Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabili...Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed.Firstly,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.展开更多
Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are...Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.展开更多
Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilis...Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.展开更多
In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too...In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.展开更多
To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-...To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.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
相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种...相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种基于修正概率数据关联(modified probabilistic data association,MPDA)的波形自适应选择目标跟踪算法。采用MPDA算法建立和更新杂波下目标的航迹,利用修正的Riccati方程估计下一时刻的滤波协方差矩阵,并推导了相应的波形优化准则函数。仿真表明,该算法降低了密集杂波条件下的滤波误差,相比于未采用波形优化的PDA和MPDA算法,显著提高了跟踪性能。展开更多
本论文研究了基于同步DS-CDMA协作通信系统的迭代接收机性能,该系统在协作伙伴节点端和基站端均使用由随机数据联合检测器与LDPC译码器级联(PDA+LDPC,probabilistic data association+low density parity check codes)所组成的迭代接收...本论文研究了基于同步DS-CDMA协作通信系统的迭代接收机性能,该系统在协作伙伴节点端和基站端均使用由随机数据联合检测器与LDPC译码器级联(PDA+LDPC,probabilistic data association+low density parity check codes)所组成的迭代接收机,利用从该迭代接收机所提供的信息,提出了一种集中式协作伙伴节点选择策略。根据该策略,由于距离基站较近的节点与基站间通信能力相对较强,每个信源节点将优先选择距离基站近的节点作为其协作伙伴节点,仿真结果表明,本文所提出的集中式协作伙伴节点选择策略可取得较好的系统性能。展开更多
基金Supported by the National Nature Science Foundation of China(No.61300214)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+5 种基金the National Natural Science Foundation of Henan Province(No.132300410148)the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Postdoctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher of Henan Province Universities(No.2013GGJS-026)the Postdoctoral Fund of Henan Province(No.2013029)the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)
文摘Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed.Firstly,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.
基金Defense Advanced Research Project "the Techniques of Information Integrated Processing and Fusion" in the Eleventh Five-Year Plan (513060302).
文摘Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.
文摘Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.
基金the Youth Science and Technology Foundection of University of Electronic Science andTechnology of China (JX0622).
文摘In most of the passive tracking systems, only the target kinematical information is used in the measurement-to-track association, which results in error tracking in a multitarget environment, where the targets are too close to each other. To enhance the tracking accuracy, the target signal classification information （TSCI） should be used to improve the data association. The TSCI is integrated in the data association process using the JPDA （joint probabilistic data association）. The use of the TSCI in the data association can improve discrimination by yielding a purer track and preserving continuity. To verify the validity of the application of TSCI, two simulation experiments are done on an air target-tracing problem, that is, one using the TSCI and the other not using the TSCI. The final comparison shows that the use of the TSCI can effectively improve tracking accuracy.
基金Sponsored by the National Natural Science Foundation of China(60572120)
文摘To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.
文摘相比于传统雷达系统,感知雷达能够通过对复杂多变的电磁环境感知自适应调整雷达发射波形,以适应当前的环境,实现推理和判决的优化,从而获得系统性能的大幅提升。从感知雷达的思想出发,研究了杂波环境下的波形自适应选择问题,提出了一种基于修正概率数据关联(modified probabilistic data association,MPDA)的波形自适应选择目标跟踪算法。采用MPDA算法建立和更新杂波下目标的航迹,利用修正的Riccati方程估计下一时刻的滤波协方差矩阵,并推导了相应的波形优化准则函数。仿真表明,该算法降低了密集杂波条件下的滤波误差,相比于未采用波形优化的PDA和MPDA算法,显著提高了跟踪性能。
文摘本论文研究了基于同步DS-CDMA协作通信系统的迭代接收机性能,该系统在协作伙伴节点端和基站端均使用由随机数据联合检测器与LDPC译码器级联(PDA+LDPC,probabilistic data association+low density parity check codes)所组成的迭代接收机,利用从该迭代接收机所提供的信息,提出了一种集中式协作伙伴节点选择策略。根据该策略,由于距离基站较近的节点与基站间通信能力相对较强,每个信源节点将优先选择距离基站近的节点作为其协作伙伴节点,仿真结果表明,本文所提出的集中式协作伙伴节点选择策略可取得较好的系统性能。
