ISAR Imaging Based on Sparse Signal Representation with Multiple Measurement Vectors

摘要

A new imaging method is proposed for ISAR with Multiple Measurement Vectors (MMV) based on sparse signal representation. As an extension of single measurement sparse signal representation, MMV can enhance the ability of suboptimal procedure to find the proper sparse solution and also offers potential robustness to noise, supposing the measurement vectors having a same sparsity structure. Simulations have shown its superior performance. Here it is first presented for ISAR imaging. Imaging result of real data shows it is a promising method for ISAR.