Parameter Estimation for Multi-Scale Multi-Lag Underwater Acoustic Channels Based on Modified Particle Swarm Optimization Algorithm

Abstract

The wideband underwater acoustic multipath channel can be modeled as a multi-scale multi-lag (MSML) channel because signals from different paths might experience different Doppler scales. This brings great challenge to channel parameter estimation. In this paper, we propose a novel algorithm for parameter estimation of MSML channels. This new algorithm is a modified particle swarm optimization (MPSO) algorithm, which can estimate the parameters of the Doppler scale, the time delay, and the amplitude simultaneously for each individual path. Comparing to PSO algorithm, MPSO algorithm uses a multipath list to record positions and fitness values of particles whose fitness values are selected as lbest s, and uses these lbest s to update particles’ velocities at each iteration. As for training sequence, we employ the zero correlation zone sequence which has excellent correlation properties. Computer simulation is used to evaluate the proposed algorithm in comparison with the matching pursuit (MP)-based method and the fractional Fourier transform (FrFT)-based method. Simulation results confirm that the proposed MPSO algorithm outperforms both MP-based method and FrFT-based method in estimation accuracy as well as computation complexity.