| 无源声呐场景中使用辅助粒子滤波的邻近目标检测前跟踪方法 |
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| 引用本文： | 王燕,李想,齐滨,梁国龙.无源声呐场景中使用辅助粒子滤波的邻近目标检测前跟踪方法[J].声学学报,2023,48(2):277-290. |
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| 作者姓名： | 王燕 李想 齐滨 梁国龙 |
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| 作者单位： | 1.哈尔滨工程大学 水声技术重点实验室 哈尔滨 150001 |
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| 基金项目： | 国家重点研发计划项目(2017YFC0306900)、国家自然科学基金项目(62101153)、国防基础科研计划项目(JCKY2019604B001)和水声技术重点实验室稳定支持课题项目(JCKYS2021604SSJS003)资助 |
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| 摘 要： | 针对无源声呐多目标方位跟踪问题,研究了一种基于粒子滤波的检测前跟踪方法,关注于改善邻近目标和机动目标的跟踪性能。首先,提出了一种考虑了邻近目标影响的似然函数;其次,采用辅助变量利用量测信息优化粒子采样,当算法运动模型与目标实际运动状态失配时,这种策略具有很大优势。结合以上两点,提出了一种检测前跟踪算法,该算法将邻近目标划分为一组,使用邻近目标的预测状态计算目标的似然,计算效率较高。利用仿真生成的数据和海上采集的实际数据分别验证了该算法的性能,并与其他多目标粒子滤波检测前跟踪算法进行比较,证明了该算法具有良好的跟踪性能。在目标邻近和目标机动的情况下,该算法的优势更加明显。
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| 关 键 词： | 纯方位跟踪 检测前跟踪 粒子滤波 目标跟踪 |
| 收稿时间： | 2022-04-11 |
A track-before-detect method for neighboring targets based on auxiliary particle filter in passive sonar scenarios |
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| Affiliation: | 1.Acoustic Science and Technology Laboratory, Harbin Engineering University Harbin 1500012.Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology Harbin 1500013.College of Underwater Acoustic Engineering, Harbin Engineering University Harbin 1500014.Qingdao Haina Underwater Information Technology Co., Ltd Qingdao 266400 |
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| Abstract: | The problem of multiple target tracking based on particle filter in passive sonar is studied, which becomes more difficult when there are nearby targets and maneuvering targets in the observation space. First, a likelihood function considering the influence of neighboring targets is proposed. Second, the auxiliary variable is utilized to consider the measurement information in the particle sampling process. This strategy has great advantages when the actual motion of the target is mismatched with the dynamic model of the algorithm. Then, a track-before-detect algorithm is proposed, which divides the neighboring targets into a group and calculates the likelihood of the target by using the predicted states of the neighboring targets with high computational efficiency. The performance of the proposed algorithm is verified by simulation data and data collected at sea. Compared with other multi-target particle filter track-before-detect algorithms, the proposed algorithm has better tracking performance, especially in the presence of neighboring and maneuvering targets. |
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