桂林电子科技大学学报2026，Vol.46Issue(4)：331-337,7.DOI:10.16725/j.1673-808X.202458
基于自适应噪声计算的水下机动目标跟踪算法
Underwater maneuvering target tracking algorithm based on adaptive noise estimation
摘要
Abstract
Underwater target tracking is a crucial component of marine exploration missions.Due to complex underwater environ-ments and target motion model switching,unknown statistical characteristics of noise arise,while employing fixed observation noise significantly degrades filtering accuracy.To address the unknown statistical properties of underwater observation noise and model switching delays,an adaptive interactive multiple-model square-root cubature Kalman filter incorporating the Sage-Husa noise esti-mation method is proposed and applied to a stationary dual-observation system.Initially,the square-root cubature Kalman filter(SR-CKF)is utilized to estimate the target state,while incorporating a time-varying Sage-Husa noise estimator to adaptively estimate ob-servation noise and correct the state estimates in real time.Meanwhile,within the interactive multiple model(IMM)algorithm,the transition probability matrix was updated by utilizing the rate of change in model probabilities at adjacent time steps,thereby im-proving the match between motion models and the actual trajectory.Simulation results show that the proposed adaptive IMM-SR-CKF improves model-switching speed and adaptively estimates the statistical characteristics of the observation noise.In variable-noise environments,the proposed algorithm achieves superior tracking performance and outperforms conventional nonlinear filter-ing methods.关键词
水下目标跟踪/容积卡尔曼滤波/Sage-Husa/交互式多模型/转移概率矩阵Key words
underwater target tracking/cubature Kalman filter/Sage-Husa/interactive multiple model/transfer probability matrix分类
通用工业技术引用本文复制引用
陈名松,赵申奇,陈哲..基于自适应噪声计算的水下机动目标跟踪算法[J].桂林电子科技大学学报,2026,46(4):331-337,7.基金项目
国家自然科学基金(91836301) （91836301）
广西科技基地和人才专项(桂科AD21220098) （桂科AD21220098）
认知无线电与信息处理教育部重点实验室开放基金 （）
桂林电子科技大学研究生教育创新计划(2023YCXS030) （2023YCXS030）
