Multi-Target CFAR Detection Method for HF Over-The-Horizon Radar Based on Target Sparse Constraint in Weibull Clutter Background

High frequency radar has a wide monitoring range and low range resolution. It may contain multiple targets or outlier interference phenomena in different clutter regions of the range-Doppler (RD) spectrum in detected background. The key to the performance of target detection in multi target backgrounds is the ability to determine the attributes of targets or outliers. Our previous research shows that the number of targets belongs to an absolute minority compared to the number of background units. In this paper, we propose a new method for multi-target detection building on the ordered statistics constant false alarm detector (OS-CFAR). The new method fully utilizes the sparse characteristics of the target and uses the idea of introducing regularization processing to eliminate interfering targets, and obtain an estimate of shape parameters for target detection. To further improve the performance of the algorithm, a correction method is proposed for the inaccurate selection of the k value. Upon estimating the distribution parameters, the detection threshold is calculated, and the target's constant false alarm detection is completed. Simulation and measured data show that our algorithm can effectively counter the interference of multiple targets and maintain a constant false alarm characteristic under different conditions, providing a reliable target detection method.

Automated summary

High frequency radar has a wide monitoring range and low range resolution, which can contain multiple targets or outlier interference phenomena. The key to multi-target detection in background clutter is determining the attributes of targets or outliers. This paper proposes a new method for multi-target detection based on the ordered statistics constant false alarm detector (OS-CFAR), utilizing the sparse characteristics of the target and introducing regularization processing to eliminate interfering targets. Simulation and measured data demonstrate the effectiveness of the algorithm in counteracting interference and providing reliable target detection.