Xunchao Cong

Millimeter-wave spotlight circular synthetic aperture radar (scsar) imaging for Foreign Object Debris on airport runway

Foreign Object Debris (FOD) on airport runway is one of the greatest threats to aviation safety. The major characteristics of FOD are diversity and smallness. Synthetic aperture radar (SAR) has a natural advantage in high resolution imaging, and is, therefore, very suitable for small and weak targets imaging on airport runway. Firstly, a new millimeter-wave FOD imaging system called Spotlight Circular Synthetic Aperture Radar (SC-SAR) is introduced in this paper. The SC-SAR system not only can solve the practical limitations for ground-based applications but also fully exploit the advantage of existing ground-based SAR systems for FOD imaging. Secondly, based on the geometry and imaging principle of SC-SAR, the adjusted version of the Range-Doppler imaging formula was presented. Finally, the simulation results validate feasibility of SC-SAR imaging system and algorithm.

A novel adaptive wide-angle SAR imaging algorithm based on Boltzmann machine model

Scattering dependency often exists in both the spatial location and the viewing angle. Based on the assumption of isotropic point scattering model, however, conventional narrow-angle synthetic aperture radar (SAR) imaging algorithms have been no longer suitable to the scattering dependency model. To improve azimuth resolution and capture richer observation information, sparsity-driven (SD) wide-angle SAR (WSAR) imaging algorithms have been developed. Actually, existing SD-based WSAR imaging algorithms are sensitive to the regularization parameters which are required to adjust manually. These methods indeed limit their practical applications. To solve this problem, in this paper, we propose an adaptive WSAR imaging algorithm based on the Boltzmann machine (BM) model. In particular, we model the spatial sparsity and high azimuth correlation of scattering energy by virtual of a special BM prior. Then, the support of sparse representation and imaging parameters including BM parameters, noise variance and the variance of each sparse representation element are jointly estimated by a block-coordinate descent process. Finally, the proposed WSAR imaging algorithm is performed adaptively via sparse representation. Experiments are conducted by synthetic scene and simple tank dataset of high-frequency electromagnetic scattering calculation software. Extensive empirical results demonstrate that the proposed algorithm can achieve better imaging performance than the conventional algorithms in terms of relative mean squared error and support identification error.

Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.

Bl-GESPAR: A fast SAR imaging algorithm for phase noise mitigation

The performance of Synthetic aperture radar (SAR) imagery is often significantly deteriorated by the random phase noises arose from the atmospheric turbulence or frequency jitter of the transmit signal within SAR observations. The computational time of the traditional phase retrieval based SAR autofocus algorithms is sharply increased with the size of scene. In this paper, we recast the SAR imaging problem via the phase-corrupted data as a special case of block-based quadratic compressed sensing (BBQCS) problem. We propose a novel fast SAR imaging algorithm to recover the focused well SAR image from the phase-corrupted data and reduce the computational time and memory requirement for several orders of magnitude. Experimental results show our proposed algorithm not only reduces the computational complex but also provides satisfactory reconstruction performance.

A Tensor Decomposition Based Multiway Structured Sparse SAR Imaging Algorithm with Kronecker Constraint

This paper investigates a structured sparse SAR imaging algorithm for point scattering model based on tensor decomposition. Several SAR imaging schemes have been developed by researchers for improving the imaging quality. For a typical SAR target scenario, the scatterers distribution usually has the feature of structured sparsity. Without considering this feature thoroughly, the existing schemes have still certain drawbacks. The classic matching pursuit algorithms can obtain clearer imaging results, but the cost is resulting in an extreme complexity and a huge computation resource consumption. Therefore, this paper put forward a tensor-based SAR imaging algorithm by means of multiway structured sparsity which makes full use of the above geometrical feature of the scatterers distribution. The spotlight SAR observation signal is formulated as a Tucker model considering the Kronecker constraint, and then a sparse reconstruction algorithm is introduced by utilizing the structured sparsity of the scene. The proposed tensor-based SAR imaging model is able to take advantage of the Kronecker information in each mode, which ensures the robustness for the signal reconstruction. Both the algorithm complexity analysis and numerical simulations show that the proposed method requires less computation than the existing sparsity-driven SAR imaging algorithms. The imaging realizations based on the practical measured data also indicate that the proposed algorithm is superior to the reference methods even in the severe noisy environment, under the condition of multiway structured sparsity.

MRF model-based joint interrupted SAR imaging and coherent change detection via variational Bayesian inference

Abstract In this paper, we study the problem of interrupted synthetic aperture radar (SAR) imaging and coherent change detection (CCD) in the setting of gapped collections with missing pulses. Conventional interrupted SAR CCD technique is performed using a coherence estimator for paired SAR images which are reconstructed by matched-filtering (MF). However, this method suffers from substantial false alarms due to the poor resolution and high sidelobe level problems in the reconstruction. To improve image resolution and lessen the number of false alarms, sparsity-driven (SD) estimation techniques have been utilized to form SAR image pair and then apply coherence estimator to the resulting images. Actually, existing SD-based regularization SAR imaging algorithms are sensitive to the regularized parameters which are required to tune manually, leading to a serious restriction in practical application. To solve this problem, in this work, we develop an adaptive joint imaging and CCD algorithm in interrupted environments based on Bayesian framework. Our formulation utilizes a partially coherent model to incorporate prior information about the scenes and properties of changes. In particular, we model the sparsity and spatial clustering in changes by virtue of a Markov random fields (MRF) prior. To tackle the difficulty of the calculation of posterior, the mean-field variational Bayesian expectation-maximization (VBEM) method is utilized to simultaneously estimate the MRF parameters and the latent variables. Experimental results are provided to verify the effectiveness of the proposed method.

Robust adaptive beamforming for noncircular signal against array steering vector mismatch and interference nonstationarity

The performance of adaptive arrays is severely degraded if the weights are in the presence of interference nonstationarity and signal steering vector mismatch. Because of this, we proposed a new robust null broadening adaptive beamforming algorithm. The method is realized by the combination of projection transform and diagonal loading technique. We got a new sample covariance matrix through diagonal loading technique and the received data transform technique which is based on the concept of subspace projection. We applied the proposed algorithm to noncircular signals which are usually encountered in the context of radio communications. According to the theoretical analysis, the projection transform operation can improve the orthogonality between signal subspaces and noise subspaces. The proposed approach can effectively broaden the jammer nulls and strengthen the null depth. Simulation results demonstrate that the proposed algorithm can provide strong robustness against both signal steering vector mismatch and jammer motion.

Anisotropic image formation based on basic sequential algorithmic scheme and non-quadratic regularization

The emerging synthetic aperture radar (SAR) missions motivate the application of automated processing techniques in obtaining abundant information from the scene for the decision and interpretation due to developments in aeroelectronics. In this paper, we develop a new anisotropic imaging algorithm called BSAS-NQR which is based on basic sequential algorithmic scheme (BSAS) and the non-quadratic regularization (NQR) techniques. Sparse signal representation and canonical scattering center models have been combined to characterize anisotropic scattering. The BSAS technique is used to cluster atoms in order to intelligently reduce the size and mutual coherence of the overcomplete dictionary. Then, the SAR image and the type of canonical scatterers are obtained by NQR simultaneously. Experimental results show the proposed algorithm not only reduces the memory requirements but also retains anisotropic reconstruction performance.