Volume 38, Number 2, April 2020
|Page(s)||359 - 365|
|Published online||17 July 2020|
Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048 China
2 Shaanxi Key Laboratory for Network Computing and Security Technology, Xi'an, 710048 China
3 School of Marine Science and technology, Northwestern Polytechnical University Xi'an 710072, China
4 College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, 741200, China
Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.
针对强干扰环境水下纯方位多目标跟踪的非线性、不可观测性以及数据关联模糊等问题，基于期望极大化算法，结合扩展卡尔曼滤波（extended Kalman filter，EKF）平滑算法和无味卡尔曼滤波（unscented Kalman filter，UKF）平滑算法，提出了基于EKF和UKF的多传感器多目标纯方位概率多假设跟踪（probabilistic multiple hypothesis tracking，PMHT）算法。纯方位PMHT算法通过引入目标和量测数据之间的关联变量来解决量测与目标之间的数据关联模糊问题。简化了基于EKF平滑算法的多传感器纯方位PMHT算法，避免堆积每个传感器的合成量测，有效减小了运算量。仿真结果表明，在水下强干扰环境下，对于静止多观测站和机动单观测站，2种算法对多个交叉运动目标和邻近运动目标的航迹关联成功率高，抗干扰性能好，并且运算量小，证明了算法的有效性。
Key words: bearing-only / multi-target tracking / probabilistic multiple hypothesis tracking / data association / extended Kalman filter / unscented Kalman smoother
关键字 : 纯方位 / 多目标跟踪 / 概率多假设跟踪 / 数据关联 / 扩展卡尔曼滤波 / 无味卡尔曼滤波
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.