改进的概率假设密度滤波多目标检测前跟踪算法

基于概率假设密度滤波(Probability Hypothesis Density,PHD)的检测前跟踪(Track before detect,TBD)技术可以有效解决未知目标数的弱小点目标检测前跟踪问题.文章针对现有PHD-TBD算法存在目标数估计不准、目标发现延时较久的问题进行研究.从标准PHD滤波出发,更为合理地推导出PHD-TBD算法的粒子权重更新计算表达式,实现对目标数的准确估计；同时利用贝叶斯滤波理论,推导出基于量测的新生粒子概率密度采样函数,完成对目标的快速发现.仿真实验表明,与现有的PHD-TBD相比,改进算法能够适应目标扩散情况,准确估计目标数目,并实现对目标的快速发现和位置准确估计.  

多元假设检验GMPHD轨迹跟踪

由于在军事和民事领域逐步广泛的应用，数目不定的多目标跟踪技术正受到越来越多的关注。概率假设密度(PHD)滤波方法，特别是具有闭式递归的高斯混合概率假设密度(GMPHD)技术，在噪声和漏警等影响下仍能形成优越的群目标跟踪性能。然而PHD滤波器并不能实现多目标航迹跟踪，而其与传统数据互联的结合，复杂度高且跟踪效果不尽如人意。在该文中，各目标的航迹信息以假设形式表述，数据互联则是通过使用经典的多元假设检测方法判决假设矩阵实现。其与GMPHD的结合不仅实现了数据互联和轨迹管理，还因为积累时间信息大大降低了杂波干扰的影响。实验结果证明，该算法可以对多个目标所形成的轨迹实施正确跟踪，同时，计算量的大幅度降低带来了跟踪系统可实现性的提高。  

一种改进的CPHD多目标跟踪算法

CPHD(Cardinalized Probability Hypothesis Density)滤波是一种杂波环境下可变目标数的多目标跟踪算法,该文针对算法中存在的目标漏检问题提出一种改进算法,该算法在高斯混合框架下实现贝叶斯递归,通过对各个高斯分量进行标记,对目标进行航迹关联,在此基础上对修剪合并后各个高斯分量的权值进行两次分配.首先对超过检测门限的高斯分量权值进行分配,有效解决了目标漏检问题,然后基于一个目标只可能产生一个观测的事实进行第2次分配,改善了目标发生交叉时的算法性能.实验结果表明,所提方法在多目标状态估计和航迹维持方面均优于普通的CPHD算法.  

基于有限集统计学理论的目标跟踪技术研究综述

有限集统计学理论为杂波背景下的目标跟踪问题提供了一种工程友好的理论工具.对近年来基于有限集统计学理论的目标跟踪技术研究现状进行了综述,包括最优多目标贝叶斯滤波器及其近似技术、参数未知与机动多目标跟踪技术、航迹生成方法、单目标联合检测与跟踪滤波器及基于有限集观测的单目标滤波器等,对相关应用亦有所介绍.最后在已有研究发展的基础上,着眼于提高目标跟踪精度和增强目标跟踪鲁棒性的发展需要,提出了基于有限集统计学理论的目标跟踪技术需重点解决和关注的若干问题,包括多目标跟踪性能评价、弱小目标跟踪、多机动目标跟踪、多传感器融合跟踪以及联合目标检测、跟踪与分类等方面.  

基于随机集的RBPF多目标关联跟踪算法

针对大量杂波环境下数量变化的纯角度多目标航迹关联跟踪问题,提出一种新的基于Rao-Blackwellized粒子采样(RBPF)航迹关联的高斯混合概率假设密度(GMPHD)滤波算法.算法首先利用GMPHD在每时刻对多个目标组成的随机集合进行估计;然后利用基于随机有限集的RBPF对GMPHD所得到的目标集合进行检测和关联,有效解决GMPHD算法中无法进行多目标航迹识别的弊端;最后通过对所有粒子的融合完成航迹区分和估计.实验结果表明,提出方法比起目前经典的随机集Label-PHD关联跟踪算法,可以更有效的对数量未知的多目标航迹进行区分和关联估计,同时算法的跟踪性能及稳定性要好于Label-PHD算法.  

基于势概率假设密度滤波的检测前跟踪新算法

基于势概率假设密度滤波(Cardinalized Probability Hypothesis Density, CPHD)检测前跟踪(Track before detect, TBD)算法能有效解决未知目标数的弱小目标检测跟踪.文章深入研究了CPHD算法, 从标准CPHD滤波的粒子权重更新出发, 结合检测前跟踪的实际, 合理地推导出CPHD-TBD算法的粒子权重更新表达式; 分析了CPHD滤波目标势分布的物理意义, 实现了目标势分布更新计算在检测前跟踪的应用.将CPHD滤波和TBD进行有效结合, 提出了基于势概率假设密度滤波的检测前跟踪算法, 并给出其详细实现步骤.仿真实验证明提出的CPHD-TBD算法与现有概率假设密度检测前跟踪(PHD-TBD)算法相比, 能更详细地传递目标分布信息, 从本质上改变了PHD-TBD对目标数估计的方式, 能更准确稳定估计目标数, 实现了对目标的发现和状态准确估计, 性能明显更优.  

Extensions of the SMC-PHD filters for jump Markov systems

The probability hypothesis density (PHD) filter is a promising algorithm for multitarget tracking, which can be extended for jump Markov systems (JMS). Since the existing multiple model sequential Monte Carlo PHD (MM SMC-PHD) filter is not interacting, two extensions of the SMC-PHD filters are developed in this paper. The interacting multiple-model (IMM) SMC-PHD filter approximates the model conditional PHD of target states by particles, and performs the interaction by resampling without any a priori assumption of the noise. The IMM Rao-Blackwellized particle (RBP) PHD filter uses the idea of Rao-Blackwellized to further enhance the performance of target state estimation for JMS with mixed linear/nonlinear state space models. The simulation results show that the proposed algorithms have better performances than the existing MM SMC-PHD filter in terms of state filtering and target number estimation.  

Localization of multiple emitters based on the sequential PHD filter

The localization of multiple emitters from passive angle measurements is a widely investigated problem. Traditionally, the central problem of state estimation for multiple targets by multiple passive sensors is data association. Mathematically, the formulation of the data association problem leads to a generalization of an S-dimensional (S-D) assignment problem. Unfortunately, the complexity of solving an S-D assignment problem for S≥3 is NP hard. A practical solution is to solve the multidimensional assignment problem using multistage Lagrangian relaxation. However, the computational requirements of it explode with the number of sensors. Additionally, it cannot give satisfactory results in dense clutter environment. In this paper, the sequential probability hypothesis density (PHD) filter using passive sensors in two different manners for localization of multiple emitters is introduced. Simulation results show that the sequential PHD filter can achieve better performance with smaller computational complexity than the method based on S-D assignment programming in dense clutter environment.  

PHD粒子滤波中目标状态提取方法研究

 采用概率假设密度(PHD)粒子滤波进行多目标跟踪时，各时刻的目标状态表现为大量的加权粒子，需以一定方法从该粒子近似中提取出来。该文提出一种增强的目标状态提取方法，先以k-means算法对粒子进行空间分布的聚类，再于各类中寻找粒子权的峰值位置作为目标状态的估计。仿真结果表明：由于综合利用了粒子的权值和空间分布信息，该算法具有比现有算法更小的目标状态估计误差。  

A new data association algorithm using probability hypothesis density filter

Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise, clutter and misdetection. For linear Gaussian Mixture (GM) system, PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are computationally expensive. In this paper, we proposed a new data association algorithm using GMPHD filter, which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.