Multitarget CFAR Detection Method Based on Neural Network for HF Hybrid Sky-Surface Wave Radar in Weibull Clutter Background

The high-frequency (HF) hybrid sky-surface wave radar has a wide surveillance range and relatively low distance resolution. In real environments, the detection background of the range-Doppler (RD) spectrum is usually a mixed background of different types of clutter overlaid. Therefore, it is difficult to obtain stable and reliable background information as prior knowledge. In this case, traditional constant false alarm rate (CFAR) detectors designed based on specific background models may not be fully adaptable. Our research group found through a large amount of measured data analysis that the actual RD spectrum detection unit has a good fitting effect on the Weibull distribution. Based on the measured RD spectrum of HF hybrid sky-surface wave radar, this article proposes a multitarget CFAR detection algorithm for HF hybrid sky-surface wave radar based on the autoencoder (AE) neural network under the Weibull clutter distribution model. This new method harnesses the pattern recognition ability of deep neural networks to estimate the background distribution parameters of detection units in the RD spectrum under different radar configurations and sea conditions, thereby obtaining the detection threshold under the specified false alarm probability and completing intelligent target detection. Finally, by verifying the RD spectrum of the measured data of the nonuniform clutter background of the HF hybrid sky-surface wave radar, the results show that the algorithm can well recognize the targets, and it can also recognize the simulated targets added under different signal-to-clutter ratios (SCRs). Also, under the same simulation conditions, our proposed detector has better detection performance.