State Adaptive Unscented Kalman Filter Algorithm and Its Application in Tracking of Underwater Maneuvering Target

被引：0
|
作者：
Ma Y. [1 ]
Liu X. [2 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi
[2] China Ship Development and Design Center, Wuhan, 430061, Hubei
来源：
Binggong Xuebao/Acta Armamentarii | 2019年 / 40卷 / 02期
关键词：
Adaptive filter; Course; Speed; Underwater maneuvering target tracking; Unscented Kalman filter;
D O I：
10.3969/j.issn.1000-1093.2019.02.016
中图分类号：
学科分类号：
摘要：
In order to meet the needs for tracking the underwater maneuvering target in real-time and predicting its location in the underwater confrontation environment, the higher requirements are put forward for accurate and fast estimation of target's sailing speed and course. The traditional unscented Kalman filter(UKF) tracking algorithm is improved for maneuvering target tracking with range and azimuth. The improved tracking algorithm can also be used to estimate the system status noise online in real-time without determining the state equation and the state noise variance in advance, thus tracking the maneuvering target adaptively. A novel adaptive UKF method is proposed, which adaptively adjusts the target state noise with residual probability distribution according to the residuals of predicted and observed values of UKF algorithm, so that the UKF tracking algorithm can adjust the state according to the target state the equation reduces the reliance on the predicted value when the target is maneuvering and increases the reliance on the prediction when the target is maneuvering. Primary numerical simulation results show that the algorithm not only has good tracking performance in target maneuvering but also has accurate estimation in non-maneuvering. At last, the tracking performance of state adaptive UKF algorithm is illustrated by sonar simulation system. © 2019, Editorial Board of Acta Armamentarii. All right reserved.
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页码：361 / 368
页数：7
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