In order to evaluate the health status of pigs in time,monitor accurately the disease dynamics of live pigs,and reduce the morbidity and mortality of pigs in the existing large-scale farming model,pig detection and tr...In order to evaluate the health status of pigs in time,monitor accurately the disease dynamics of live pigs,and reduce the morbidity and mortality of pigs in the existing large-scale farming model,pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs.However,it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets.In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig,this study proposed a method that used color feature,target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm,which based on joint probability data association and particle filter.Experimental results show the proposed algorithm can quickly and accurately track pigs in the video,and it is able to cope with partial occlusions and recover the tracks after temporary loss.展开更多
The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to de...The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.展开更多
A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation ...A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.展开更多
Joint Probabilistic Data Association (JPDA) is a very fine optimal multitarget tracking and association algorithm in clutter. However, the calculation explosion effect in computation of association probabilities has b...Joint Probabilistic Data Association (JPDA) is a very fine optimal multitarget tracking and association algorithm in clutter. However, the calculation explosion effect in computation of association probabilities has been a difficulty. This paper will discuss a method based on layered searching construction of association hypothesis events. According to the method, the searching schedule of the association events between two layers can be recursive and with independence, so it can also be implemented in parallel structure. Comparative analysis of the method with relative methods in other references and corresponding computer simulation tests and results are also given in the paper.展开更多
A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood d...A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.展开更多
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system....Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.展开更多
A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The int...A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The interconnection probability of the two targets is calculated,the weighted value is processed and the target tracks are obtained.The simulation results show that JPDA algorithm achieves higher tracking accuracy and provides a basis for more targets tracking.展开更多
In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrins...In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrinsic messages,it is concluded that the Probabilistic Data Association(PDA) algorithm is equivalent to the Soft Interference Cancellation plus Minimum Mean Square Error algo-rithm(SIC-MMSE) .展开更多
Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is ...Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode p...An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.展开更多
针对联合概率数据关联(JPDA,Joint Probabilistic Data Association)算法关联概率计算过于复杂,无法适应复杂电磁环境下多目标实时跟踪的需求,提出了一种改进的JPDA算法(MJPDA)。首先,考虑多重因素重新定义关联矩阵,并计算关联概率;其次...针对联合概率数据关联(JPDA,Joint Probabilistic Data Association)算法关联概率计算过于复杂,无法适应复杂电磁环境下多目标实时跟踪的需求,提出了一种改进的JPDA算法(MJPDA)。首先,考虑多重因素重新定义关联矩阵,并计算关联概率;其次,对密集杂波下公共量测的关联概率进行修正,引入马氏距离对公共量测进行二次加权,同时考虑公共与非公共量测数目的影响,最后计算修正关联概率。该算法规避了确认矩阵的拆分,有效解决了JPDA算法计算量随杂波密度增加呈指数级增长的问题。通过理论分析和蒙特卡罗仿真实验结果表明,在密集杂波环境下,改进算法具有良好的跟踪性能和较小的计算量,显著提升了算法的实时性。展开更多
中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,...中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,因此提出一种基于动力学守恒定律的弹道目标概率数据关联(probability data association,PDA)方法,即在传统关联门筛选出有效量测的基础上,对动量矩和机械能进行联合统计检验,进一步剔除电假目标点迹或其他错误量测,并使用动量矩和机械能对加权关联概率进行修正。蒙特卡罗仿真验证了该方法的有效性。仿真结果表明,与传统PDA方法相比,所提方法能够有效抑制有源距离欺骗干扰和杂波的影响,提高跟踪精度。展开更多
针对概率数据互联(Probability data association, PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近...针对概率数据互联(Probability data association, PDA)算法在杂波环境下计算复杂度高的问题,设计了一种基于PDA算法的数据关联方法,当波门内量测点数量大于阈值时,采用PDA算法更新目标状态;当波门内量测点数量小于等于阈值时,采用最近邻思想筛选目标量测点,接着利用卡尔曼滤波(Kalman filter, KF)算法实现杂波环境下的快速滤波更新。在此基础上,通过自适应区间平滑方法,动态修正平滑区间,实现整体状态估计的反向平滑,从而提升算法的精度。不同杂波环境下的实验结果表明,本文方法相较于PDA算法与KF-PDA算法,在保证跟踪效率的同时,有效提升了系统状态的估计精度,验证了该方法的鲁棒性和有效性。展开更多
基金This work was supported by the National High Technology Research and Development Program(863 Plan)(Grant No.2013AA102306).
文摘In order to evaluate the health status of pigs in time,monitor accurately the disease dynamics of live pigs,and reduce the morbidity and mortality of pigs in the existing large-scale farming model,pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs.However,it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets.In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig,this study proposed a method that used color feature,target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm,which based on joint probability data association and particle filter.Experimental results show the proposed algorithm can quickly and accurately track pigs in the video,and it is able to cope with partial occlusions and recover the tracks after temporary loss.
文摘The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.
文摘A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.
基金Supported by Defense Advanced Research Fund of China
文摘Joint Probabilistic Data Association (JPDA) is a very fine optimal multitarget tracking and association algorithm in clutter. However, the calculation explosion effect in computation of association probabilities has been a difficulty. This paper will discuss a method based on layered searching construction of association hypothesis events. According to the method, the searching schedule of the association events between two layers can be recursive and with independence, so it can also be implemented in parallel structure. Comparative analysis of the method with relative methods in other references and corresponding computer simulation tests and results are also given in the paper.
基金This project was supported by the National Natural Science Foundation of China (60272024).
文摘A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest （HF） algorithm and Hungarian tree （HT） algorithm, and their combination with the naYve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.
基金Supported by the National Natural Science Foundation of China(11078001)
文摘Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.
文摘A tracking algorithm for multiple-maneuvering targets based on joint probabilistic data association（JPDA）is proposed to improve the accuracy for tracking algorithm of traditional multiple maneuvering targets.The interconnection probability of the two targets is calculated,the weighted value is processed and the target tracks are obtained.The simulation results show that JPDA algorithm achieves higher tracking accuracy and provides a basis for more targets tracking.
文摘In this letter,by employing Gaussian distribution to approximate the probability density function(pdf) of the extrinsic information at the output of the multiuser detector as a function of the pdf of the input extrinsic messages,it is concluded that the Probabilistic Data Association(PDA) algorithm is equivalent to the Soft Interference Cancellation plus Minimum Mean Square Error algo-rithm(SIC-MMSE) .
基金the National Natural Science Foundation of China(61771367)the Science and Technology on Communication Networks Laboratory(HHS19641X003).
文摘Since the joint probabilistic data association(JPDA)algorithm results in calculation explosion with the increasing number of targets,a multi-target tracking algorithm based on Gaussian mixture model(GMM)clustering is proposed.The algorithm is used to cluster the measurements,and the association matrix between measurements and tracks is constructed by the posterior probability.Compared with the traditional data association algorithm,this algorithm has better tracking performance and less computational complexity.Simulation results demonstrate the effectiveness of the proposed algorithm.
基金National Natural Science Foundation of China (60975028)National High-tech Research and Development Program (2009AA112203)+1 种基金Fundamental Research Funds for the Central Universities (CHD2009JC037)Natural Science Basic Research Plan in Shaanxi Province (2006F12)
文摘An improved particle filtering（IPF） is presented to perform maneuvering target tracking in dense clutter.The proposed filter uses several efficient variance reduction methods to combat particle degeneracy,low mode prior probabilities and measure-ment-origin uncertainty.Within the framework of a hybrid state estimation,each particle samples a discrete mode from its poste-rior distribution and the continuous state variables are approximated by a multivariate Gaussian mixture that is updated by an unscented Kalman filtering（UKF）.The uncertainty of measurement origin is solved by Monte Carlo probabilistic data associa-tion method where the distribution of interest is approximated by particle filtering and UKF.Correct data association and precise behavior mode detection are successfully achieved by the proposed method in the environment with heavy clutter and very low mode prior probability.The performance of the proposed filter is examined and compared by Monte Carlo simulation over typical target scenario for various clutter densities.The simulation results show the effectiveness of the proposed filter.
文摘针对联合概率数据关联(JPDA,Joint Probabilistic Data Association)算法关联概率计算过于复杂,无法适应复杂电磁环境下多目标实时跟踪的需求,提出了一种改进的JPDA算法(MJPDA)。首先,考虑多重因素重新定义关联矩阵,并计算关联概率;其次,对密集杂波下公共量测的关联概率进行修正,引入马氏距离对公共量测进行二次加权,同时考虑公共与非公共量测数目的影响,最后计算修正关联概率。该算法规避了确认矩阵的拆分,有效解决了JPDA算法计算量随杂波密度增加呈指数级增长的问题。通过理论分析和蒙特卡罗仿真实验结果表明,在密集杂波环境下,改进算法具有良好的跟踪性能和较小的计算量,显著提升了算法的实时性。
文摘中段伴飞突防造成的各种有源或无源的弹道群目标会给雷达跟踪系统带来极大的挑战,导致其跟踪非本体实体目标或电假目标,从而出现关联错误的情况。中段实体弹道目标满足动力学守恒定律,可以充分利用该特性来改善跟踪系统的数据关联机制,因此提出一种基于动力学守恒定律的弹道目标概率数据关联(probability data association,PDA)方法,即在传统关联门筛选出有效量测的基础上,对动量矩和机械能进行联合统计检验,进一步剔除电假目标点迹或其他错误量测,并使用动量矩和机械能对加权关联概率进行修正。蒙特卡罗仿真验证了该方法的有效性。仿真结果表明,与传统PDA方法相比,所提方法能够有效抑制有源距离欺骗干扰和杂波的影响,提高跟踪精度。
