基于粒子群优化的MPSK信号频偏估计算法

Frequency estimation of MPSK signals based on PSO algorithm

针对传统多进制数字相位调制(MPSK)信号非数据辅助(NDA)频偏估计算法存在估计范围有限、估计方差较大、符号个数少时估计性能受限等问题,提出了基于粒子群优化的频偏估计方法。该算法以频偏估计的似然函数为目标函数,模拟群体智能搜索最优解。仿真结果表明,本算法无偏估计范围大,在符号数较少、信噪比较低时,估计方差接近克拉美罗下限(CRLB),性能优于经典的离散傅里叶变换(DFT)算法和Kay算法。

Traditional Non-Data-Aided(NDA) frequency offset estimation algorithm of Multiple Phase Shift Keying(MPSK) signals feature some disadvantages like small estimation range, large estimation variance, and limited performance for small symbol number. Aiming at these problems, a new frequency offset estimation algorithm based on particle swarm optimization is proposed. This algorithm takes the maximum likelihood function of frequency offset estimation as goal-function, imitating swarm intelligence to search the optimal solution. Simulation results validate that this algorithm has a large estimation range and the estimation variance is closed to the Cramer-Rao Lower Bound(CRLB) at small symbol number and low SNR, which is better than other classical algorithms like Discrete Fourier Transform(DFT) and Kay algorithm.