Phase error compensation is very important for inverse synthetic aperture radar (ISAR) high-resolution imaging. However, the high-order motion of maneuvering target will cause the echo to produce spatial-variant phase error, traditional compensation methods cannot achieve good compensation results. Therefore, a phase error compensation method for maneuvering target is proposed. Firstly, the spatial-variant phase error model of maneuvering target is established. Then, the target motion is modeled as a high-order polynomial, and the phase error compensation model based on image entropy minimization is established. Finally, the whale optimization algorithm (WOA) algorithm is used to iteratively search the target motion parameters for phase error compensation. The proposed method's performance is demonstrated by the simulation results.
Compressive sensing is a promising theory that can sense data in a sub-Nyquist sampling. This theory has developed rapidly in many fields as soon as it appeared. It depends on the sparsity of the signal and senses the signal by low measurement and sparse reconstruction. In theory, sparse reconstruction is the core factor of compressive sensing theory and its application. In order to explore the feasibility of applying compressive imaging in remote sensing, this paper investigate the effect of sparse reconstruction on remote sensing images using typical datasets. The reconstruction algorithms such as convex optimization and greedy are introduced to implement the image reconstruction. The quality of reconstructed images is measured by PSNR index with respect to the different datasets and algorithms. The simulations show that the sparsely reconstructed remote sensing images keeps the image quality under different compression ratios of compressive imaging. The results verify the feasibility for applying compressive imaging in remote sensing.
In order to reduce the shadow in traditional linear SAR image, a multi-angle SAR non-coherent image fusion algorithm based on HIS statistic characteristics is proposed. By converting SAR image to HIS space, a threshold based on the statistic characteristics of SAR image’s HIS parameter is calculated and SAR images of different observation angles, which have been filtered according to the threshold previously calculated, are fused by non-coherent accumulation method. The fused image not only effectively reduces the image shadow, but also improves the detection probability of targets. The simulation results verify the effectiveness of the proposed algorithm.
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