6/19/2023 0 Comments Omegat timetracker![]() To obtain a considerably high range and cross range resolution targets’ image through RD algorithm, the bandwidth of radar transmitted signal must be sufficiently large and the targets’ observation duration must be long enough. Under this imaging framework, range resolution is proportional to the bandwidth of transmitted signal, and cross-range resolution is dependent on both the coherent processing interval (CPI) as well as the target rotational motion from variation of radar viewing angles. In conventional ISAR imaging framework, ISAR images are usually obtained using the range-doppler (RD) algorithm which is based on the 2D Fourier transformation. In these applications, a critical requirement of the ISAR image is to achieve high resolution in both range and cross-range domains. ISAR imagery plays an important role in military and civilian applications such as target identification, recognition and classification. ![]() Inverse synthetic aperture radar (ISAR) has been proven to be a powerful signal processing technique for imaging moving targets in range and cross-range domain under all-weather circumstances. Both the peak sidelobe ratio (PSLR) and the reconstruction relative error (RE) indicate that the proposed method outperforms the l 1 norm based method. ![]() Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Instead of simultaneous perturbation stochastic approximation (SPSA), we use weighted l 1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Different from the traditional l 1 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. In this paper, we propose an improved version of CS-based method for inverse synthetic aperture radar (ISAR) imaging. 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.
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