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In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Dealing with range ambiguity is a hot issue in SAR signal processing. One limitation of current algorithms is their poor robustness to noise and the energy of ambiguous targets, which can lead to performance degradation in complex environments. This letter proposes a new algorithm for range ambiguity suppression in SAR. The algorithm utilizes the orthogonality of the transmitted signal to separate the echo signals of the main region and the ambiguous area, and subsequently applies the alternating direction method of multipliers (ADMM) algorithm to perform joint sparse recovery on both areas. Experimental results show that the proposed method can effectively suppress range ambiguity, and it has a good ability to maintain the weak targets and the details of the main region's image. [ABSTRACT FROM AUTHOR]

In a synthetic aperture radar (SAR) system, prolonging the integration time can improve the detection performance of the ground moving target. However, the complex motions of the target will induce severe range migration and Doppler frequency migration (DFM), which cause target energy defocused and deteriorate the imaging quality. In order to deal with the problem, a novel ground moving target focusing method is proposed. In this paper, the discrete polynomial-phase transform (DPT) is performed to reduce the phase order of the range compressed signal. Then, the residual range walk is removed by the constructed compensation function. Lastly, Lv's distribution (LVD) is used to motion parameter estimation and DFM correction. The proposed method is capable of focusing the targets both near and apart from the scene center and is computationally efficient. Several simulation results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: A new multi-SVD based subspace estimation algorithm is proposed to improve the direction of departure (DOD) and direction of arrival (DOA) estimation accuracy for bistatic multiple-input multiple-output (MIMO) radar. First, the matched filter output is transformed to a 3-order tensor. Then the signal subspace is estimated by multi-SVD of the matrix unfoldings of this tensor or its covariance tensor. Since the multidimensional structure is utilized, the estimated signal subspace of our method has better accuracy than that of the traditional SVD/EVD method. Combined with the classical subspace based methods, such as MUSIC and ESPRIT, the proposed approach improves the angle estimation performance compared with the existing ones. Simulation results confirm the effectiveness of our method. [Copyright &y& Elsevier]