Signal Detection Based on Gradient Clustering and Forward Consecutive Mean Excision
Ying Jian Kang, Hao Wu, Zhihua Zhao, Jin Meng · 2022
Aiming at the problem of signal detection in wireless channels, this paper proposes a dynamic threshold signal detection method based on gradient clustering and forward Consecutive Mean Excision (CFCME). Firstly, the mathematical model of signal detection in wireless channel and the implementation details and existing problems of traditional Consecutive Mean Excision algorithm (CME) are given. Then, a signal detection method based on gradient clustering and forward CME is designed to realize signal existence decision. The simulation and experimental results show that, compared with the traditional CME method, CFCME can effectively determine the presence or absence of signals in the wireless channel, and the detection performance is significantly improved.