A Multiple-Target Detection Algorithm Based on Mixed Echo Model for HFSWR

A high-frequency surface-wave radar (HFSWR) is a tool used for maritime surveillance and plays an important role in sea surface target detection. However, multiple-target detection in HFSWR is a challenging issue due to the presence of various types of clutter (such as sea clutter, ionospheric clutter, and ground clutter) in the actual range-Doppler (RD) map. To address this problem, a mixed model is established to describe the statistical characteristics of HFSWR echoes. The mixed model uses the Weibull distribution and the Swerling I fluctuation model to fit the amplitudes of clutter and sea surface target echoes, respectively. This article proposes a robust constant false-alarm rate (CFAR) detector for HFSWR, as the detection performance of many CFAR detectors is unsatisfactory in such detection backgrounds. The proposed CFAR detector constructs the objective function considering the fitting degree of HFSWR echoes while introducing a regularization term based on outlier sparsity to enhance the model’s generalization ability. To more accurately estimate the distribution parameters of detection background, this CFAR detector first employs the maximum likelihood (ML) method to estimate outliers and censors cells containing outliers and then uses the remaining units to estimate the parameters. The proposed CFAR detector outperforms conventional CFAR detectors in both Monte Carlo simulation tests and also performs well in measured data tests.