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论文:2020,Vol:38,Issue(2):359-365 |
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引用本文： |
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李晓花, 李亚安, 鲁晓锋, 赵晨旭, 蔚婧. 强干扰环境下水下纯方位PMHT多目标跟踪[J]. 西北工业大学学报 |
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LI Xiaohua, LI Ya'an, LU Xiaofeng, ZHAO Chenxu, YU Jing. Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT[J]. Northwestern polytechnical university |
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| 强干扰环境下水下纯方位PMHT多目标跟踪 |
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| 李晓花1,2, 李亚安3, 鲁晓锋1,2, 赵晨旭4, 蔚婧3 |
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1. 西安理工大学 计算机科学与工程学院, 陕西 西安 710048;
2. 陕西省网络计算与安全技术重点实验室, 陕西 西安 710048;
3. 西北工业大学 航海学院, 陕西 西安 710072;
4. 国防科技大学 机电工程与自动化学院, 湖南 长沙 741200 |
| 摘要: |
| 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。 |
| 关键词:
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
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| Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT |
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| LI Xiaohua1,2, LI Ya'an3, LU Xiaofeng1,2, ZHAO Chenxu4, YU Jing3 |
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1. 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 |
| Abstract: |
| 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. |
| Key words:
bearing-only
multi-target tracking
probabilistic multiple hypothesis tracking
data association
extended Kalman filter
unscented Kalman smoother
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| 收稿日期: 2019-01-07
修回日期:
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| DOI: 10.1051/jnwpu/20203820359 |
| 基金项目: 国家自然科学基金（61703333）、陕西省自然科学基础研究计划一般项目（2019JQ-746）和陕西省教育厅自然科学研究项目（18JK0557）资助 |
| 通讯作者:
Email： |
| 作者简介: 李晓花(1986-),西安理工大学讲师、博士,主要从事目标跟踪及多传感器信息融合等研究。
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