COMPRESSIVE SENSING ISAR IMAGING WITH STEPPED FREQUENCY CONTINUOUS WAVE VIA GINI SPARSITY

In this paper, we propose an improved version of CS-based model for inverse synthetic aperture radar (ISAR) imaging, which can sustain strong clutter noise and provide high quality images with extremely limited measurements. Different from traditional l(1) norm based CS ISAR imaging models, the essential of our model is to use the Gini index to measure the sparsity of signals. We also develop an iteratively re-weighted algorithm to find the solution of our model and reconstruct sparse signals from compressed samples. Experimental results of point targets and complex scene show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise and outperforms l(1) norm based methods.