Yanfei Wang

Imaging and Parameter Estimation of Fast-Moving Targets With Single-Antenna SAR

This letter proposes a novel algorithm for focusing moving targets with fast cross-track velocities and estimating their motion parameters with single-antenna synthetic aperture radar. The spectrum of a fast-moving target contains the Doppler ambiguity and the third-order phase error. Hough transform is utilized to estimate the slope of the range walk trajectory, from which the cross-track velocity is obtained and the Doppler ambiguity problem is solved. Then, polynomial Fourier transform is adopted to estimate the second- and third-order Doppler parameters of the moving target. By estimating and compensating for the third-order phase error, the imaging resolution of the fast-moving target is improved and the motion parameters, including the cross-track acceleration, are correctly estimated. Both simulated and real data processing results are provided to demonstrate the effectiveness of the proposed algorithm.

Detection and Imaging of Ground Moving Targets With Real SAR Data

Detection and imaging of multiple moving targets is a challenging task, particularly with real synthetic aperture radar (SAR) data. In this paper, moving targets with different motion parameters are classified based on the relative locations of their spectra to that of the clutter. Based on the classification, a novel moving target processing strategy with real SAR data is proposed, including a two-step ground moving target indication (GMTI) algorithm and a practical ground moving target imaging (GMTIm) algorithm with motion error compensation. The two-step GMTI algorithm has the ability of indicating multiple moving targets, particularly those submerged by the clutter; the practical GMTIm algorithm shows a robust performance in real data moving target imaging since the motion errors are estimated and compensated. Both simulated and real data processing results are provided to demonstrate the effectiveness of the proposed strategy.

A Robust Motion Error Estimation Method Based on Raw Data

High-resolution airborne synthetic aperture radar (SAR) systems are very sensible to deviations of the aircraft from the reference track. In high-resolution imagery, the improvement of range resolution increases the difficulty of implementing range cell migration correction (RCMC), while a wider synthetic aperture increases the cumulative time of motion errors which will affect the image quality. To enable accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the limit of accuracy of such systems, motion errors are hard to be compensated correctly, causing mainly the resolution decrease in final image. Moreover, in large swath mode, the range-dependent phase errors are difficult to be compensated by using the conventional autofocus algorithm only. In this paper, we propose a robust motion error estimation method based on raw SAR data. To apply this estimation method, we first estimate the double phase gradients in subaperture. Second, a filtering method based on curve fitting was proposed to reduce the phase estimation errors caused by low signal-to-clutter ratio (SCR). Finally, we propose a weighted total least square method to calculate the motion errors using the filtered phase gradients. Because the proposed algorithm is nonparametric, it can estimate high-order motion errors. This is very important for the airborne SAR, particularly the light aircraft SAR platform, due to their more complicated movement in air turbulence. The versatility that the proposed method can be used in any imaging algorithms is another advantage. The processing of large number of raw SAR data shows that the algorithm is as robust and practical as phase gradient autofocus and can generate better focused images.

A Two-Dimensional Spectrum Model for General Bistatic SAR

This paper derives a 2-D spectrum model for general bistatic synthetic aperture radar (SAR). By introducing some new parameters such as equivalent monostatic parameters, bistatic factor, and weighted-equivalent range, the 2-D spectrum of general bistatic SAR can be expressed in the form of monostatic SAR even when the transmitter and receiver move along unparallel trajectories with different velocities. The result formulates bistatic SAR into an equivalent monostatic SAR model and would be useful for developing efficient bistatic SAR algorithms in frequency-domain or hybrid-domain processing. Simulation results are given to validate the performance of the model. For special bistatic SAR configurations, the model can be simplified. Compared to other similar models, the proposed model is clearer and much more concise.

An improved imaging algorithm for fixed-receiver bistatic SAR

Based on the signal model of fixed-receiver bistatic SAR, this paper analyzes the characteristics of azimuth-variant, which includes variation of range migration curve and variation of Doppler rate caused by different azimuth position of targets. Then, an extended nonlinear chirp scaling (NLCS) algorithm is proposed, with its flowchart and phase compensation functions being provided. Simulation results show that the algorithm is available for the characteristics of azimuth-variant of fixed-receiver bistatic SAR, and good focused radar images could be acquired.

An Estimation Algorithm for Phase Errors in Synthetic Aperture Radar Imagery

This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.

Nonlinearity Correction of FMCW SAR Based on Homomorphic Deconvolution

In this letter, a novel algorithm based on homomorphic deconvolution is proposed to give a theoretically unbiased estimation of transmitted nonlinearity in the beat signal in frequency-modulated continuous-wave synthetic aperture radar. Transmitted and received nonlinearities in the beat signal are separated in the Cepstrum domain by using a controllable delay line, and the transmitted nonlinearity can be extracted by comb notch filter. Moreover, an integrated nonlinearity correction strategy with residual-video-phase removal is presented by combining with the proposed estimation algorithm. Extensive simulations are performed to validate the generality and the robustness of the proposed algorithm.

An Efficient Design for General Mixed Radix FFT Processors

A general mixed-radix FFT design for in-place strategy is derived and a low-complexity scheme for efficiently implementing mixedradix FFTs is proposed. In this method, we develop an accumulator that can simply and practically generate addresses for the operands, as well as the twiddle factors. This approach extends the range of FFT size and reduces the hardware complexity of any non-power-of-two memory-based FFTs. Finally, the 3780-point FFT is taken an example to illustrate the validation of the proposed method.

A range-dependent autofocus algorithm based on the Tikhonov regularization method

Phase Gradient Autofocus (PGA) is an algorithm developed to estimate phase errors in synthetic aperture radar (SAR) images. It is widely used in SAR image processing and has been shown to be very robust on a variety of imagery and phase error functions. Original PGA makes a narrow beam assumption, which is valid for most SAR systems. But it is known that the narrow beam assumption is not valid for large swath or low-altitude mode SAR, where range-dependent phase errors need to be compensated. Some PGA-based range-dependent autofocus algorithms were proposed to deal with this problem, but during raw data processing, we found that there are ill-posed problems in the linear motion error estimation model that will affect the accuracy of the estimation. In this article, combining the Tikhonov regularization (TR) method with phase gradient estimation, a novel autofocus algorithm is proposed to deal with the ill-posed problem in estimation. Real data processing demonstrates that the new method avoids the effect of parameter errors, estimates the range-dependent phase errors more precisely and finally leads to better focusing quality.

Indication and imaging of along-track moving targets in frequency modulated continuous wave synthetic aperture radar systems

The spectrum of a moving target exhibits new characteristics in frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR) systems. The new spectrum characteristics can be a favourable factor for ground moving target indication (GMTI) rather than being corrected as a phase error. In this letter, a novel algorithm is presented to indicate and focus along-track moving targets in FMCW SAR systems. Firstly, the signal model of an along-track moving target in FMCW SAR systems is derived. Secondly, the Hough transform is adopted to detect the along-track moving target by searching for its additional range walk and estimating its motion parameters. Finally, the target can be refocused and relocated in the stationary image. Simulation experiments offer evidences that the proposed algorithm is effective in the along-track moving target indication and imaging.