An IMM-BP based Algorithm for Tracking Maneuvering Underwater Targets by Multistatic Marine Robot Networks

Feng Zheng, Yu Tian, Weicong Zhan, Jiancheng Yu, Kaizhou Liu
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引用次数: 1

Abstract

A mobile and distributed multistatic active sonar network formed by a collection of autonomous marine robots is a significant type of tool for underwater surveillance applications such as antisubmarine warfare. Developing a tracker that fuses the sonar data collected by the robots to accurately estimate the locations and velocities of many underwater maneuvering targets is a crucial issue for realizing such a robotic surveillance network. This paper investigates this issue and an IMM-BP tracking strategy. The strategy adopts the interacting multiple model (IMM) method to cope with the problem of accurate tracking of highly maneuvering targets and employs a belief propagation (BP)-based tracking method to implement the Bayesian multisensor-multitarget tracking. And a new algorithm implementing the integration of the IMM method with the BP-based tracker is developed and presented in this paper. The main features of the algorithm include the extraction of measurement-target association information and its utilization in the model probability update step, and using a track score-based strategy and predicted measurements in the track management step to improve the performance of track confirmation and maintenance. The performance of the developed algorithm is validated with numerical simulations. The results show that the developed algorithm could accurately estimate the number and states of multiple maneuvering targets in environments with clutter and conditions of missed measurements.
基于IMM-BP的多静态海洋机器人网络水下机动目标跟踪算法
由自主海洋机器人组成的移动分布式多源声呐网络是一种重要的水下监视应用工具,如反潜战。开发一种跟踪器,融合机器人收集的声纳数据,以准确估计许多水下机动目标的位置和速度,是实现这种机器人监视网络的关键问题。本文对这一问题进行了研究,并提出了一种IMM-BP跟踪策略。该策略采用交互多模型(IMM)方法解决高机动目标的精确跟踪问题,采用基于信念传播(BP)的跟踪方法实现贝叶斯多传感器-多目标跟踪。在此基础上,提出了一种将基于bp的跟踪器与IMM方法相结合的新算法。该算法的主要特点是在模型概率更新步骤中提取测量目标关联信息并加以利用,在航迹管理步骤中采用基于航迹分数的策略和预测的测量值,提高航迹确认和维护的性能。通过数值仿真验证了该算法的性能。结果表明,该算法能在杂波环境和漏测条件下准确估计出多个机动目标的数量和状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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