Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (17): 253-259.DOI: 10.3778/j.issn.1002-8331.2006-0044

Previous Articles     Next Articles

Underwater Bearing-Only Multi-target Tracking in Dense Clutter Environment Using Single Observation

LI Xiaohua, SU Jun, LI Xiuxiu   

  1. 1.Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
    2.School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
  • Online:2021-09-01 Published:2021-08-30



  1. 1.西安理工大学 计算机科学与工程学院,西安 710048
    2.西北工业大学 航海学院,西安 710072


The weakness of the basic Probabilistic Multiple Hypothesis Tracking (PMHT) is in its sensitivity on the targets’ initialization. Once the estimated initialization is different from the real initialization, the tracking performance of the PMHT is degraded seriously. Based on the principle of PMHT and the deterministic annealing approach, combining the Unscented Kalman Smoother(UKS) algorithm, it proposes a new multi-target tracking algorithm: deterministic annealing PMHT. It compares and analyzes the estimation accuracy and calculation cost of the deterministic annealing PMHT and PMHT in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets under the dense clutter environment. The experimental results demonstrate that PMHT algorithm’s tracking performance degrades severely when setting a relatively bad initialization value, while the correct association of deterministic annealing PMHT is of high probability. The deterministic annealing PMHT has effective detection performance and noise tolerance, and it has little computational load at the same time, and confirms the effectiveness of the deterministic annealing PMHT to the bearing-only multiple target tracking in dense clutter.

Key words: probabilistic multiple hypothesis tracking, bearing-only, multi-target tracking, unscented Kalman smoother


针对概率多假设跟踪(Probabilistic Multiple Hypothesis Tracking,PMHT)算法对多目标状态初始值敏感问题,结合确定性退火技术,提出了改进的PMHT算法。该算法借鉴确定性退火过程,在多目标的条件概率函数中引入退火因子,增加了多目标与纯方位量测数据关联的准确性,提高了多目标后验关联概率的精确度。针对纯方位量测的非线性性,利用无味卡尔曼平滑(Unscented Kalman Smoother,UKS)算法进行滤波。仿真结果表明,当多目标状态初始值与真实值相差较大时,对于强干扰环境下单观测站纯方位水下交叉和邻近运动多目标,所提算法的目标与航迹关联成功率高,抗干扰性能强,并且运算量小,实时性高,证明了所提算法的有效性。

关键词: 概率多假设跟踪, 纯方位, 多目标跟踪, 无味卡尔曼平滑算法