Compressive Sensing Inverse Synthetic Aperture Radar Imaging Based on Gini Index Regularization

Abstract

Abstract:In compressive sensing (CS) based inverse synthetic aperture radar (ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar (ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation (SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio (PSLR) and the reconstruction relative error (RE) indicate that the proposed method outperforms the l1 norm based method.

On the Use of Gini Coefficient for Measuring Time-Frequency Distribution Concentration and Parameters Selection

“…For instance, let us observe an example of the signal in the form: s(n) � e j(2 sin(2πn)+sin(3πn)+cos(3πn)) , (12) where the signal parameters and window length are assumed as in the case of signal given by (11). Due to the fast-varying spectral behaviour, it is necessary to use higher-order time-frequency distribution in order to provide highly concentrated representation.…”

“…Particularly, the Gini coefficient has been incorporated into a stochastic optimization algorithm to provide more accurate reconstruction from compressive samples. Another example is the use of the Gini index in compressive sensing ISAR imaging [12] providing high-quality image reconstruction under strong clutter and a very limited number of measurements. e improvements (over many state-of-the-art methods) achieved by using the Gini coefficient-based indexes in feature extraction methods have been proven in the application of machinery fault feature extraction [13].…”

Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar

“…The field of compressive sensing has established a mathematical framework which guarantees sparse solutions for underdetermined linear inverse problems that occur across numerous engineering and mathematical science fields. In particular, this framework has found applications in various radar imaging problems, ranging from moving target indication, ISAR imaging, coherence imaging, multichannel imaging, micro-Doppler imaging, to through-the-wall radar imaging (see, e.g., [15][16][17][18][19][20][21][22][23][24][25][26]).…”

New Compressed Sensing ISAR Imaging Algorithm Based on Log–Sum Minimization

“…Also Gini index was used to measure the sparsity of signal in CS ISAR. But theoretical conditions need to be established to guarantee exact reconstruction [5]. In 2012, Zhao et al proposed a novel reconstruction model based on Meridian norm [6].…”