An efficient track association algorithm for the multitarget tracking problem

A new and extremely promising approach is presented for the solution of the multitarget tracking problem for an important class of targets. For the class of targets that can be described as a linear, time-invariant system, it appears that this approach may be considerably more efficient, computationally, than existing procedures. A survey of approaches to the solution of the multitarget tracking problem has been presented in Ref. [2]. As indicated there, most algorithms require the use of the Kalman filter to provide state estimation for data association and track assignment. In this report, the need for state estimation to identify feasible tracks is eliminated. The use of simpler input-output models for track prediction results in a very substantial reduction in computational burden when compared with methods requiring state estimation.The utility of the method presented here depends, as do Kalman-filter-based methods, upon the definition of data windows that permit the identification of feasible tracks and the elimination of infeasible tracks in a timely manner and according to statistically meaningful criteria. After identifying feasible tracks, the Kalman filter can be utilized to obtain the required state estimates and to analyze and refine the information in the feasible track files.     

Speech Separation of Multiple Moving Speakers Using Multisensor Multitarget Techniques

The general problem addressed in this paper is that of separating the voices of active moving speakers in the presence of background noise and moderate reverberation level in the acoustic field using a single microphone array. We adapt the multisensor multitarget tracking theory to the context of microphone arrays in order to form receptive beams that lock on each moving speaker on an extended time basis and therefore, achieve voice separation. Our approach: 1) incorporates kinematical information of speakers' movement by using an interacting multiple model (IMM) estimator per speaker in order to constrain the evolution of direction of arrival (DOA) measurements, which characterize various motions of the speakers, and 2) can directly account for measurement origin uncertainty, i.e., which measurement comes from which speaker, by using the probabilistic-data-association technique in conjunction with the IMM estimator. The effectiveness of the approach is illustrated by an extensive simulation study on tracking the DOAs of two speakers with crossing trajectories and three static speakers having a conversation with partially overlapping speech and long pauses     

Toward multidimensional assignment data association in robot localization and mapping

It is well accepted that the data association or the correspondence problem is one of the toughest problems faced by any state estimation algorithm. Particularly in robotics, it is not very well addressed. This paper introduces a multidimensional assignment (MDA)-based data association algorithm for the simultaneous localization and map building (SLAM) problem in mobile robot navigation. The data association problem is cast in a general discrete optimization framework and the MDA formulation for multitarget tracking is extended for SLAM using sensor location uncertainty with the joint likelihood of measurements over multiple frames as the objective function. Methods for feature initialization and management are also integrated into the algorithm. When clutter is high and features are sparse, the compatibility information of features of a single measurement frame is not sufficient to make effective data-association decisions,thus compromising performance of single-frame-based methods. However, in a multiple-measurement-frame approach, the availability of more than one frame of measurement provides for more effective data-association decisions to be made, as consistency of measurements are looked at in several frames of measurement. Simulations are conducted to verify the performance gains over the conventional nearest neighbor (NN) data association algorithm and the joint compatibility branch and bound (JCBB) algorithm, especially in the presence of varying densities of spurious measurements and dynamic objects. Experimental results with ground truth are presented to demonstrate the practicality of the proposed data-association method in complex and large outdoor environments and its effectiveness over single-frame-based NN and JCBB schemes.     

一种模糊算法及其在多假设多目标跟踪中的应用

研究了基于模糊交互式多模型算法解决多假设多目标跟踪中的新目标数据相关问题。使用模糊交互式多模型算法处理新目标的数据相关问题,能够提高跟踪精度、时间效率。用隶属度表示每个模型与所跟踪新目标的隶属关系,根据隶属度选择最佳的模型对新目标进行跟踪。仿真结果表明,该方法不仅能够处理新目标的数据相关,同时又能够提高算法的时间效率。     

雷达多目标方位测量数据的一种处理方法

多目标数据关联是雷达数据融合过程中的重要一环。针对分布式雷达组网模式 ,在目标批数未知的条件下 ,提出了一种杂波环境下的多目标点迹关联算法。该算法利用雷达对目标的方位测量数据 ,通过引入关联矩阵 ,建立多目标多雷达数据关联数学模型 ,采用模拟退火技术 ,在使似然函数最大化的意义下求取关联矩阵和监视空域中目标的批数。根据多个雷达的多个采样周期的关联矩阵 ,建立各雷达测量数据之间的相关关系 ,为目标跟踪和数据融合提供前提条件。模拟仿真结果表明了算法的有效性。     

未知杂波环境下的多目标跟踪算法

提出了一种未知杂波环境下的多目标跟踪算法. 该算法通过有限混合模型(Finite mixtrue model, FMM)建立多目标似然函数, 其中混合模型参数可通过期望极大化(Expectation maximum, EM)算法及模型合并与删除技术得到. 由估计的混合模型参数可进一步得到杂波模型估计、目标个数估计以及多目标状态估计. 类似基于随机有限集(Random finite set, RFS)的多目标跟踪算法, 该算法也可避免目标与测量的关联过程. 仿真实验表明, 当杂波分布未知并且较复杂时, 本文算法的估计效果要明显优于未进行杂波拟合时的多目标跟踪算法.     

跟踪密集多回波环境下多机动目标的IMM-PC算法

跟踪密集多回波环境下的多机动目标不论是对数据关联方法,还是对滤波算法来说都是一个具有挑战性的问题,根据已有研究成果,采用交互多模型结合全局最优关联体制的方法是目前解决该问题的推荐方法。本文提出的交互多模型一投影收缩(IMM-PC)算法符合上述想法,采用当前优化算法前沿中简单有效的投影收缩算法,来解决全局最优关联问题,因此对跟踪密集多回波环境下的多机动目标是实用有效的,大量的仿真试验也验证了这一点。     

Multitarget miss distance via optimal assignment

The concept of miss distance-Euclidean, Mahalanobis, etc.-is a fundamental, far-reaching, and taken-for-granted element of the engineering theory and practice of single-target systems. In this paper we introduce a comprehensive L/sub p/-type theory of distance metrics for multitarget (and, more generally, multiobject) systems. We show that this theory extends, and provides a rigorous theoretical basis for, an intuitively appealing optimal-assignment approach proposed by Drummond for evaluating the performance of multitarget tracking algorithms. We describe tractable computational approaches for computing such metrics based on standard optimal assignment or convex optimization techniques. We describe the potentially far-reaching implications of these metrics for applications such as performance evaluation and sensor management. In the former case, we demonstrate the application of multitarget miss-distance metrics as measures of effectiveness (MoEs) for multitarget tracking algorithms.     

基于强跟踪滤波器的多目标跟踪方法

在诸多的多目标跟踪算法中，相互作用多模型（IMM）算法是目前公认的最为有效的算法。但到目前为止，LMM估计方法都是建立在卡尔曼滤波器（KF）和扩展卡尔曼滤波器（EKF）基础上，因而其性能不仅依赖于所采用的模型集，而且在更大程度上依赖于所采用的滤波技术。强跟踪滤波器（STF）克服了卡尔曼和扩展卡尔曼的三大缺陷，因而设计一种基于STF的IMM目标跟踪算法显然能提高其性能。仿真实验表明，基于STF的IMM算法的跟踪性能要优于基于KF和EKF的IMM算法的跟踪性能。     

多机动目标跟踪的IMM-GMPHD滤波算法

针对现有多机动目标跟踪算法精度低、计算量大、约束条件苛刻等问题,本文将高斯混合概率假设密度(Gaussian Mixture PHD,GM-PHD)滤波器和交互式多模型(Interacting Multiple Model,IMM)相结合,提出交互式多模型GM-PHD(Interacting Multiple Mode...     

MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements

In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC     

多传感器多目标跟踪的粒子PHD滤波算法

针对单传感器跟踪系统的缺陷,提出了基于粒子概率假设密度(PHD)滤波的多传感器多目标跟踪算法.这种算法不仅避免了多传感器多目标跟踪的数据关联问题,而且在漏检、目标密集、航迹交叉、小范围内目标数多的杂波环境下能够稳定、精确地估计目标状态和目标数.仿真实验比较了单传感器粒子PHD滤波与多传感器的粒子PHD滤波的跟踪性能,验证了该方法的跟踪性能和精度.     

Derivation and analytic evaluation of an equivalence relationclustering algorithm

Clustering algorithms have been recently used in multitarget multisensor tracking (MMT) problems in order to reduce the size of the data association problem. This paper derives an equivalence relation (ER) clustering algorithm used in a MMT problem and briefly compares it to other clustering schemes such as the nearest neighbor method. The main contribution of this work is the analytical evaluation of ER clustering performance, in the context of multitarget multisensor tracking, as a function of the distance between targets, measurement probability density function, and cluster parameter.     

双Markov多假设IMM机动目标跟踪算法

为提高交互式多模型(IMM)算法对机动目标的估计精度,需要增加其模型数量,但模型过多将导致计算量大并降低估计器性能。针对上述问题提出一种基于模型集的双马尔可夫多假设IMM机动目标跟踪算法。该算法用模型集间的马尔可夫转移阵描述模型集之间的大跳变,用模型的马尔可夫转移阵描述模型集内各模型间的小跳变或慢变,以达到细化建模、提高滤波精度的目的。     

A computational strategy for doubly smoothed MLE exemplified in the normal mixture model

A typical problem for the parameter estimation in normal mixture models is an unbounded likelihood and the presence of many spurious local maxima. To resolve this problem, we apply the doubly smoothed maximum likelihood estimator (DS-MLE) proposed by Seo and Lindsay (in preparation). We discuss the computational issues of the DS-MLE and propose a simulation-based DS-MLE using Monte Carlo methods as a general computational tool. Simulation results show that the DS-MLE is virtually consistent for any bandwidth choice. Moreover, the parameter estimates in the DS-MLE are quite robust to the choice of bandwidths, as the theory indicates. A new method for the bandwidth selection is also proposed.     

Event-averaged maximum likelihood estimation and mean-field theoryin multitarget tracking

This paper presents a novel type of Kalman filter for track maintenance in multitarget tracking using thresholded sensor data at high target/clutter densities and low detection levels. The filter is robust against tracking errors induced by crossing tracks, clutter, and missed detections, and the computational complexity of the filter scales well with problem size. There are two key features that differentiate this approach from earlier work. First, to reduce computational load, the filter exploits techniques from statistical field theory to simplify measurement to track association by using a mean-field approximation to sum over associations. Second, to enhance tracking of close together targets, the filter explicitly models the error correlations that occur between such target pairs. These error correlations are caused by measurement to track association ambiguities that arise when target separations are comparable to sensor measurement errors     

Low-resolution color-based visual tracking with state-space model identification

A novel tracking method is proposed to resolve the poor performance of color-based tracker in low-resolution vision. The proposed method integrates vector autoregression (VAR) with a conceptual frame of state-space model (SSM) to achieve an appropriate model that clearly describes the relation between high-resolution tracking results (states) and corresponding low-resolution tracking results (observations). Here, the parameters of SSM are calculated by the maximum likelihood (ML) estimator to optimize the SSM and minimize its model error. By using the Kalman filter, known as an effective filter of SSM, to estimate the states of the tracked object from its incomplete observations, it is observed that the estimated states are closer to their actual values than their observations or estimates by other unoptimized SSMs. Therefore, the proposed method can be used to improve low-resolution tracking results. Moreover, it can decrease computational complexity and save on processing time.     

基于最优线性无偏估计的交互式多模型机动目标跟踪

为了解决雷达目标跟踪的非线性估计问题,提出了一种基于最优线性无偏估计的交互式多模型(IMM)机动目标跟踪算法.该算法采用最优线性无偏估计(BLUE),把目标的状态在笛卡尔坐标来表示,而把雷达测量误差保留在极坐标下,并结合交互式多模型算法,实现对机动目标的有效跟踪.仿真实验验证了该算法的准确性和有效性.     

Manoeuvring multitarget tracking method in cluttered environment

A method for tracking a manoeuvring multitarget in a cluttered environment is presented. The clutter or false alarms are assumed to occur uniformly and to be independently distributed. The algorithm is performed by taking a sliding window of length uT (T is the sampling time) at time K. Instead of solving a large problem, the entire set of targets and measurements is divided into clusters so that a number of smaller problems are solved independently. When a set of measurements is received, we form a new data-association hypothesis for the set of measurements lying in the validation gales; with each cluster from time K — u + 1 to K the probability of each track history is computed, and ihen by choosing the largest of these histories we perform the target measurement updated with the adaptive state esiimator. Meanwhile, the covariance-matching technique is adopted so that the accuracy of the adaptive state estimator will be improved. Simulation has shown the effectiveness of the tracking algorithm.     

基于协同关联粒子滤波算法的交互多视频目标跟踪

首先介绍了带马尔科夫跳变非线性系统(JMNSs)的状态估计问题,然后总结了JMNSs最优状态估计的难点和具有交互作用的多目标跟踪问题.在总结分析各类不同算法的基础上,提出了一种协同关联粒子滤波算法来解决目标数目在变化的交互多目标跟踪问题,改进后的算法不需要观测与目标状态关联和目标数量已知的假设.最后,通过仿真实验验证了改进后的算法在跟踪效果上优于现有算法,并能成功估计目标的数量.