Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

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.

Modified joint probabilistic data association with classification-aided for multitarget tracking

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.

Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association

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.

Data association based on target signal classification information

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.

Novel Grouped Probability Data Association Algorithm for MIMO Detection

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.

VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

一种改进的精确最近邻PDA（IENNPDA）算法

近二十年来，人们提出了许多关于多目标跟踪的方法，其中精确最近邻ＰＤＡ（ＥＮ－ＮＰＤＡ）方法计算量小、性能较好、易于工程实现。但ＥＮＮＰＤＡ方法在密集多回波情况下性能不够理想，文章对此进行了探讨：将“Ｃｈｅａｐ”ＪＰＤＡ方法与ＥＮＮＰＤＡ方法及航迹分裂方法相结合，给出了一种改进的精确最近邻ＰＤＡ，即ＩＥＮＮＰＤＡ。仿真表明，ＩＥＮＮＰＤＡ的效果比ＥＮＮＰＤＡ有明显改善。

多传感器多目标跟踪的JPDA算法

传统的联合概率数据关联算法(JPDA)是在密集杂波环境下的一种良好的多目标跟踪算法,但它是针对单传感器对多目标跟踪的情况下使用,不能直接用于多传感器对多目标的跟踪。针对这一问题,文中提出了一种适用于多传感器多目标跟踪的JPDA算法,它以极大似然估计完成对来自多传感器的测量集合进行同源最优分划,然后采用JPDA方法对多目标进行跟踪。经过理论分析和仿真试验,证明了该方法能有效地进行多传感器多目标的跟踪,且具有算法简单、跟踪精度高、附加的计算量小等优点。

基于MPDA的波形自适应目标跟踪

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。

基于高斯粒子JPDA滤波的多目标跟踪算法

在多目标跟踪中,由于观测的不确定性带来数据关联问题,并且,多目标状态空间尺寸的增长带来了维数增大问题,该文提出了一种新的高斯粒子联合概率数据关联滤波算法(GP-JPDAF),在JPDA框架中引入高斯粒子滤波(GPF)的思想,通过高斯粒子而不是高斯量,来近似目标与观测的边缘关联概率,利用GPF计算目标状态的预测及更新分布。将其应用于被动多传感器多目标跟踪,仿真结果表明该算法比MC-JPDAF具有更好的跟踪性能。

ON THE EQUIVALENCE OF PDA ALGORITHM AND SIC-MMSE ALGORITHM

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) .

基于PDAF和线性预测的实时小目标跟踪算法

根据红外图像中小目标的典型特征提出了一种新的小目标检测算法。利用图像小目标的微分几何特性,计算图像的最小法向曲率,并以此为阈值,获得小目标的候选区对象,以实现目标检测。针对复杂背景下跟踪过程出现背景杂波干扰或目标受到遮挡时,出现目标消失的问题,提出了一种基于概率数据互联滤波器和线性预测技术相结合的实时跟踪算法,以提高目标跟踪的稳定性和精度。最后,利用实际录制的图像序列进行仿真实验,可准确跟踪信噪比不小于2、运动速度为1帧/像素的目标,验证了算法的有效性和实时性。

基于ML-PDA算法的低可见目标跟踪研究

针对利用传统算法难以跟踪低空目标的问题,提出了一种可行的跟踪低空目标的最大似然-概率数据关联(ML-PDA)算法。在分析各种低空目标特性的基础上,首先建立了基于ML-PDA滤波算法的低空目标跟踪模型,然后对该模型进行了深入分析,最后通过计算机仿真对该模型进行了验证。结果表明:ML-PDA滤波算法对低空目标跟踪十分有效,并且提高了滤波实时性,具有较好的工程应用前景。

一种基于PDA+LDPC迭代检测算法的协作通信策略

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。

一种改进的JIPDA多目标跟踪算法

针对边扫描边跟踪多频连续波雷达在杂波环境下的多目标跟踪问题,借助JIPDA算法中考虑目标存在概率的思想,并结合该体制雷达能提供高精度的径向速度信息,提出了一种引入径向速度量测的改进JIPDA滤波跟踪新方法。首先介绍了传统的JIPDA算法,然后对引入径向速度量测的改进JIPDA算法进行了理论分析,并讨论了引入径向速度量测和目标存在概率时互联概率的计算,最后进行了仿真实验。仿真结果表明,由于速度观测值提供了有关目标运动的更进一步信息,使得改进的JIPDA算法在跟踪性能上得到了显著提高。

基于PDA-UKF的杂波环境下单目标跟踪算法

提出将概率数据关联算法和不敏卡尔曼滤波相结合,用于杂波环境下的单目标跟踪。该算法同时解决了测量方程非线性情况下的滤波问题和杂波环境下的目标跟踪问题。给出了PDA-UKF算法的仿真过程,提供了一个基于距离、方位角、俯仰角的目标跟踪问题的仿真算例。理论分析与仿真结果均表明,该算法提高了滤波的稳定性和跟踪的精确性,具有较高的实用价值和广泛的应用前景。

基于声阵列的运动车辆航向估计算法

野外监控传感器网络通常会利用多个节点的信息来估计运动目标的航向。若单个传感器节点能够实现对运动目标航向的估计,将有效降低传感器网络的通信开销,并提高信息融合效率。在此背景下,文中提出一种适用于单体传感器节点的运动车辆目标航向角估计方法。该算法利用了节点内置的声阵列和电子罗盘数据,并针对野外环境中存在的风噪声和突发干扰进行了相应的处理策略设计。首先,对提出的航向估计算法理论进行了分析,将航向估计问题的主要研究内容细分为方位角估计问题和最邻近时刻估计问题;其次,对抗干扰方位角估计算法和最邻近时刻估计算法进行了介绍;最后为验证提出算法的效果,在采集的样本上进行了仿真实验,分别从风力大小和布设距离两个角度对实验结果进行分析,验证了所提算法对于野外环境下车辆目标航向角计算的有效性。

基于QMC-IPF-JPDA的多目标无源协同定位算法

针对杂波环境中多目标的检测跟踪问题,提出一种基于拟蒙特卡罗智能粒子滤波联合概率数据关联的无源协同定位算法。该算法建立双基站无源协同定位系统目标检测跟踪的数学模型。通过拟蒙特卡罗技术使粒子分布更加均匀,并对更新阶段的粒子进行交叉变异以提高粒子多样性。结合测量信息,利用联合概率数据关联算法实现多目标检测跟踪。仿真结果表明,所提算法能有效解决杂波环境下多目标检测跟踪问题,提高跟踪性能。

一种基于IMM/MSPDAF的多传感器数据融合目标跟踪算法

基于交互式多模型和多传感器联合概率数据关联算法的机动目标跟踪,先用融合算法将红外和雷达的量测进行融合,然后利用融合后的数据,采用交互式多模型机动目标跟踪方法实现对机动目标的跟踪。仿真实验验证了算法具有良好的机动目标跟踪效果。

掠海机动目标跟踪的IPDA算法

综合概率数据关联(IPDA)算法不仅能进行目标状态估计,还能提供对潜在目标的存在性/可感知性/可观测性概率的估计。这些估计通常作为评价航迹质量的参数,用于航迹确认和航迹终止。所研究的几种跟踪算法主要针对低空掠海飞行的机动目标,目标探测概率受海面信号反射导致的多路径效应和海杂波的影响而变化,将IPDA算法和EB-PDA、IMM PDA算法进行了比较研究,并提出了一种新的IPDA算法-VM-IPDA算法。仿真结果显示,VM-IPDA算法跟其他几种算法相比不仅提高准确性还能减少计算量。