An Efficient Preprocessing Approach for Airborne Hybrid SAR and ISAR Imaging of Ship Target Based on Kernel Distribution

Abstract

The airborne hybrid synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging for the ship target is very important in the field of ocean surveillance, but it suffers from the problem of high computational complexity. In this paper, an efficient preprocessing approach for the airborne hybrid SAR and ISAR imaging based on the kernel distribution is proposed, which can reduce the computational complexity and preserve the image quality simultaneously. Firstly, the residual Doppler frequency is estimated. Then, a novel measurement method with good robustness is proposed for evaluating the stationarity of Doppler frequency from the aspect of statistics, which adopts the kernel distribution to estimate the probability distribution function (PDF) curve for the Doppler frequency. Afterwards, an effective preprocessing approach is addressed consequently to eliminate the incomplete illumination time and select the time interval with steady target motion. Results of simulated and actual SAR data verify the effectiveness of the novel algorithm proposed in this paper.