Generating dense and super-resolution ISAR image by combining bandwidth extrapolation and compressive sensing

Abstract

Recent developing compressive sensing (CS) theory indicates that it is possible to obtain precise recovery of a sparse signal from very limited measurements, which provides a new way for data acquisition and signal processing as nature signals usually involve some degree of sparsity. In this paper, we present an algorithm for inversed synthetic aperture radar (ISAR) imaging with super resolution by combining CS and bandwidth extrapolation (BWE) technique. For ISAR imaging, the backscattering field of target is usually contributed by a few strong scattering centers, whose number is much less than that of image pixels. Thus, CS is intuitively suitable for constructing super resolution ISAR image. According to CS theory, the number of extracted dominating scatterers relies on the signal length, which indicates that if only limited data is available, it is difficult to generate dense ISAR image robustly by CS, and some signal components tend to lose. To soften this constraint, BWE is combined with CS imaging to increase the degree of freedom of signal while preserving its coherence. A refined CS-based formation for ISAR image-resolution enhancement is then developed. Both real and simulated data experiments are performed to evaluate the proposed approach, and an example of using this technique demonstrates the enhanced image resolution in application of maneuvering target imaging.