Aerial target classification using an LFL-Net with multiview HRRPs

Non-cooperative aerial target classification is one of the most attractive but challenging tasks in radar remote sensing applications. Multiview high-range resolution profiles (HRRPs) of the aerial target contain abundant information and will benefit to classification. In this paper, a new aerial target classification method based on an end-to-end lightweight feature learning network (LFL-Net) with multiview HRRPs is proposed. The aerial target classification scenario using multiview HRRPs is first studied and modelled. Then a LFL-Net with multi-inputs and some distinct modules is designed to effectively learn the target classification information from the multiview HRRPs. Therefore, the proposed method can achieve accurate and reliable classification results under different signal-to-noise ratios (SNRs). Experimental results have shown the superiorities of the proposed non-cooperative aerial target classification method.