关键词: 超宽带, 信号检测, FHN神经元模型, 混沌量子粒子群

Abstract: For quantum particle swarm FHN neuron model reduces the particle swarm diversity, easy to fall into local optimum, leads to a lower accuracy in UWB-IR signal detection, chaos optimization algorithm is introduced to quantum update parameter in quantum particle swarm optimization. A UWB-IR signal detection method based on chaotic quantum particle swarm optimization is proposed for FHN neurons model. The convergence of the proposed algorithm is analyzed. The performance of the proposed algorithm is simulated. Simulation results show that the proposed algorithm is able to improve the diversity of particle swarm and the convergence rate and the accuracy, attain the optimal system parameter simultaneously. The UWB-IR signal is adaptively detected under different noise intensities.

Key words: ultra-wideband, signal detection, FHN neurons model, chaotic quantum particle swarm optimization