Distributed Radar Target Detection Based on RF-SSA in Non-Gaussian Noise

Distributed radar target detection in non-Gaussian noise, modeled as the sum of K-distributed clutter plus thermal noise, is considered in this paper. The conventional target techniques, e.g., constant false-alarm rate (CFAR), scatterer density-dependent generalized likelihood ratio test (SDD-GLRT), and energy integration (EI) detectors, have limited performance. On the other hand, since radar target detection can be considered a classification task, deep learning techniques have been widely applied as radar detectors in recent years, but such techniques require a larger amount of training samples to prevent overfitting, which is time-consuming. To balance detection efficiency and accuracy, this paper proposes an improved random forest algorithm based on the sparrow search algorithm (RF-SSA). First, we propose a mixed method of 3DT space-time adaptive processing and wavelet denoising (3DT-WD) to improve the output signal-to-clutter plus-noise ratio. Then, the SSA is applied to the RF algorithm to adaptively obtain the optimal parameters of the detection model. The simulation results show that the proposed RF-SSA ensures higher detection performance than the other classical methods, showing a gain of about 2 dB at the same detection probability. Moreover, the detection results of the real data further confirm the superiority of the proposed RF-SSA.

Automated summary

This paper proposes an improved RF-SSA algorithm to balance detection efficiency and accuracy. The algorithm combines the 3DT-WD method and SSA algorithm to obtain higher detection performance through parameter optimization. Simulation experiments and real data results have demonstrated the superiority of this approach.