Radar high-resolution range profile recognition via geodesic weighted sparse representation

One of the radar high-resolution range profile (HRRP) recognition issues is the target-aspect sensitivity. Both theoretical analysis and real-world data show that the HRRP shows a high correlation only in a very small aspect region. To overcome this problem, in traditional methods, the scattering centre model and averaged range profile are utilised. In this study, the authors present a graph-based semi-supervised method, called geodesic weighted sparse representation (GWSR), to overcome the target-aspect sensitivity problem. It is assumed that HRRP from different targets is located on different manifolds and the correlation information is utilised to separate these manifolds. In GWSR, the geodesic distance is calculated firstly and then the labelled HRRP is reconstructed by the geodesic weight. The nonlinear structure of HRRP can be transformed into a linear one through the reconstruction process. Then, the unlabelled HRRP is sparsely reconstructed and the sparse reconstruction weight can be utilised to estimate the label of the unlabelled HRRP from the given labels. Experiments on three kinds of ground target HRRPs with different backgrounds demonstrate the effectiveness of the authors' method.