Research Article
Artificial Fish Swarm Algorithm-Based Sparse System Estimation
@INPROCEEDINGS{10.1007/978-3-030-72792-5_43, author={Si Zhang and Dan Li and Lulu Bei and Kailiang Zhang and Ping Cui}, title={Artificial Fish Swarm Algorithm-Based Sparse System Estimation}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part I}, proceedings_a={SIMUTOOLS}, year={2021}, month={4}, keywords={Sparse system estimation Artificial fish swarm algorithm Underwater acoustic channel Doppler spread}, doi={10.1007/978-3-030-72792-5_43} }- Si Zhang
Dan Li
Lulu Bei
Kailiang Zhang
Ping Cui
Year: 2021
Artificial Fish Swarm Algorithm-Based Sparse System Estimation
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-72792-5_43
Abstract
In this paper, the estimation of Doppler-distorted underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe multipath spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (Doppler scale factor, delay, amplitude). Based on this, orthogonal matching pursuit (OMP) algorithm has been widely used. But the estimation accuracy of OMP depends on the size of the dictionary and finer resolution requires higher computational complexity. Thus, this paper proposes a new method called improved artificial fish swarm algorithm (IAFSA), for the UWA channel estimation. Different from basic AFSA, IAFSA proceeds in an iterative manner to separate multipath and will adaptively adjust fish’s positions and step during each sub-iteration, thus can achieve fine resolution and fast convergence. The performance of the IAFSA is evaluated by various numerical simulations, including channels generated by BELLHOP. The simulation results show that IAFSA outperforms OMP algorithm in both estimation accuracy and computational complexity.
