文章摘要
高阗琦,陈虹宇,张峻铭,李可非.基于BELLHOP模型的水下多目标跟踪算法研究[J].声学技术,2023,42(2):248~255
基于BELLHOP模型的水下多目标跟踪算法研究
Research on underwater multi-target tracking algorithm based on BELLHOP ray acoustic model
投稿时间:2021-10-21  修订日期:2021-12-08
DOI:10.16300/j.cnki.1000-3630.2023.02.019
中文关键词: BELLHOP模型  目标回波信号  高斯混合概率假设密度  无迹卡尔曼滤波
英文关键词: the BELLHOP model  acoustic echo signal  Gaussian mixture probability hypothesis density  unscented Kalman filter
基金项目:国防科技重点实验室基金项目(SSDKKFJJ-2019-02-01)。
作者单位E-mail
高阗琦 中国科学院声学研究所北海研究站, 山东青岛 266114  
陈虹宇 中国船舶工业系统工程研究院, 北京 100036  
张峻铭 中国船舶工业系统工程研究院, 北京 100036  
李可非 中国科学院声学研究所北海研究站, 山东青岛 266114 likefei@mail.ioa.ac.cn 
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中文摘要:
      针对高频主动声呐的深海多目标跟踪问题,提出了基于BELLHOP模型的无迹卡尔曼滤波-高斯混合概率假设密度(Unscentesd Kalman Filter-Gaussian Mixture-Probability Hypothesis Density,UKF-GM-PHD)水下多目标跟踪算法。该算法首先利用BELLHOP射线声学模型,计算出本征声线、目标信号的幅度、相位及时延信息,以此构造目标回波信号并叠加高斯白噪声。然后,由回波信号计算得到目标相对于观测站的距离、方位角和俯仰角信息,作为目标跟踪系统中的量测信息。最后利用提出的UKF-GM-PHD多目标跟踪算法,实现高频主动声呐非线性系统的多目标跟踪。仿真结果表明,在深海高频主动声呐条件下,文章提出的UKF-GM-PHD多目标跟踪算法较传统高斯混合概率假设密度(Gaussian Mixture Probability Hypothesis Density,GM-PHD)方法,明显降低了目标丢失率,并且最优子模式指派统计量(Optimal Sub-Patter Assignment,OSPA)距离也更小,跟踪效果更好。
英文摘要:
      An Unscented Kalman Filter-Gaussian Mixture Probability Hypothesis Density (UKF-GM-PHD) underwater multi-target tracking algorithm based on the BELLHOP model is proposed for the deep-sea multi-target tracking problem of high-frequency active sonar. Firstly, the BELLHOP ray acoustic model is used for the calculation of the amplitude, phase and time delay information of the intrinsic sound line and target signal and for the construction of target echo signal with Gaussian white noise. Then, the distance, azimuth, and pitch angle of the target relative to the observation station, which are calculated from the constructed echo signal, are used as the measurement information in the target tracking system. Finally, the proposed UKF-GM-PHD multi-target tracking algorithm is applied to multi-target tracking of the nonlinear system of high frequency active sonar. The simulation results show that compared with the traditional Gaussian Mixture Probability Hypothesis Density (GM-PHD) method, the proposed UKF-GM-PHD multi-target tracking algorithm can significantly reduces the target loss rate for deep-sea highfrequency active sonar, and the optimal sub-patter assignment (OSPA) distance is smaller and the tracking effect is better.
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