ISAR Imaging of Non-Stationary Moving Target Based on Parameter Estimation and Sparse Decomposition

This paper studies the inverse synthetic aperture radar imaging problem for a non-stationary moving target and proposes a non-search imaging method based on parameter estimation and sparse decomposition. The echoes received by radar can be thought of as consisting of chirp signals with varying chirp rates and center frequencies. Lv's distribution (LVD) is introduced to accurately estimate these parameters. Considering their inherent sparsity, the signals are reconstructed via sparse representation using a redundant chirp dictionary. An efficient algorithm is developed to tackle the optimization problem for sparse decompositions. Then, by using the reconstructed data, adaptive joint time-frequency imaging techniques are employed to create high-quality images of the non-stationary moving target. Finally, the simulated experiments and measured data processing results confirm the proposed method's validity.

Automated summary

This paper investigates the problem of inverse synthetic aperture radar imaging for a non-stationary moving target and proposes a non-search imaging method based on parameter estimation and sparse decomposition. The received radar echoes are modeled as chirp signals with varying chirp rates and center frequencies. The Lv's distribution (LVD) is introduced to accurately estimate these parameters. Via sparse representation using a redundant chirp dictionary, the signals are reconstructed, and an efficient algorithm is developed for sparse decomposition. The reconstructed data is then utilized to employ adaptive joint time-frequency imaging techniques for generating high-quality images of the non-stationary moving target. Simulated experiments and measured data processing results confirm the validity of the proposed method.