卡尔曼滤波器
平滑的
平方根
跟踪(教育)
水下
计算机科学
计算机视觉
快速卡尔曼滤波
扩展卡尔曼滤波器
算法
人工智能
数学
控制理论(社会学)
地质学
心理学
几何学
教育学
海洋学
控制(管理)
作者
Yao Qi-guo,Yung‐Yeh Su,Lili Li
标识
DOI:10.2991/macmc-17.2018.98
摘要
In passive tracking, the nonlinearity may cause computational complication and precision degradation.To solve this problem, a novel filtering-smoothing algorithm based on Square-Root Unscented Kalman Filter (SR-UKFS) is proposed to track underwater target.In the SR-UKFS algorithm, the Square-Root Unscented Kalman Filter (SR-UKF) is used as forward-filtering algorithm to provide current location results, and the Rauch-Tung-Striebel (RTS) algorithm smoothes the previous state vector and covariance matrix using the current location results.Comparative analysis and validation are made on the tracking performances of SR-UKFS algorithm and SR-UKF algorithm, and the simulation results show that, under the same conditions, the SR-UKFS can more effectively improve the tracking precision than the SR-UKF algorithm.The SR-UKFS algorithm can reduce nearly 59% of the position error and nearly 54% of the velocity error.
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