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.     

Tracking in a cluttered environment with probabilistic data association

This paper presents a new approach to the problem of tracking when the source of the measurement data is uncertain. It is assumed that one object of interest (‘target’) is in track and a number of undesired returns are detected and resolved at a certain time in the neighbourhood of the predicted location of the target's return. A suboptimal estimation procedure that takes into account all the measurements that might have originated from the object in track but does not have growing memory and computational requirements is presented. The probability of each return (lying in a certain neighborhood of the predicted return, called ‘validation region’) being correct is obtained—this is called ‘probabilistic data association’ (PDA). The undesired returns are assumed uniformly and independently distributed. The estimation is done by using the PDA method with an appropriately modified tracking filter, called PDAF. Since the computational requirements of the PDAF are only slightly higher than those of the standard filter, the method can be useful for real-time systems. Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.     

An interacting multi-pattern probabilistic data association(IMP-PDA) algorithm for target tracking

A theoretical development of a novel approach for target tracking based on multiple patterns extracted from measurement sequences is presented in this paper. The introduction of patterns leads to a new paradigm for developing high performance algorithms. An interacting multi-pattern probabilistic data association (IMP-PDA) algorithm is developed, taking the advantage of clever formulation of the interacting multiple model approach. The IMP-PDA algorithm employs distance, directional and maneuver information for data association, which enhances significantly the capability of discriminating correct measurements from false measurements     

复杂环境下多无人机协作式地面移动目标跟踪

针对多无人机(UAV)协同地面移动目标跟踪问题展开研究.提出一种基于主动感知的问题求解框架,建立多UAV协同目标跟踪问题模型;在此基础上,采用分布式无色信息滤波实现目标状态融合估计与预测;然后,基于预测目标状态,结合滚动时域控制与遗传算法设计一种多UAV在线协同航迹规划算法.仿真结果表明:结合预测目标状态在线优化UAV...     

Hand tracking in a natural conversational environment by the interacting multiple model and probabilistic data association (IMM-PDA) algorithm

Traditional image based hand tracking algorithms use a single model Kalman filter to estimate and predict the hand state (position, velocity, and acceleration) and do not consider multiple measurements with noise and false alarms. However, these approaches may fail in the case of large maneuvers and/or a clutter measurement environment. In this paper, we apply the interacting multiple model (IMM) to catch hand maneuvers and the probabilistic data association (PDA) method to process noisy measurements and false alarms. A theoretical framework of image based hand tracking by the IMM-PDA algorithm is set up. Experiment results from several long video segments show that the IMM-PDA algorithm gives a superior performance compared to single model based Kalman filters.     

Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

In this paper,we present a trajectory generation method of a quadrotor,based on the optimal smoothing B-spline,for tracking a moving target with consideration o...     

目标跟踪中基于自适应模糊控制的数据融合方法研究

提出一种基于自适应模糊控制的数据融合方法。利用反向传播学习算法对其参数进行优化 ,并针对杂波环境中的单目标跟踪问题进行了仿真研究。仿真结果表明 ,该方法计算量小 ,能较好地处理不确定信息     

Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment

Learning of an autonomous mobile robot for path generation includes the use of previous experience to obtain the better path within its work space. When the robot is moving in its search space for target seeking, each task requires different form of learning. Therefore, the modeling of an efficient learning mechanism is the hardest problem for an autonomous mobile robot. To solve this problem, the present research work introduced an adaptive learning-based motion planner using artificial immune system, called adaptive immune-based path planner. Later the developed adaptive mechanism has been integrated to the innate immune-based path planner in order to obtain the better results. To verify the effectiveness of the proposed adaptive immune-based motion planner, simulation results as well as experimental results are presented in various unknown environments.     

S模式数据链自适应航迹跟踪滤波器设计

如何设计和实现适合空管系统使用的自适应滤波器成为了实现航迹滤波和目标跟踪的关键。首先对飞行器的三维空间运动轨迹建模"拉格朗日"(Lagrange)三阶级数展开,基于此设计了一种兼顾实时性和预测滤波效果的数据处理算法,通过对该滤波器模型稳定性、初始状态、收敛性、滤波参数等的深入分析,提出了一种基于"查表"策略的"残差-新息"估计方法,在保证收敛性的同时兼顾了收敛速度,一系列建模仿真和飞行试验均表明了所论述关键技术的有效性。     

Object tracking by exploiting adaptive region-wise linear subspace representations and adaptive templates in an iterative particle filter

Aiming at tracking visual objects under harsh conditions, such as partial occlusions, illumination changes, and appearance variations, this paper proposes an iterative particle filter incorporated with an adaptive region-wise linear subspace (RWLS) representation of objects. The iterative particle filter employs a coarse-to-fine scheme to decisively generate particles that convey better hypothetic estimates of tracking parameters. As a result, a higher tracking accuracy can be achieved by aggregating the good hypothetic estimates from particles. Accompanying with the iterative particle filter, the RWLS representation is a special design to tackle the partial occlusion problem which often causes tracking failure. Moreover, the RWLS representation is made adaptive by exploiting an efficient incremental updating mechanism. This incremental updating mechanism can adapt the RWLS to gradual changes in object appearances and illumination conditions. Additionally, we also propose the adaptive mechanism to continuously adjust the object templates so that the varying appearances of tracked objects can be well handled. Experimental results demonstrate that the proposed approach achieves better performance than other related prior arts.     

混响噪声下声源定位与跟踪的多特征自适应IMM粒子滤波算法EI北大核心CSCD

针对混响噪声下声源定位精度低和鲁棒性弱等问题,提出了多特征自适应IMM粒子滤波算法.该算法以麦克风接收信号的多特征作为观测信息,采用空时相关和迭代滤波建立了时延选择机制和波束输出能量优化机制,并在两者的基础上构建了似然函数以获得合理的声源位置信息.考虑到说话人运动的随机性,给出了自适应IMM算法,通过在线粒子集生成并将不同过程方差的模型进行交互来拟合说话人的不同运动模式,改善了说话人跟踪系统的稳健性.仿真和实测结果表明,所提算法利用了多特征定位信息的互补性,降低了观测误差不确定性对声源位置估计的影响,增强了随机运动声源跟踪系统的鲁棒性,提高了系统的定位精度.     

Lie群下利用改进JPDA滤波器的智能车立体视觉多目标跟踪方法

提出基于改进联合概率数据关联滤波器的智能车立体视觉多目标跟踪方法。利用立体视觉摄像头采集车辆及行人图像、视频;在Lie群下对传感器的不确定性进行建模,并采用欧几里德群算法对预处理的图像进行状态滤波;在可能存在车辆的区域内利用双目视觉去除误检,并获得车辆的位置信息;通过卡尔曼滤波器对测量的不确定度和预测目标运动的轨迹进行确认;运用改进的联合概率数据关联滤波器对车辆及行人的跟踪结果进行优化修正。实验结果表明,提出的方法可以有效解决智能车多目标跟踪问题,大幅度提升驾驶系统的自动化和智能化水平。相比其他较新的目标跟踪方法,提出的方法在跟踪精度和速度上具有明显的优势,且在跟踪车辆时不会产生明显的偏移、不会遗漏对行人的跟踪。     

A color-based particle filter for multiple object tracking in an outdoor environment

Tracking multiple objects is more challenging than tracking a single object. Some problems arise in multiple-object tracking that do not exist in single-object tracking, such as object occlusion, the appearance of a new object and the disappearance of an existing object, updating the occluded object, etc. In this article, we present an approach to handling multiple-object tracking in the presence of occlusions, background clutter, and changing appearance. The occlusion is handled by considering the predicted trajectories of the objects based on a dynamic model and likelihood measures. We also propose target-model-update conditions, ensuring the proper tracking of multiple objects. The proposed method is implemented in a probabilistic framework such as a particle filter in conjunction with a color feature. The particle filter has proven very successful for nonlinear and non-Gaussian estimation problems. It approximates a posterior probability density of the state, such as the object’s position, by using samples or particles, where each state is denoted as the hypothetical state of the tracked object and its weight. The observation likelihood of the objects is modeled based on a color histogram. The sample weight is measured based on the Bhattacharya coefficient, which measures the similarity between each sample’s histogram and a specified target model. The algorithm can successfully track multiple objects in the presence of occlusion and noise. Experimental results show the effectiveness of our method in tracking multiple objects.     

Object delineation in noisy images by a modified policy-iterationmethod

The contours of isolated objects in noisy images may be detected with a minimal cost contour detection algorithm. An algorithm that is based on the policy-iteration method for locating the closed minimal cost path is introduced. Computational results indicate that it is computationally more efficient than the dynamic programming approach. The method is applied to left ventricular contours in scintigraphic images, although it is applicable to any domain where a closed minimal cost path is to be computed in a matrix of cost coefficients     

Disseminating streaming data in a dynamic environment: an adaptive and cost-based approach

In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination trees. In this paper, we focus on the problem of constructing dissemination trees to minimize the average loss of fidelity of the system. We observe that existing heuristic-based approaches can only explore a limited solution space and hence may lead to sub-optimal solutions. On the contrary, we propose an adaptive and cost-based approach. Our cost model takes into account both the processing cost and the communication cost. Furthermore, as a distributed stream processing system is vulnerable to inaccurate statistics, runtime fluctuations of data characteristics, server workloads, and network conditions, we have designed our scheme to be adaptive to these situations: an operational dissemination tree may be incrementally transformed to a more cost-effective one. Our adaptive strategy employs distributed decisions made by the distributed servers independently based on localized statistics collected by each server at runtime. For a relatively static environment, we also propose two static tree construction algorithms relying on apriori system statistics. These static trees can also be used as initial trees in a dynamic environment. We apply our schemes to both single- and multi-object dissemination. Our extensive performance study shows that the adaptive mechanisms are effective in a dynamic context and the proposed static tree construction algorithms perform close to optimal in a static environment.     

Measurement correlation for multiple sensor tracking in a dense target environment

In this note, we describe an algorithm for correlating measurements from several sensors. This is a problem area in multiple sensor tracking in a dense target environment. It is shown that the correlation problem is similar to the assignment problem in operation research with assignment penalties being equal to the sufficient statistic of the generalized likelihood ratio test.     

A unified approach to tracking performance analysis of the selective partial update adaptive filter algorithms in nonstationary environment

In this paper, a unified approach to mean-square performance analysis of the family of selective partial update (SPU) adaptive filter algorithms in nonstationary environment is presented. Using this analysis, the tracking performance of Max normalized least mean squares (Max-NLMS), N-Max NLMS, the various types of SPU-NLMS algorithms, SPU transform domain LMS (SPU-TD-LMS), the family of SPU affine projection algorithms (SPU-APA), the family of selective regressor APA (SR-APA), the dynamic selection of APA (DS-APA), the family of SPU-SR-APA, the family of SPU-DS-APA, SPU subband adaptive filters (SPU-SAF), and the periodic, sequential, and stochastic partial update LMS, NLMS, and APA as well as classical adaptive filter algorithms can be analyzed with a unified approach. Two theoretical expressions are introduced to study the performance. The analysis is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. We demonstrate through simulations that the derived expressions are useful in predicting the performance of this family of adaptive filters in nonstationary environment.     

Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.