基于粒子群优化粒子滤波的SC-FDE系统信道估计方法

信道估计是SC-FDE系统中接收机对信道进行补偿的前提，针对该系统经典估计算法PF算法存在的粒子权值退化问题，结合粒子群算法，提出了基于粒子群寻优的改进PF算法的SC-FDE系统时变信道估计方法。在分析SC-FDE系统通信原理和建立信道估计动态空间模型的基础上，分析粒子滤波原理，引入粒子群寻优的思想，通过Logistic映射获得随机粒子序列，并利用PSO算法改善粒子分布区域。利用MATLAB软件将PSO PF算法与LS算法、EKF算法、DFT算法进行仿真对比，仿真结果表明，与其他传统信道估计算法相比，PSO PF算法在高斯噪声与非高斯噪声信道环境中均能有较低的误码率与归一化均方误差，并且在慢时变信道环境中性能更好。

A Channel Estimation in SC-FDE System Based on Particle Swarm Optimization Particle Filter

Channel estimation is the premise for receiver to compensate the channel in the SC-FDE system. Aimed at the problems that particle weight degradation exists in the PF algorithm of the classic estimation algorithm of the system in combination with the PSO algorithm, a timeC-varying channel estimation method for SC-FDE system based on particle swarm optimization and improved PF algorithm is proposed. On the basis of analyzing the SC-FDE system communication principle and the establishment of the channel estimation dynamic space model, the principle of particle filtering is analyzed, the idea of PSO algorithm is introduced, the random particle sequence is obtained through Logistic mapping, and the PSO algorithm is used to improve the particle distribution area. Use MATLAB software to compare the PSOC-PF algorithm with LS algorithm, EKF algorithm, and DFT algorithm. The simulation results show that compared with other traditional channel estimation algorithms, the PSOC-PF algorithm has lower BER and NMSE both in Gaussian noise channel and in nonC-Gaussian noise channel environments, and better performance in a slow timeC-varying channel environment.