generated and sampled at F_{ c } = 8 GHz. A second derivative Gaussian pulse is employed with T_{ f } = 200 ns, T_{ c } = 1 ns, T_{ b } = 4 ns, and N_{ s } = 1. Each realization has a TOA uniformly distributed within (0, T_{ f }).
The four statistical parameters were calculated, and the results obtained are given in Figures 4 and 5. These figures show that the characteristics of the four parameters with respect to the SNR are similar for the two channels. Further, Figures 4 and 5 show that the kurtosis and skewness increase as the SNR increases, but the skewness changes more rapidly. Conversely, the maximum slope and standard deviation decrease as the SNR increases, but the maximum slope changes more rapidly. Since the skewness and maximum slope change more rapidly than the kurtosis and standard deviation, they better reflect changes in SNR. Therefore, they are more suitable for TOA estimation. Moreover, when the SNR is less than 15dB, skewness changes slowly while the maximum slope changes rapidly. On the other hand, when the SNR is higher than 15dB, the skewness changes rapidly but the maximum slope changes slowly. Therefore, no single parameter is a good measure of SNR change over a wide range of values. Thus, the following joint metric based on skewness and maximum slope is proposed.
where S is the skewness and M is the maximum slope.
Table 1 shows the standard deviation of the statistics. In all cases, the standard deviations of Maximum Slope and Standard Deviation are much less than 0 and the standard deviations of Skewness and Kurtness increase with the increase of SNRs but the former is much lower than the latter. Therefore, the less variability of Skewness and Maximum Slope implies more confidence about the statistic.
In order to verify that the proposed metric J is sensitive to both high and low SNRs, 1,000 channel realizations were generated for many SNR values in each IEEE802.15.4a channel. In the simulations, because of the random signal, the J values are not unique for one SNR, but in order to draw Figure 6, the average J value with respect to SNR were calculated for each channel model and integration period. Because there were 29 SNR values simulated, there are 29 JSNR pairs for each channel model and integration period. Figure 6 shows that J is a monotonic function for a large range of SNR values, and that J is more sensitive to the changes in SNR than any single parameter. The four curves differ somewhat due to the channel model and integration period used. The figure shows that the metric is more sensitive to T_{b} than the channel model.
Optimal normalized threshold with respect to J
Before training the ANN, the relationship between J and the optimal normalized threshold ξ_{opt} must be established. According to Figure 6, the curves for channel models CM1 and CM2 for a given value of T_{b} are similar, so models are derived only for T_{b}=1 ns and T_{b}=4 ns. There are four steps to establish the relationship between J and ξ_{opt}.

(1)
Generate a large number of channel realizations for each channel model, integration period, and SNR value in the range [4, 32] dB.

(2)
Calculate the average MAE value with respect to normalized threshold ξ _{norm} for each J value, channel model, and integration period as shown in Section “Average MAE with respect to the normalized threshold”. In the simulation, because of the random signal, there are many MAE values with respect to one normalized threshold, so the average MAE should be calculated. At the same time, because J is a real value, J should be rounded to the nearest discrete value, for example integer value or halfinteger value.

(3)
Select the normalized threshold with the lowest MAE as the best threshold ξ _{best} with respect to J for each channel model and integration period, as shown in Section “Optimal thresholds”.

(4)
Calculate the average normalized thresholds of channels CM1 and CM2 for each J as the optimal normalized threshold ξ _{opt}, as shown in Section “Optimal thresholds”.
Average MAE with respect to the normalized threshold
To determine the optimal threshold ξ_{opt} based on J, the relationship between the average MAE and the normalized threshold ξ_{norm} for different J, channel model and T_{b} was determined. ξ is the threshold which is compared to the energy values to find the first TC, as defined (12). When ξ is larger than the maximum energy value z_{max}, no value is found for τ, so in this case ξ is set to z_{max}, and ξ_{norm} is set to 1.
In the simulation, all J values were rounded to the nearest integer and halfinteger values for all SNR values, that is, the range [−9, 16] and [−4, 8] for T_{ b } =1 ns and T_{ b } =4 ns. Figures 7, 8, 9 and 10 only show the MAE for integer J = 1 to 8 for the CM1 and CM2 channels, and T_{ b } = 1 ns and T_{ b } = 4 ns. The relationship is always that the MAE decreases as J increases. In addition, the minimum MAE is lower as J increases.
Optimal thresholds
The normalized threshold ξ_{norm} with respect to the minimum MAE is called the best threshold ξ_{best} for a given J. Therefore, the lowest points of the curves in Figures 7, 8, 9, and 10 for each J are selected as the ξ_{best}. These best thresholds are given in Figures 11 and 12.
These results show that the relationship between the two parameters is not affected significantly by the channel model, but is more dependent on the integration period, so the values for channels CM1 and CM2 can be combined. Therefore, the average of the two values is used as the optimal normalized threshold
Threshold selection using an ANN based on skewness and maximum slope
Structure of the ANN
A BP NN is used which consists of an input layer, a hidden layer and an output layer, as shown in Figure 13. The weights between the layers are adjusted according to the output layer error.
The number of neurons in the hidden layer is difficult to choose [10], but it can be estimated based on repeated training results. In [11], several ANNs are initialized and trained and the best one is selected. Moreover, in [11], an algorithm (implemented in Matlab) for initializing the ANN weights and biases is used, which warrants the stability and convergence at the beginning of the training. Here, the number of neurons in the hidden layer is varied from 2 to 40, and for each value, the ANN was trained 200 times and the mean squared error (MSE) calculated. The percentage of the MSE values which were less than 1e–10 is given in Figure 14. This shows that as the number of neurons in the hidden layer increases, the percentage also increases, so the effectiveness of the model improves. However, the computational complexity also increases. For T_{ b } = 1 ns, when the number of neurons in the hidden layer is more than 20, the percentage is greater than 90% and changes only slightly with increasing values, so 20 is selected as the number of neurons in the proposed ANN. For T_{ b } = 4 ns, when this number of neurons is more than 10, the percentage is greater than 95% and changes very little with increasing values, so 10 is selected in this case.
The value of ξ_{norm} ranges from 0 to 1, so the logsig function is selected as the transfer function for the neurons of both the hidden and output layers. This function is defind as logsig(x) = 1/(1 + exp(−x)). The LevenbergMarquardt (LM) algorithm is used in the network training to update the weight and bias values according to LM optimization [12]. Although this algorithm requires more memory than other algorithms, it is often the fastest BP algorithm. Because there is only one input and one output element in the proposed ANN, and only 39 ξ_{norm}J pairs (J = −9 to 16 for T_{ b } =1 ns and J = −4 to 8 for T_{ b } =4 ns), the memory requirements are modest. The weight and bias values before training were set to random values uniformly distributed between −1 and 1.
ANN training
In order to train the ANN, i.e., to determine the relationship between J and the normalized threshold ξ_{norm}, 1,000 CM1 and CM2 channel realizations for each value of SNR from 4 to 32 dB were generated for both T_{b} = 1 ns and T_{b} = 4 ns. The integer J values in the range [−9, 16] and [−4, 8] for T_{b} =1 ns and T_{b} =4 ns, respectively, were used to train the ANN. Thus, there were 39 samples to train the ANN. On the other hand, the halfinteger J values in the range [−0.85, 15.5] and [−3.5, 7.5] for T_{b} =1 ns and T_{b} =4 ns, respectively, were used to conduct the external validation for the trained ANN. To obtain the best ANN, 100 separate training iterations were conducted for each value of T_{b}, and the one with the lowest MSE was selected.
Validation of the ANN
In order to evaluate the performance of the trained ANN, the internal validation and the external validation were both conducted as shown in Table 1 and Figure 15. The J values from −9 to 16 for the internal validation with T_{b} =1 ns, from −8.5 to 15.5 for the external validation with T_{b} =1 ns, from −4 to 8 for the internal validation with T_{b} =4 ns and from −3.5 to 7.5 for the external validation with T_{ b } =4 ns were input to the ANN to get the estimated normalized thresholds. As shown in Table 2, the two coefficients of determination of the internal validation for T_{b} =1 ns and T_{b} =4 ns are both nearly equal to 1 and the two coefficients of determination of the external validation for T_{b} =1 ns and T_{b} =4 ns are both more than 0.97, so the trained ANN output fits well with the optimal normalized thresholds for T_{b} =1 ns and T_{b} =4 ns. However, the ANN is able to provide values for any J, and not just discrete values. The ANN also eliminates the complicated and timeconsuming optimization process used in Section “Optimal normalized threshold with respect to J”. The IEEE802.15.4a channel models reflect the statistical properties in specific environments, and the choice of ANN parameters depends on the characteristics of the channel. Our ANN can easily be employed with any channel, and the parameters adjusted to fit any environment. This is particularly useful when the channel is not static.
Performance results and discussion
In this section, the MAE is examined for different ED based TOA estimation algorithms in the IEEE 802.15.4a channel model CM1 and CM2. As before, 1,000 channel realizations were generated for each case. A second derivative Gaussian pulse with a 1 ns pulse width was employed, and the received signal sampled at F_{ c } = 8 Ghz. The other system parameters were T_{ f } = 200 ns and N_{ s } =1. Each realization had a TOA uniformly distributed within (0, T_{ f }).
Figure 16 presents MAE of the TOA estimation based on the ANN for SNR values from 4 to 32 dB in the LOS (CM1) and NLOS (CM2) channels with T_{b} = 1 ns and 4 ns. This shows that the ANN algorithm performs well at high SNRs. The performance in CM1 is better than in CM2 by at most 18 ns. When SNR > 22 dB, the MAE for CM1 is less than 3.85 ns while for CM2 it is less than 11 ns. In most cases, the performance with T_{b} = 1 ns is better than that with T_{b} = 4 ns, regardless of the channel, but the difference is less than 4 ns.
Table 3 shows the MAE averaged over all the simulated realizations. Here “ANN” refers to the proposed algorithm, “MES” to the MES algorithm, and the normalized threshold for the FT algorithm is set to 0.4. In all cases, the average MAE of ANN is the lowest among the four algorithms.
Figures 17 and 18 present the MAE for four TOA algorithms in channels CM1 and CM2, respectively. As expected based on the results in Section “Statistical characteristics of the signal energy”, the MAE with the proposed algorithm is lower than with the other algorithms, particularly at low to moderate SNR values. The proposed algorithm is better than the Kurtosis algorithm except when the SNR is greater than 27 dB. For these large SNR values, the Kurtosis algorithm is slightly better. For example, when SNR > 27 dB, the MAE of the proposed ANN algorithm is at most 2 ns greater than that of the Kurtosis algorithm.
The performance of the proposed algorithm is more robust than the other algorithms, as the difference between T_{b} = 1 ns and 4 ns is very small compared to the difference with the Kurtosis algorithm. For almost all SNR values the proposed algorithm is the best. Conversely, the performance of the Kurtosis algorithm varies greatly with respect to the other algorithms, and is very poor for low to moderate SNR values.
Conclusions
A low complexity ANNbased (TOA) estimation algorithm has been developed for UWB remote sensing applications. Four statistical parameters were investigated, and from the results obtained, a joint metric based on skewness and maximum slope was developed for TC TOA estimation. The optimal normalized threshold was determined using performance results for the CM1 and CM2 channels. The effects of the integration period and channel model were investigated. It was determined that the proposed threshold selection technique is largely independent of the channel model. The performance of the proposed algorithm is shown to be better than several wellknown algorithms. In addition, the proposed algorithm is more robust to changes in the SNR and integration period.
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Acknowledgments
This study was supported by the Nature Science Foundation of China under grant No. 60902005, the Outstanding Youth Foundation of Shandong Province under grant No. JQ200821, and the Program for New Century Excellent Talents of the Ministry of Education under grant No. NCET080504.
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^{1}Department of Information Science and Engineering, Ocean University of China, Qing Dao, China. ^{2}Department of Computer and Communication Engineering, China University of Petroleum (East Chinxa), Qing Dao, China.^{3}Department of Electrical Computer Engineering, University of Victoria, Victoria, Canada.
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Zhang, H., Cui, X. & Gulliver, T.A. Remotelysensed TOA interpretation of synthetic UWB based on neural networks. EURASIP J. Adv. Signal Process. 2012, 185 (2012). https://doi.org/10.1186/168761802012185
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Keywords
 Artificial Neural Network (ANN)
 Remote sensing
 UltraWideband (UWB)
 TOA estimation
 Ranging
 Skewness