High sidelobe analysis and reduction in multistatic inverse synthetic aperture radar imaging fusion with gapped data

Abstract

Radar observations from different angles are often discontinuous in multistatic inverse synthetic aperture radar (ISAR) imaging. Based on Fourier transform, such as Polar Format Algorithm and Range Doppler Algorithm, the discontinuity of the angle will make the performance of traditional ISAR imaging algorithm worse. The sidelobe of the image will rise and the mainlobe may split. Generally, it is necessary to pre-process the gapped data and then the traditional ISAR imaging algorithm is used for imaging. The most commonly used pre-processing method is to interpolate the gap. However, the performance of this method is not satisfied, especially when the gap is large. The reason of sidelobe rising and mainlobe splitting is first analysed. Then, a sidelobe reduction method based on compressive sensing (CS) is proposed. This method establishes a relationship between the complete data and the gapped data, and the complete data can be solved from the gapped data by CS method. After that, the complete data will be used for imaging by utilising traditional ISAR imaging algorithm and the high sidelobe will be reduced effectively. The effectiveness of the proposed method is verified by the analysis and the simulation results.