An integrative synchronization and imaging approach for bistatic spaceborne/stratospheric SAR with a fixed receiver
© Zhang et al.; licensee Springer. 2013
Received: 1 July 2013
Accepted: 21 October 2013
Published: 1 November 2013
Bistatic spaceborne/stratospheric synthetic aperture radar (SAR) with a fixed receiver is a novel hybrid bistatic SAR system, in which a spaceborne SAR serves as the transmitter of opportunity, while a fixed receiver is mounted on a stratospheric platform. This paper presents an integrative synchronization and imaging approach for this particular system. Firstly, a novel synchronization method using the direct-path signal, which can be collected by a dedicated antenna, is proposed and applied. The synchronization error can be completely removed using the proposed method. However, as the cost of synchronization, the characteristic of synchronized echo’s range history becomes quite different from that of general bistatic SAR data. To focus this particular synchronized data, its 2-D spectrum is derived under linear approximations and then a frequency-domain imaging algorithm using two-dimensional inverse scaled Fourier transform (2-DISFT) is proposed. At last, the proposed integrative synchronization and imaging algorithm is verified by simulations.
Bistatic synthetic aperture radar (SAR) has been an active research area in the last decade, where in particular, the method based on spaceborne illuminator appears to be more attractive. Several experiments of bistatic SAR with spaceborne illuminator have been conducted by numerous organizations. In these experiments, the receiver is either spaceborne, aircraft, or stationary on the ground. The promising imagery results show the great potentials and capabilities of bistatic SAR as an innovative imaging system [1–6].
This paper discusses a particular sub-class of spaceborne hybrid configuration, where a spaceborne SAR, e.g. TerraSAR-X, serves as the transmitter of opportunity, while a fixed receiver is mounted on a stratospheric platform, e.g. a stratospheric aerostat. Its advantages include low-cost, reduced vulnerability to counter measurement, high operational flexibility and wide observation scene [7, 8]. Such a system is a good tool for high-resolution imaging and high-precision height information extraction, which has the potential for future mission both in civil and military applications.
Time and phase synchronization is essential and foremost for such a kind of bistatic system, since independent local oscillators (LO) are used, and the common time reference is missed for the transmitter and the receiver. The synchronization errors result not only in range cell migration (RCM) error but also in the distortion of the azimuth dependent phase history [9–12]. This implies that synchronization errors will degrade the quality of the bistatic SAR images, and therefore, corresponding compensation must be implemented.
The synchronization scheme has been well studied for cooperative bistatic SAR systems, in which the dedicated synchronization link can be constructed [13–17]. An echo-domain phase synchronization approach by using correlation of bistatic echo is proposed for bistatic SAR in alternating bistatic/ping-pong mode in . For un-cooperative bistatic SAR systems, it is a common method to achieve synchronization by using direct-path signal [19–23].
Assuming that the direct-path channel and the reflected-path channel are previous balanced and the common LO is used for both channels, the reflected signal and the direct-path signal will be contaminated by the same synchronization errors. However, without any other auxiliary data, it is quite difficult to estimate the phase synchronization error with high precision . On the one hand, the accuracy of the satellite's trajectory is not sufficient for separating the phase synchronization error from the nominal phase caused by direct-path range history. On the other hand, the phase synchronization error caused by the LO phase noise is a random component, which is difficult to estimate .
In this paper, we propose a new synchronization method using direct-path signal's time delays and peak phases, without the isolation of synchronization errors, to compensate corresponding components in the reflected signal directly. In this way, the time and synchronization errors can be completely removed, and this method is quite simple and fast.
However, due to the fact that direct-path signal’s time delays and peak phases constitute both the synchronization error component and the nominal range component, the range history of direct-path signal will be removed in the synchronized echo data as well. This means that the system impulse response of the synchronized data will be quite different from that of the general bistatic SAR data. It is most straightforward to use time-domain algorithms such as back-projection algorithm (BPA) to focus the synchronized data [25–27]. However, it requires high-precision position measurement and suffers from severe computational load. Therefore, a frequency-domain algorithm is the preferred choice, but has to be re-engineered accordingly.
The calculation of an analytical bistatic point-target reference spectrum (BPTRS) is the key to develop frequency-domain imaging algorithm, since the bistatic range history loses its hyperbolic form . Based on the approximate BPTRS, including Loffeld bistatic formula (LBF) [29, 30] and series reversion , several image formations have been proposed. The 2-DISFT  algorithm and chirp scaling (CS) algorithm  were developed from LBF for corresponding bistatic configurations. Based on the series reversion method, a non-linear CS (NLCS) process was applied to equalize the azimuth chirp rate in bistatic SAR focusing for general configuration , and a range Doppler (RD) algorithm was proposed to handle the azimuth-invariant bistatic case . In bistatic SAR with a fixed receiver configuration, a two-dimensional NLCS processor was applied in  to deal with the large bistatic angle case. In , a highly accurate bistatic range migration algorithm (RMA) was proposed for asymmetric bistatic SAR system with a fixed receiver. A modified range Doppler algorithm was presented for space-surface bistatic SAR (SS-BSAR) . In the spaceborne/airborne configuration, a frequency-domain processing method based on 2-DISFT was proposed in [39, 40].
According to the property of the synchronized data, a frequency-domain imaging algorithm based on 2-DISFT is proposed in this paper. Firstly, for the sake of triple square-root terms in the range history, the Taylor series is applied to obtain the stationary phase point when deriving the BPTRS. Further, under the properly designed linear approximation of BPTRS, the 2-D spectrum of the synchronized scene data is derived. It is found in the 2-D spectrum that the dominant component is the 2-D scaled Fourier transform of target's bistatic backscattering coefficient. At last, a frequency-domain imaging algorithm based on 2-DISFT is proposed.
This paper is organized as follows. In Section 2, the geometry of the bistatic spaceborne/stratospheric SAR with a fixed receiver is given, and the signal model of the echo data with synchronization errors is derived. In Section 3, the synchronization implementation using direct-path signal is presented. Section 4 derives the BPTRS and 2-D spectrum for synchronized data. The analysis of approximation errors for the derivation of 2-D spectrum is presented in Section 5. Then, a frequency-domain imaging algorithm using 2-DISFT is proposed in Section 6. To validate the proposed algorithm, simulation experiments are carried out in Section 7. Finally, in Section 8, some conclusions are presented.
2. Geometry and signal model
where . From (5), we can see that x is the function of the variable r 0T; hence, r 0R is expressed as r 0T(r 0T).
where is the closest distance from the transmitter to the receiver.
where σ(·) is the bistatic backscattering coefficient of the point target P, t delay = (r T(t,t 0T,r 0T) + r R(t 0T,r 0T))/c is the time delay corresponding to the time it takes the signal to travel the transmitter-target-receiver distance, c is the speed of light, and T S is the synthetic aperture time for the point target P.
In bistatic satellite/stratosphere SAR system, there is no common time reference between the transmitter and the receiver. Moreover, independent oscillators are used in the transmitter and the receiver, thus the phase noise of the oscillator cannot be cancelled out as in monostatic SAR. Therefore, time and phase synchronization errors must be considered in this system.
As can be seen from (13), the existing time and phase synchronization errors result not only in a drift of the echo sampling windows which will cause RCM errors but also in the distortion of the azimuth-dependent phase history. From the knowledge of SAR imaging, we can conclude that the time and phase synchronization errors will degrade the quality of bistatic SAR image .Therefore, the time and phase synchronization compensation must be implemented.
where t D_delay = r D(t)/C.
3. Synchronization using the direct-path signal
As stated before, extracting the synchronization errors from direct-path signal is a straightforward idea for the bistatic SAR synchronization. It is possible to extract time synchronization error from direct-path signal . However, it seems to be difficult to estimate and extract the phase synchronization error with high precision if no other auxiliary data are available. The reason for this is double sided. On one hand, the accuracy of the satellite's trajectory is not sufficient to separate the phase synchronization error from the nominal phase caused by direct-path range history. On the other hand, the phase synchronization error caused by the LO phase noise is a random component, which is difficult to be estimated . Therefore, a different approach has to be applied here.
As can be seen from (15) and (16), the extracted time delays and peak phases constitute both the nominal range component and the synchronization error component. Instead of separating the synchronization error component from the nominal range component, we use the whole extracted time delay and peak phase to compensate the corresponding terms in the reflected signal. This can be done by applying an opposite time-delay shift and by multiplying an opposite phase term on each pulse.
From (22), we can see that after the implementation of synchronization, synchronization errors are completely removed. However, as the cost of synchronization, the range history of the point target P r T + r R is replaced by r T + r R - r D. We note here that the range history is quite different from that of the general bistatic SAR. Thus, some special processing approaches have to be implemented, which will be discussed in the following sections.
4. 2-D spectrum of synchronized data
To understand the features of the synchronized data, this section will describe the processing steps performed to obtain its 2-D spectrum, which is based on the signal model (22) and the principle of stationary phase (POSP).
4.1 Derivation of BPTRS
Carrier frequency (GHz)
Pulse repetition frequency (Hz)
Sampling frequency (MHz)
Azimuth beam width (°)
Time synchronization error
1 × 10-9
Carrier frequency offset (ppm)
1 × 10-11
Incident/reflected angle (°)
Central slant range (km)
The first and the second terms in (35) represent the RCM which is not only azimuth variant but also range-variant. The third term is a linear function of azimuth frequency f a, expressing the azimuth position of the point target in the focused image. The forth term stands for the cross coupling between azimuth and range, and the last one is responsible for the range modulation. We can see from (35) that BPTRS is dependent on both the azimuth coordinate t 0T and the range coordinate r 0T of the point target P.
4.2 Derivation of 2-D spectrum of the scene data
As shown in (35), the phase term ψ (f a,f,t 0T,r 0T) is a complicated function with respect to the position of target, i.e., variables t 0T and r 0T.To obtain the analytical solution of the integral, the linearity approximation of ψ (f a,f,t 0T,r 0T) with regard to variables t 0T and r 0T has to be carried out.
First of all, even though both the first term and the second term of ψ (f a,f,t 0T,r 0T) represent the RCM shift, the second term is a quadratic function of t 0T. Therefore, the second term will be automatically omitted in the linearity approximation expression. The impact of this approximation will be given in the following section.
where λ = c/f 0 is the wavelength. In (45), the first term is a pure Doppler phase term which represents the range-variant azimuth modulation, and the second term is the scaled range-frequency phase term which expresses the residual space-variant RCM component.
However, due to the expressions of ψ tL(f a, f, 0, r 0) and ψ rL(f a, f, 0, r 0), σ(ψ tL, ψ rL) should be viewed as the 2-D scaled Fourier transform of the bistatic backscattering coefficient σ(t 0T, r 0T).
5. Analysis of approximation errors
Examining the derivation of (50), it can be seen that two linear approximations are made to the phase term ψ (f a,f,t 0T,r 0T). To validate these operations, these approximation errors should be analysed in detail.
Using the parameters listed in Table 1, we get r 0 = 726.9 km, r 0d = 645.8 km, f = 25 MHz. The azimuth frequency f a and the slant range deviation r = r 0T - r 0 are used as independent variables in the simulation. From Figure 3, we can see that |Δψ E1| ≪ π/8 is satisfied. This implies that the linear approximation operation of (45) is accurate enough.
In (53), the first term is due to the azimuth displacement error. The second term and the third term represent the residual RCM error and may result in a range displacement error. The last term is the residual coupling phase which may result in a defocus.
We can see that the geometric distortion will be introduced by this linear approximation. However, this geometric distortion can be corrected by interpolating after image focusing . Moreover, the geometric correction is a necessary step for SAR imaging in frequency domain, and thus this process will not increase the computational load.
Using the same parameters as before, we get |f a r| ≤ 4.87 × 105. For example, if the scene width is assumed to be 10 km in the slant-range direction, i.e. max(r) = 5,000 m, the azimuth frequency needs to satisfy |f a| ≤ 97.4 Hz.
Using the parameters listed in Table 1, we get B a = 158.99 Hz. If we assume that W a = 2,000 m; thus, max(|f a|) = 458.02 Hz. However, it is beyond the restriction given above. This means that a compromise between f a and r is needed to meet the requirement.
In this way, the 2-D spectrum of the block N will satisfy the constriction denoted by (59). However, blocks have to overlap, and then the efficiency of the algorithm decreases with increasing the number of blocks.
6. Imaging process using 2-DISFT
6.1 Analysis of 2-D spectrum
where is the Doppler bandwidth for the traditional bistatic SAR with a fixed receiver configuration. In this case, if we apply inverse Fourier transform along azimuth direction directly, the azimuth resolution of inferred image will be . Compared with the azimuth resolution of the traditional bistatic SAR with a fixed receiver configuration, which is , the inferred azimuth resolution is scaled by |r 0d/(r 0d - r 0)|. When the value of |r 0d/(r 0d - r 0)| is many times larger than 1, the inferred image would suffer a very poor azimuth resolution. Using the same parameters as before, we get ρ a0 = 5.31 m, |r 0d/(r 0d - r 0)| = 7.96, and then ρ a0 = 4.82 m. Similarly, the range frequency suffers the same problem, but not as serious as the azimuth one. To circumvent this limitation, we propose to apply ISFT both on range and on azimuth direction, which will be presented in the next section.
6.2 Principle of ISFT
The inverse scaled Fourier transform, abbreviated as ISFT, was introduced to focus monostatic SAR data in  and was compared with chirp scaling method in . The ISFT was also applied for bistatic SAR data imaging [32, 40].
6.3 Imaging process
According to the constriction (59), the whole synchronized scene data is divided into smaller blocks.
- 2.DCC was performed by multiplying the DCC function in 2-D time-domain for different azimuth blocks.(66)
The 2-D time-domain data was transformed into 2-D frequency domain using Fourier transform.
- 4.Bulk RCM correction (RCMC) and 2-D compression by multiplying the reference function (RF). As is pointed out, H 0(f a, f) represents the space-invariant range modulation, RCM and azimuth modulation. Therefore, RF should be given as the conjugate of H 0(f a,f). However, to keep the phase which can be used in the interferometric application, RF can be expressed as(67)After this implementation, the remaining signal can be given by(68)
- 5.ISFT with regard to the range frequency, which can be expressed as(69)where(70)
In the implementation of (69), it is worth noting that f r is the frequency variable with respect to the variable r, while f is the frequency variable with respect to the fast-time variable τ. The relation between f r and f is .
- 6.Residual azimuth compression by multiplying the range-variant phase functions in range-time/azimuth-frequency domain.(71)
- 7.ISFT with regard to the azimuth frequency f a to remove azimuth scaling.(72)
According to (54) and (57), applying geometric correction by interpolation
Stitching the divided blocks into a whole SAR image.
To validate the proposed algorithm, the simulations are carried out. The simulation parameters are listed in Table 1. The transmitter's parameters are referred to TerraSAR-X, and the receiver's parameters are referred to a stratospheric aerostat.
From the knowledge of synchronization, the linear component is the dominant component in the time synchronization error . For the phase synchronization error, both the fixed carrier frequency offset and the phase noise are considered [17, 22]. Therefore, the slope of the time synchronization error is given, and the corresponding parameters of phase synchronization error are also listed in Table 1, where 1 ppm means that the fixed carrier frequency offset is ∆f = f c · 10-6, and the Alan variance σ(τ = 1 s) = 1 × 10-11 can be regarded as a representative example for the ultra stable oscillators (USO) of current spaceborne SAR system .
7.1 Echo characteristics before and after synchronization
7.2 Imaging simulation for small scene
Quality parameters of the imaging results
As shown in Figure 10, the profiles of targets 1, 5 and 9 are very close to those of the ideal results. This implies that these three targets are focused well with the proposed algorithm. It can also be found from Figure 10 (a),(c) that the first side lobes are slightly asymmetric for the two edge points. The reason for this is the approximation error shown in (58).
The resolutions of bistatic SAR are 2-D dependent . Therefore, to demonstrate the performance of the proposed algorithm, the resolution before geometric correction is presented in Table 2. According to parameters listed in Table 1, the theoretical value of azimuth resolution and the slant-range resolution is 5.31 m. The theoretical value of peak side lobe ratio (PSLR) is -13.26 dB, and the theoretical value of integrated sidelobe ratio (ISLR) is -9.72 dB .
In Table 2, we can find that both the azimuth resolution and the range resolution have a deviation less than 0.08 m with respect to the theoretical value. The maximum deviation of the measured azimuth PSLR is less than 0.49 dB of the theoretical value. The maximum deviation of the measured range PSLR is less than 0.14 dB of the theoretical value. For ISLR, the maximum deviation is less than 0.48 and 0.65 dB along the azimuth and the range directions, respectively.
7.3 Imaging simulation for large scene
According to (60), to avoid the Doppler ambiguity, the Doppler frequency should meet max(|f a|) < PRF/2. In other words, the azimuth width of the imaged scene should satisfy . Based on the parameters listed in Table 1, we get W a < 4.86 km. If we want to extend the azimuth width of the target scene, higher PRF should be applied.
It can be seen from Figure 11 that the Doppler frequency is shifted to baseband using DCC. Therefore, the same imaging process can be applied for this block.
It can be seen from Figure 12 that three targets are well focused and located at correct position using the proposed algorithm. This demonstrates the capability of the proposed algorithm for large scene imaging.
Bistatic spaceborne/stratospheric SAR has the potential to play an important role in the future missions. The crucial steps for such a system are synchronization and imaging. There have been quite a number of published studies in these two fields. However, the combined research of synchronization and imaging has not been developed. This paper proposes an integrative synchronization and imaging approach for a particular configuration-bistatic spaceborne/stratospheric SAR with a fixed receiver.
In published methods, the direct-path signal is a common choice for the synchronization of bistatic SAR systems, since the direct-path signal takes the advantage of high SNR and clean phases. However, it is still very difficult to extract synchronization errors with high precision from the direct-path signal, if no other auxiliary data is variable. In this paper, as a novel idea, time delays and peak phases of direct-path signal are utilized to compensate corresponding components of reflected signal directly. Time and phase synchronization errors can be completely removed using the presented method. Meanwhile, as the cost of synchronization, the system impulse response of the synchronized echo becomes quite different from that of the general bistatic SAR. To focus the particular synchronized data, the 2-D spectrum of synchronized data is derived under linear approximations, and then a frequency-domain imaging algorithm is proposed. The theoretical analysis and simulation results show that the proposed approach can provide accurate time and phase synchronization and well-focused images.
Based on the presented method, we continue our research on bistatic spaceborne/stratospheric SAR. The next steps of research are multiple. One is the study of synchronization and imaging for general bistatic spaceborne/stratospheric SAR configuration. The other is the study of the interferometric application of such a bistatic system.
The authors express their appreciation to Dr. Zeng Zhanfan. Thanks for his proofreading and helpful discussions. We also would like to appreciate the reviewers for their very patient work and kind suggestions, which helped a lot in correcting many mistakes, increasing the readability, and improving the quality of this paper.
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