中山大学学报（自然科学版）（中英文）2026，Vol.65Issue(1)：64-75,12.DOI:10.13471/j.cnki.acta.snus.ZR20250190
基于IGWO-STCPF的自主水下航行器跟踪方法
AUV tracking method based on Improved Grey Wolf Optimizer and Strong Tracking Cubature Kalman Particle Filter
摘要
Abstract
This paper proposes an Improved Grey Wolf Optimization-based Strong Tracking Cubature Kalman Particle Filter algorithm(IGWO-STCPF).The proposed method first employs a Strong Tracking Cubature Kalman Filter(STCKF)to incorporate measurement information for dynamically adjusting the particle mean and covariance,thereby enhancing the effectiveness of importance sampling.Then,an entropy-weighted GWO is introduced into the resampling stage to mitigate particle degeneration and improve estimation accuracy.Simulation results demonstrate that,compared with STCKF,PF,PSO-PF,and PSO-CPF algorithms,the proposed IGWO-STCPF improves trajectory estimation accuracy by 13.41%,18.58%,21.86%,and 21.33%,respectively.These results confirm the robustness and effectiveness of the proposed method in complex underwater scenarios.关键词
水下自主航行器/粒子滤波/强跟踪容积卡尔曼滤波/灰狼优化/信息熵Key words
autonomous underwater vehicle/PF/STCKF/GWO/information entropy分类
信息技术与安全科学引用本文复制引用
邢传玺,孟轶涵,孟强,保德彪..基于IGWO-STCPF的自主水下航行器跟踪方法[J].中山大学学报（自然科学版）（中英文）,2026,65(1):64-75,12.基金项目
国家自然科学基金(61761048) （61761048）
云南省基础研究专项(202101AT070132) （202101AT070132）
