在噪声复杂多变的海上环境上，针对传统卡尔曼滤波算法在被动声呐纯方位跟踪场景中存在的滤波发散问题，提出一种残差自适应的纯方位伪线性卡尔曼滤波算法。将观测残差引入到伪线性卡尔曼滤波中，改进直接用于自适应估计伪线性观测噪声方差，并通过SAM准则对滤波的过度补偿进行修正。结果表明，在观测噪声方差不匹配时，本文算法的稳定性和性能优于传统的偏差补偿伪线性卡尔曼滤波算法；在偏差补偿伪线性卡尔曼滤波算法发散情况下，所提算法与IEKF、EKF 算法相比，位置方向上时间平均均方根误差（RTAMS）分别减少44.87%和64.88%，速度方向上时间平均均方根误差分别减少17.30%和30.99%，在改善伪线性卡尔曼滤波的稳定性的同时，增大了补偿算法的性能，可以为海上环境的机动平台目标跟踪提供参考意义。
In a maritime environment with complex and changeable noise, a residual adaptive bearing-only pseudo-linear Kalman filter algorithm is proposed to address the filter divergence problem of the traditional Kalman filter algorithm in passive sonar bearing-only tracking scenarios. The observation residual is introduced into the pseudo-linear Kalman filter, and the improvement is directly used to adaptively estimate the pseudo-linear observation noise variance, and the over-compensation of the filter is corrected through the SAM criterion. The results show that when the observation noise variance does not match, the stability and performance of the proposed algorithm are better than the traditional bias compensation pseudo-linear Kalman filter algorithm; when the bias compensation pseudo-linear Kalman filter algorithm diverges, the proposed algorithm is consistent with IEKF, Compared with the EKF algorithm, the time-averaged root mean square error (RTAMS) in the position direction is reduced by 44.87% and 64.88% respectively, and the time-averaged root mean square error in the speed direction is reduced by 17.30% and 30.99% respectively. In improving the pseudo-linear Kalman filter While improving stability, it also increases the performance of the compensation algorithm, which can provide reference significance for mobile platform target tracking in the maritime environment.
2025,47(4): 130-136 收稿日期：2024-4-28
DOI：10.3404/j.issn.1672-7649.2025.04.021
分类号：P228.4
基金项目：国家自然科学基金叶企孙重点项目（U2341228）
作者简介：陈启航(2001-),男,硕士研究生,研究方向为目标跟踪
参考文献：
[1] 谢伟, 陶浩, 龚俊斌, 等. 海上无人系统集群发展现状及关键技术研究进展[J]. 中国舰船研究, 2021, 16(1): 7-17+31.
[2] 陈帅, 王宁, 陈廷凯, 等. 置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用[J]. 中国舰船研究, 2022, 17(1): 203-211.
[3] 孙彧, 潘宣宏, 王幸军, 等. 无人潜航器装备技术发展及作战运用研究[J]. 舰船科学技术, 2023, 45(21): 104-109.
[4] 杨波, 李敬辉, 吉顺东. 水下战场UUV侦察与监视研究[J]. 舰船电子工程, 2014, 34(7): 15-17+41.
[5] 刘忠, 周丰等. 纯方位目标运动分析 [M]. 北京：国防工业出版社, 2009.
[6] KALMAN R E. A new approach to linear filtering and prediction problems [J]. 1960.
[7] AIDALA V J. Kalman filter behavior in bearings-only tracking applications [J]. IEEE Transactions on Aerospace and Electronic Systems, 1979, (1): 29-39.
[8] 张俊根. 分布式伪线性卡尔曼滤波纯方位跟踪[J]. 控制工程, 2023, 30(4): 739-745.
[9] DAUM F. Nonlinear filters: beyond the Kalman filter[J]. IEEE Aerospace and Electronic Systems Magazine, 2005, 20(8): 57-69.
[10] DOGANCAY K. Bias compensation for the bearings-only pseudolinear target track estimator[J]. IEEE Transactions on Signal Processing, 2005, 54(1): 59-68.
[11] 卢晓林, 刘健, 刘忠. 一种纯方位跟踪中的自适应滤波算法 [J]. 海军工程大学学报, 2007, (1): 86-89+98.
[12] 孙铭芳, 吕旭, 赵仁杰, 等. 基于新息自适应的扩展卡尔曼滤波雷达目标跟踪算法[J]. 科学技术与工程, 2023, 23(9): 3738-3743.
[13] 王广玉, 窦磊, 窦杰. 基于自适应卡尔曼滤波的多目标跟踪算法[J]. 计算机应用, 2022, 42(S1): 271-276.
[14] 周萌萌, 张冰, 赵强, 等. 基于自适应渐消Sage-Husa扩展卡尔曼滤波的协同定位算法[J]. 中国舰船研究, 2022, 17(4): 92-97.
[15] 李理敏, 龚文斌, 刘会杰, 等. 基于自适应扩展卡尔曼滤波的载波跟踪算法[J]. 航空学报, 2012, 33(7): 1319-1328.
[16] NGUYEN N H, DOĞANÇAY K. Improved pseudolinear Kalman filter algorithms for bearings-only target tracking[J]. IEEE Transactions on Signal Processing, 2017, 65(23): 6119-6134.
