【作者】 徐亮；

【导师】 周共健；

【作者基本信息】 哈尔滨工业大学 ， 信息与通信工程， 2015， 硕士

【摘要】 在多目标跟踪过程中,在对传感器探测到的量测数据进行滤波处理后,实时的发现新目标并对新目标和已有目标的运动状态进行估计。在多目标跟踪过程中可能会遇到各种复杂的环境,一些问题无法应用现有的方法进行解决。在杂波环境下进行密集目标跟踪的情况就是其中之一。在目标密集的情况下,目标量测的分布也非常密集,而位置相近的目标量测将会给目标跟踪带来很大的问题,如航迹合并和杂波密度估计偏差。相邻的航迹很容易受到其余航迹目标量测的吸引,在航迹保持近距离平行或小角度交叉的时候,目标跟踪可能出现航迹合并的现象,而且密集目标所产生的目标量测也可能使得数据关联过程中,在对航迹进行杂波估计的时候限定范围(如波门区域)中其余目标的量测被视为杂波,从而使得杂波密度的估计值过高,而过高的杂波密度估计值可能给跟踪带来困难。本文针对密集目标跟踪的这两个问题进行研究,去除位置相近的目标量测对跟踪产生的不利影响,从数据关联的角度改善航迹合并的现象,随后又对杂波密度估计方法进行研究,提出不受目标量测影响的无偏杂波密度估计算法。论文主要工作如下:(1)防止密集目标跟踪下产生的航迹合并。在目标比较密集的情况下,目标的彼此临近会给跟踪带来很大的复杂性。在目标间距较近或者小角度交叉的时候,引起来自目标的量测很容易落入公共区域,相邻的航迹都会使用这个量测来进行更新,而在航迹的波门长时间保持重叠的情况下就会容易导致航迹的趋于临近与合并。本文研究了三种防止航迹合并的方法——ENNPDA,ENNJPDA,SJPDA。三种算法防止航迹合并的基本方法是:1增大目标量测与自身航迹相关联的程度;2减小其它航迹的目标量测对航迹的影响。并且从防止航迹合并的有效性和对平常跟踪的影响两个方面对三种算法的优缺点进行了分析。实验表明,三种方法中的ENNJPDA对于防止合并非常有效,同时又不会对正常的跟踪产生影响。(2)杂波密度估计方法。通常使用概率方法来区分杂波和目标探测,而杂波密度是概率的重要参数之一。早期的目标跟踪算法都是基于数据关联,而很少对杂波和系统的噪声进行估计。一个准确的杂波密度估计结果可以为目标跟踪提供很大的用处。如果杂波密度估计结果高于真实值,将会导致在确认航迹和分离目标时的困难,而过低的杂波密度估计结果可能提高假航迹的数量。在密集目标跟踪的情况下,密集的目标量测将会大大的提高杂波密度的估计值。而已有的杂波估计方法大部分并未考虑目标量测的影响,因此,本文在研究了均匀假设估计与空间稀疏性估计方法后,在空间稀疏性估计方法的基础上提出了一种新方法,在进行杂波估计的时候去除了目标量测的影响,以得到杂波密度的无偏估计结果。实验表明,修正算法对于邻近目标的跟踪性能有着明显的改善。更多还原

【Abstract】 The measurement data which is detected by sensor is handled in multi-target tracking, we filter and optimize the data to find the target, and estimate its motion state. We may encounter all kinds of complicated environment in the process of multi-target tracking, and some problems cannot be solved by existing methods. The dense targets tracking in clutter is one of the problems. In the situation of dense target tracking, the track maybe easily attracted by measurement from other target, it will cause track coalescence phenomenon in parallel neighboring or small-angle crossing scene. In the process of data association, we estimate the clutter density in a limited region(for example, the validation gate) and the measurement from other target would be seen as a clutter, the measurement cause by dense target may increase the estimated value of clutter density, and the high clutter density estimation value can bring difficulty to the tracking. In this paper, we study the two problems in dense target tracking, and improve the performance of avoiding track coalescence in data association, then we estimate the clutter density online, and proposed a new method which is not affected by the target measurement. The main work in this paper is as follows:(1)Avoid track coalescence. In the situation of dense target tracking, the closely spaced target may bring great complexities to the target tracking. When the targets keep parallel neighboring or small-angle crossing, measurement originated from targets can easily fall into the overlapping region of the validation gate, and the neighbor targets all update with this measurement, this may lead to that one track is attracted by the other, and the tracks stay close to each other, and finally coalescence. We study three methods to avoid track coalescence: Exact Nearest-Neighbor Probability Data Association(ENNPDA); Exact Nearest-Neighbor Joint Probability Data Association(ENNJPDA); Scaled Joint Probability Data Association(SJPDA). The basic method to avoid track coalescence of these algorithms is: 1 increase the correlation intensity of the target measurement and its own track; 2 decease the correlation intensity of the target measurement and other track. We analysis the efficiency of avoid track coalescence and the effect to normal track of these three methods. The simulation shows, ENNJPDA has the best performance in tracking closely-spaced targets, and would not affect the normal track.(2)Clutter density estimation method. The normal method to distinguish the clutter and the target measurement is the probability method, and clutter density estimation is one of the important parameters of probability. The early target tracking algorithm is based on data association, but rarely estimate the clutter and the noise of system. An accurate estimationresult of clutter measurement density can provide a significant benefit in target tracking. If we estimate the clutter density higher than the real value, which may lead to the difficulty in confirm the track and separate the target, and the lower clutter density would result in the increasing number of false tracks. In dense target tracking, the closely-spaced target measurements would increase the estimate value of clutter density greatly, however, most of the existing clutter estimate methods have no consideration about the target measurement. In this paper, we study about the uniform assumption estimation and spatial sparsity estimation method, and propose a new unbiased algorithm to handle the closely-spaced target tracking. In the new method we subtract the probability of target existence from the number of measurement we used for clutter density estimation in order to get the unbiased estimation of clutter intensity. The new method avoid the higher estimate of clutter density in closely-spaced target tracking, and get the clutter density which is approach to the real value. The simulation show the better performance of new method than the sparsity estimator.更多还原