Chenglong Jiang

Experimental results and analysis of sparse microwave imaging from spaceborne radar raw data

Sparse microwave imaging is a novel radar framework aiming to bring revolutions to the microwave imaging according to the theory of sparse signal processing. As compressive sensing (CS) is introduced to synthetic aperture radar (SAR) imaging in recent years, the current SAR sparse imaging methods have shown their advantages over the traditional matched filtering methods. However, the requirement for these methods to process the compressed range data results in the increase of the hardware complexity. So the SAR sparse imaging method that directly uses the raw data is needed. This paper describes the method of SAR sparse imaging with raw data directly, presents the analysis of the signal-to-noise ratio (SNR) in the echo signal by combining the traditional radar equation with the compressive sensing theory, and provides the tests on 2-D simulated SAR data. The simulation results demonstrate the validity of the SNR analysis, and the good performance of the proposed method while a large percentage of the raw data is dropped. An experiment with RadarSat-1 raw data is also carried out to show the feasibility of processing the real SAR data via the method proposed in this paper. Our method is helpful for designing new SAR systems.

Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction

With a short linear array configured in the cross-track direction, downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain the 3-D image of an imaging scene. To improve the cross-track resolution, sparse recovery methods have been investigated in recent years. In the compressive sensing (CS) framework, the reconstruction performance depends on the property of measurement matrix. This paper concerns the technique to optimize the measurement matrix and deal with the mismatch problem of measurement matrix caused by the off-grid scatterers. In the model of cross-track reconstruction, the measurement matrix is mainly affected by the configuration of antenna phase centers (APC), thus, two mutual coherence based criteria are proposed to optimize the configuration of APCs. On the other hand, to compensate the mismatch problem of the measurement matrix, the sparse Bayesian inference based method is introduced into the cross-track reconstruction by jointly estimate the scatterers and the off-grid error. Experiments demonstrate the performance of the proposed APCs’ configuration schemes and the proposed cross-track reconstruction method.

Radar Change Imaging With Undersampled Data Based on Matrix Completion and Bayesian Compressive Sensing

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

Influence factors of sparse microwave imaging radar system performance: approaches to waveform design and platform motion analysis

Sparse microwave imaging radar is a newly developed concept of microwave imaging system, which tries to combine the traditional radar imaging system with sparse signal processing theories, achieving the aim of reducing the complexity of microwave imaging systems and enhancing the system performance. In this paper, we introduce some basic concepts of sparse signal processing theory, and then apply it to the traditional radar imaging system to get the mathematical model of sparse microwave imaging system. We analyze the factors that determine the performance of sparse microwave imaging radar, including scene, waveform and platform. According to the radar model, we analyze how these factors influence the radar system and how to optimize them. Simulation results of the sparse microwave imaging radar system are also provided.

System design and first airborne experiment of sparse microwave imaging radar: initial results

In this paper, we mainly study the system design of sparse microwave imaging radar and report some of the preliminary results of airborne experiments performed with it. Sparse microwave imaging radar is a novel concept which introduces the sparse signal processing theory to microwave imaging replacing the conventional matched filtering processing method. With the exploitation of the sparse microwave imaging, the radar system could achieve better performance. As a newly developed concept, the two main types of applications of sparse microwave imaging are found in adopting the sparse signal processing theory to current radar systems, and in designing an optimized sparse microwave imaging system. Here we are trying the latter that mainly aims at lower PRF and wider swath. We first introduce the theories of sparse microwave imaging radar and its imaging algorithm. Then we discuss the system designing principles, including the sampling scheme, signal bandwidth, SNR and multi-channel mode. Based on the relationships of these parameters, we provide a design example of radar parameters. In the end, we exploit an airborne experiment using our designed radar system with jittered azimuth sampling strategy. Some preliminary analysis from the experiment result is also provided.

SAR range ambiguity suppression via sparse regularization

Range ambiguity in synthetic aperture radar (SAR) imaging primarily arises from scattered energy of bright targets outside the interested region. So to reduce the ambiguity, we need to identify these targets additionally, which yields an ill-posed problem. To find a feasible solution where the range ambiguity can be sufficiently reduced, we propose in this paper a new method using compressed sensing, a theory which tells when sparse signal can be reconstruct from undetermined linear system, by observing that the recognizable targets are approximately sparse in the ambiguous range zones. Therefore, it is possible to reconstruct the main region and identify the ambiguous targets simultaneously. The simulation results demonstrate the validation of the proposed method.