基于序贯贝叶斯滤波器的多目标跟踪方法研究

发布时间：2018-01-07 14:20

本文关键词：基于序贯贝叶斯滤波器的多目标跟踪方法研究　出处：《深圳大学》2017年硕士论文　论文类型：学位论文

【摘要】：多目标跟踪的主要目的是在目标运动模式具有不确定性、测量存在不确定性以及存在杂波等情况时,能够有效地检测出各个目标并估计出各目标的状态。传统的多目标跟踪方法通常使用数据关联技术,然而数据关联可能会出现“组合爆炸”和计算量呈指数增长等问题。针对这一问题,Mahler提出了基于随机有限集的概率假设密度(PHD),该滤波器不仅避免数据关联,而且解决了虚警、漏检和目标数未知情况下的多目标跟踪问题。虽然PHD滤波器在多目标跟踪过程中拥有许多的优势,但是该滤波器也存在着一些问题。首先,该滤波器很难将近距离的目标区分开来。其次,该滤波器对所收到的测量数据进行集中处理,如果数据处理不及时,则会导致后面数据处理延迟。最后,在低检测概率情况下容易造成目标信息丢失和目标数估计不稳定。针对上述的问题,我们提出了一种序贯多目标贝叶斯滤波器。另外,为了使该滤波器适用于多机动目标的跟踪,我们提出了一种跳变马尔可夫系统模型的序贯多目标贝叶斯滤波器。论文的主要内容如下:1)介绍了基于有限集统计学的多目标贝叶斯滤波理论,讨论了多目标跟踪模型,概述了最优多目标贝叶斯(Bayes)滤波器以及传递多目标联合后验分布一阶矩的PHD滤波器。最后介绍了传递目标边缘分布和存在概率的边缘分布贝叶斯(MDB)滤波器。2)研究并提出了一种序贯多目标贝叶斯滤波器,该滤波器传递目标的边缘分布和存在概率并序贯处理当前时刻收到的测量数据。同时,分别给出了适用于线性高斯系统和非线性高斯系统的序贯多目标贝叶斯滤波器实现方法。仿真实验结果表明,在存在杂波、漏检、目标数目未知的情况时,该滤波器具有更好的多目标跟踪能力。3)为了解决多机动目标的跟踪问题,我们将跳变马尔可夫系统模型引入到序贯多目标贝叶斯滤波器中,提出了带有跳变马尔可夫系统模型的序贯多目标贝叶斯滤波器,并且分别提出了该滤波器在线性高斯系统和非线性高斯系统的实现方法。仿真实验结果表明,在目标运动模式具有不确性、测量存在不确定性以及存在杂波等情况时,该滤波器能够对多机动目标进行有效、稳定和准确的跟踪。
[Abstract]:The main purpose of multi-target tracking is to measure the target motion pattern with uncertainty, measurement uncertainty and the existence of clutter and so on. It can effectively detect each target and estimate the state of each target. Traditional multi-target tracking methods usually use data association technology. However, data association may have some problems such as "combination explosion" and exponential increase of computational complexity. In order to solve this problem, Mahler proposed a probability assumption density (PHD) based on random finite set. The filter not only avoids data association, but also solves the problem of multi-target tracking in the case of false alarm, missed detection and unknown target number, although PHD filter has many advantages in the process of multi-target tracking. But there are some problems in the filter. Firstly, it is difficult to distinguish the short distance target. Secondly, the filter has a centralized processing of the received measurement data, if the data processing is not timely. Finally, in the case of low detection probability, it is easy to cause the loss of target information and the instability of target number estimation. We propose a sequential multi-target Bayesian filter, which can be used to track multiple maneuvering targets. We propose a sequential multiobjective Bayesian filter for a jump Markov system model. The main content of this paper is as follows: 1) the theory of multiobjective Bayesian filtering based on finite set statistics is introduced. The multi-target tracking model is discussed. The optimal multiobjective Bayesian Bayes is summarized. The filter and the PHD filter with first order moments of joint posteriori distribution are introduced. Finally, the edge distribution of the transfer target and the edge distribution of the existence probability of Bayesian MDBs filter. 2) are introduced. A sequential multiobjective Bayesian filter is proposed. The filter transmits the edge distribution and the probability of existence of the target and processes the measured data received at the current moment sequentially. At the same time. The realization methods of sequential multiobjective Bayesian filter for linear Gao Si system and nonlinear Gao Si system are presented respectively. The simulation results show that when there are clutter, missed detection and unknown number of targets. In order to solve the multi-maneuvering target tracking problem, we introduce the jump Markov system model into sequential multi-target Bayesian filter. A sequential multiobjective Bayesian filter with a jump Markov system model is proposed, and the realization methods of the filter in linear Gao Si system and nonlinear Gao Si system are presented respectively. The simulation results show that the proposed filter can be applied to the system of linear Gao Si and the nonlinear system of Gao Si. When the target motion mode is uncertain, the measurement uncertainty and clutter exist, the filter can track multiple maneuvering targets effectively, stably and accurately.
【学位授予单位】：深圳大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN713

【参考文献】