An Efficient Sparse Aperture ISAR Imaging Framework for Maneuvering Targets
Chen Chen, Zhiyong Xu, Sirui Tian
Abstract
Existing inverse synthetic aperture radar (ISAR) imaging methods under sparse aperture (SA) conditions are usually based on the assumption that the target motion is stationary, which limits their performance in practical applications. However, traditional ISAR imaging methods of maneuvering targets cannot be applied directly because they usually suffer from performance loss and computational burden under SA conditions. In this study, an efficient SA-ISAR imaging framework of maneuvering targets is proposed, which decomposes the multifactorial imaging problem into easy-solving subproblems. A fast phase compensation method that is a combination of modified eigenvector method and interpolation-based compensation matrix is proposed to efficiently remove the translational phase error and the nonuniform rotation-induced phase error. Meanwhile, the target rotation parameter is estimated by minimizing the image entropy to guarantee the compensation accuracy, where particle swarm optimization (PSO) is used achieve the global optimum efficiently. Within each iteration, alternating direction method of multipliers (ADMMs) algorithm is used to achieve fast image reconstruction, which suppresses the effects of SA to ensure the estimation accuracy. Experiments on simulated and measured data have verified that our proposed SA-ISAR imaging framework can achieve focused images of maneuvering targets. It outperforms in simple structure, high extensibility, and less computational burden.