Adaptive detection of multi-targets of HFSWR based on ES-ELM and FRFT

High frequency surface wave radar (HFSWR) has important military and civilian significance for maritime surveillance. However, in echo signal of HFSWR, the signals of targets to be detected are often submerged in sea clutter and various background noises. Therefore, effective suppression of sea clutter and adaptive detection of multi-targets are the key and difficult technique of maritime surveillance. In this paper, an adaptive detection algorithm of multi-targets combining error self-adjustment extreme learning machine (ES-ELM) and fractional Fourier transform (FRFT) is proposed. In the algorithm, phase space reconstruction theory is employed to obtain the optimal state space of ELM, and an ES-ELM is used to model and predict the sea clutter and suppress it effectively. In addition, according to the agglomeration characteristic of the peaks of objects in FRFT domain, Haar-like descriptor is employed to obtain the morphological features of target points in FRFT domain, and a proposed ES-ELM based method is used to identify the multi-targets automatically and adaptively. The experimental results show that the proposed algorithm has good sea clutter suppression ability and realizes adaptive detection of multi-targets in background of high sea clutter with high detection rate.