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

摘要： 在UWB-IR信号检测中，针对目前所采用的量子粒子群FHN神经元模型易造成粒子群多样性降低，易陷入局部最优，导致求解精度不高的问题，对量子粒子群算法中量子更新参数引入混沌优化算法，提出了基于混沌量子粒子群算法的FHN神经元UWB-IR信号检测方法，分析了所提算法的收敛性，并对所提算法的性能进行仿真验证。仿真结果表明，所提算法与现有算法相比，可提高粒子群的多样性和算法的收敛速度，提高算法精度，实现多个系统参数同时最优，从不同噪声强度下自适应地检测出UWB-IR信号。

关键词: 超宽带, 信号检测, FHN神经元模型, 混沌量子粒子群