一种新颖的水声信道参数估计算法

宽带水声信道通常被建模为多扩展多时延（MSML）信道，不同路径的多普勒扩展不同给信道参数估计带来了挑战.本文提出一种新颖的MSML信道参数估计算法，称之为改进的粒子群优化（MPSO）算法.该算法创建一个多径列表来记录局部最优粒子的位置和适应度值，并用于迭代中粒子速度的更新.算法结束时，根据多径列表的记录值，可以同时估计出每一径的多普勒扩展、时延和幅度参数.仿真结果表明：MPSO算法的参数估计精度优于匹配追踪（MP）算法和分数阶傅里叶变换（FrFT）算法.

A Novel Parameter Estimation Algorithm for Underwater Acoustic Channels

The wideband underwater acoustic multipath channels are usually modeled as a multi-scale multi-lag(MSML)channel.Different paths possess different Doppler scales which brings great challenge to channel parameter estimation.In this paper,we propose a novel algorithm for parameter estimation of MSML channels,which is called modified particle swarm optimization(MPSO)algorithm.This algorithm creates a multi-path list to record positions and fitness values of particles which are local best(lbests),and uses these lbests to update particles' velocities at each iteration.At the end of the algorithm,the Doppler scale,the time delay and the amplitude can be estimated simultaneously for each path according to the multi-path list.Simulation results show that,the MPSO algorithm outperforms both the matching pursuit(MP)algorithm and the fractional Fourier transform(FrFT)algorithm in estimation accuracy.