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The sparse microwave imaging combines the sparse signal processing theory with radar imaging to obtain new theory, new system, and new methodology of microwave imaging. In this paper, a brief review of fundamental issues in applying sparse signal processing to radar imaging is provided, including sparse representation, measurement matrix construction, unambiguity reconstruction, and so on. The developments of sparse signal processing in microwave imaging are discussed, and the initial airborne experiments on the prototype Synthetic Aperture Radar (SAR) framework with sparse constraints are introduced. The results demonstrate the feasibility and effectiveness of the principle and methodology of sparse microwave imaging. Besides, we also provide an overview of sparse signal processing in various radar applications, including Tomographic SAR (TomoSAR), Inverse SAR (ISAR), Ground Penetrating Radar (GPR) as well.

Sparse microwave imaging requires nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method is studied, in which the measured data and the relative imaging region is divided into sub-blocks, and then sparse microwave imaging algorithm based on Least absolute shrinkage and selection operator (Lasso) is performed on each sub-block, finally the sub-blocks are combined to obtain the whole image of the large scene. Compared to the overall reconstruction of the sparse scene, sub-block algorithm can control data amount involved in each reconstruction, so as to avoid the signal processor frequently accessing the disk, which will cost huge time. Indeed, the theoretical analysis illustrates that the sub-block sparse imaging method is also accurate and stable, and the associated reconstruction error is no more than two times of that of the overall reconstruction. The result proved by simulation and real data processing supports the validity of our method.

The echo data can be modeled as the product of the Toeplitz matrix and reflectivity of the observed scene. The row of the Toeplitz matrix is the time-shift of the transmitted signal. Since, it is difficult to vertify whether the Toeplitz matrix satisfis the reconstruct condition of sparse microwave imaging (such as, restricted isometry property), analyzing the performance of the transmitted signal in sparse microwave imaging is a problem. RIPless, a new progress in sparse signal processing, shows if the row of the matrix is an i.i.d. random vector drawn from a distribution, and this distribution satisfies certain conditions, then one can faithfully recover approximately sparse signals from a minimal number of measurements. Toeplitz matrix satisfies RIPless. In this paper, we first introduce the construction of measurement matrix in sparse microwave imaging. Then the relationship of pulse duration, bandwidth and waveform type and the number of measurements in sparse microwave imaging is analyzed. At last simulation results show that the effectiveness of proposed method.

In the paper, a method of moving target detection and parameters estimation is proposed used for dual channels Wide Area Surveillance (WAS) radar based on high resolution Doppler Beam Sharpen (DBS) image. First, it analyzes characteristics of clutter and moving target return, and presents the ways of clutter suppression and moving target parameters estimation. Then, an approach to decrease errors of parameters estimation is proposed. At last, an effective approach to resolve the ambiguity caused during estimating moving targets parameters is proposed. The effectiveness of the presented method is demonstrated by both simulated data and real data.