水道港口2025，Vol.46Issue(3)：450-459,10.
粒子群优化算法在水下磁法探测中的应用
Application of particle swarm optimization algorithm in underwater magnetic detection
摘要
Abstract
Effective detection of underwater unexploded ordnance(UXO)is crucial for marine environmental safety.Traditional magnetic detection methods are often constrained by environmental noise and equipment instability,leading to diminished data analysis accuracy.In this study,a wavelet threshold denoising method based on particle swarm optimization(PSO)was proposed to enhance data processing quality for underwater magnetic detection.Simulations and field experiments were conducted using MATLAB,and the denoising performances of various wavelet bases and threshold functions were compared.The PSO algorithm was employed to adaptively optimize the denoising threshold.Experimental results indicate that the PSO-based wavelet threshold denoising method effectively improves the signal-to-noise ratio(SNR),reduces the mean squared error(MSE),and preserves more details of the original magnetic anomaly signals in complex marine environments.As a result,it provides a more reliable data foundation for subsequent magnetic anomaly detection and target inversion.The effectiveness and practicality of the proposed method in underwater magnetic detection data processing are confirmed,offering valuable reference for similar projects.关键词
小波阈值去噪/粒子群优化算法/水下未爆物/阈值函数/评价指标/磁力探测Key words
wavelet threshold denoising/particle swarm optimization(PSO)algorithm/unexploded ordnance(UXO)/threshold function/evaluation indicator/magnetic detection分类
海洋学引用本文复制引用
陈昆明,李祥龙,隋海琛,雷鹏,郑重..粒子群优化算法在水下磁法探测中的应用[J].水道港口,2025,46(3):450-459,10.基金项目
天津市科技计划项目(23YDPYCG00010) （23YDPYCG00010）
中央级公益性科研院所科研创新基金项目(TKS20240804) （TKS20240804）
