基于IGWO-STCPF的自主水下航行器跟踪方法OA
AUV tracking method based on Improved Grey Wolf Optimizer and Strong Tracking Cubature Kalman Particle Filter
提出了一种融合改进灰狼优化的强跟踪容积卡尔曼粒子滤波算法(IGWO-STCPF).该方法首先利用强跟踪容积卡尔曼滤波(STCKF)结合观测信息动态调整粒子均值和协方差,有效提高重要性采样的代表性;随后在重采样阶段引入信息熵加权的灰狼优化策略,以增强粒子的多样性并抑制退化现象.仿真实验表明,相比STCKF、标准粒子滤波(PF)、粒子群优化滤波(PSO-PF)和粒子群优化-立方卡尔曼粒子滤波(PSO-CPF)方法,所提算法在轨迹估计精度上分别提升了13.41%、18.58%、21.86%和21.33%.结果验证了IGWO-STCPF在复杂水下环境中具备更强的鲁棒性和跟踪性.
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.
邢传玺;孟轶涵;孟强;保德彪
云南民族大学电气信息工程学院/云南省无人自主系统重点实验室,云南 昆明 650504云南民族大学电气信息工程学院/云南省无人自主系统重点实验室,云南 昆明 650504云南民族大学电气信息工程学院/云南省无人自主系统重点实验室,云南 昆明 650504云南民族大学电气信息工程学院/云南省无人自主系统重点实验室,云南 昆明 650504
信息技术与安全科学
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
autonomous underwater vehiclePFSTCKFGWOinformation entropy
《中山大学学报（自然科学版）（中英文）》 2026 (1)
64-75,12
国家自然科学基金(61761048)云南省基础研究专项(202101AT070132)
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