Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

Abstract

It takes a long time to detect target information in noisy radar information and reduce the probability of false alarm. Therefore, it has become a research direction to reduce the probability of false alarm and the time of effective target detection. This paper introduces a new method to reduce the occurrence of false alarm in non-uniform environment and improve the efficiency of target detection. The proposed method involves a faster and more stable method that involves preprocessing the data set, splitting it into smaller parts, and utilizing a KTH minimum value M determined by an ordered statistics class constant false alarm detection algorithm. Each data point in the small segment is then compared to M, anything above M is classified as a target, and anything below M is ignored as clutter. Then ESVI-CFAR detection was performed on the selected target to obtain the final detection result. Experimental results show that the proposed method is superior to the traditional VI-CFAR and has better target detection performance.

Keywords

• data set
• ESVI-CFAR
• false alarm probability
• noise
• VI-CFAR