Dual-Channel Detection of Sea-Surface Small Targets in Recurrence Plot

Currently, recurrence plot (RP) is a good tool to analyze the non-stationary sea clutter time series for small target detection in the existing detectors. To further improve detection performance, a dual-channel detector is proposed to fully exploit information from complex time series. First, amplitude time series and phase time series form two channels and generate two RPs. Second, two well-designed features are, respectively, extracted from the two RPs to construct a 2-D feature vector, called relative Shannon entropy (RSE) and relative truncate diagonal length (RTDL). Third, the decision threshold is determined by an improved extreme learning machine (IELM) classifier with false-alarm control, where target-guided prior information is utilized in parameter optimization. Finally, experimental results using IPIX datasets show that the proposed detector can attain good and robust performance, compared with the existing detectors.