一种混合的多机动目标跟踪算法研究

提出了一种混合的多机动目标跟踪算法:交互多模型模糊联合概率数据关联算法(IMM-FJPDA),该算法将交互多模型算法(IMM)和模糊联合概率数据关联算法(FJPDA)相结合,它克服了IMM-JPDA算法计算量大和IMM-FDA算法在强杂波环境中跟踪精度差的问题.给出了基于模糊C均值(FCM)算法的多机动目标跟踪步骤.仿真结果表明IMM-FJPDA算法跟踪精度与IMM-JPDA算法相当,但计算量明显减小,提高了跟踪实时性.     

基于视觉注意机制的认知雷达数据关联算法

针对传统关联波门设计方法在应用于机动目标跟踪时容易引起失跟、以及概率数据关联算法不适于多交叉目标跟踪的问题,提出了一种基于人类视觉选择性注意机制和知觉客体的"特征整合"理论的认知雷达数据关联算法。算法以综合交互式多模型概率数据关联算法为基础,采取假设目标最大机动水平已知的"当前"统计模型和匀速运动模型作为模型集,通过实时交互使关联波门能够随目标机动动态调整,较好地兼顾了雷达计算耗时和跟踪成功率。在利用目标位置特征的基础上,进一步提取、整合目标运动特征,对关联波门交叉区域公共量测进行分类,使多交叉目标跟踪问题转化为多个单目标跟踪问题,优化了传统概率数据关联算法。仿真结果表明：与传统关联波门设计方法相比,算法跟踪失败率和计算耗时明显降低;而且在计算资源增加不大的情况下,杂波环境适应性也得到了显著增强。     

面向目标的概率多假设跟踪算法

 概率多假设跟踪(PMHT)算法由于其计算量与目标和量测的个数成线性关系而成为一种重要的数据关联方法,但该算法采用的是一种面向量测的参数模型,容易受到杂波的干扰。针对这个问题,提出一种面向目标的PMHT（TO/PMHT）算法,将量测与目标的距离作为权重,使计算出的后验关联概率能够真实地反映量测和目标的关联可能性。通过多种典型环境的仿真计算表明：TO/PMHT算法和面向量测的PMHT算法相比,跟踪性能有了明显的提高;与多假设跟踪(MHT)算法相比,在保持跟踪性能的同时,极大地提高了计算效率。     

密集杂波环境下的数据关联快速算法

基于联合概率数据互联（JPDA）的思想,提出了一种新的数据关联快速算法（Fast Al-gorithm for Data Association,简称FAFDA算法）.该方法不需象在最优JPDA算法中那样生成所有可能的联合互联假设,因而具有计算量小,易于工程实现的特点。仿真结果表明,与最优JPDA算法相比,FAFDA算法的跟踪性能令人满意,并且在密集杂波环境下可实时、有效地跟踪100批次以上的目标。     

基于AR/CGARCH模型的液体火箭发动机自适应阈值故障检测算法

为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差（Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH）模型。针对液体火箭发动机稳态试车数据的波动性特点,提出一种基于自回归（Auto-Regressive,AR）模型和CGARCH模型的自适应阈值故障检测算法。采用AR模型对稳态参数的均值进行估计,并采用CGARCH模型对稳态参数的方差进行估计,从而利用均值和方差的估计值自适应地构造检测阈值。用某氢氧火箭发动机的热试车数据进行验证,结果表明,该算法能够准确、快速、灵敏地检测液体火箭发动机故障,在正常工作阶段,能够有效跟踪数据波动性,在故障阶段,能够避免阈值变宽带来的漏检。     

基于序贯关联算法的多目标无源跟踪

基于序贯关联算法,对多目标无源跟踪问题进行了研究。在只有角度信息可以利用的情况下,首先,利用波门技术对各个无源传感器角度测量数据进行关联和滤波,形成参数航迹;然后,将各个无源传感器的参数航迹送到融合中心进行关联配对,并在关联过程中通过构造关联质量函数对参数航迹的关联历史情况进行度量,解决参数航迹关联模糊问题;最后,通过对关联成功的参数航迹进行交叉定位,给出多个不同目标的位置信息,实现分布式无源系统对多目标的数据关联和跟踪,并通过仿真分析,对算法的有效性和可行性进行验证。     

多传感器数据融合在光电经纬仪目标分离段跟踪的应用研究

针对光电经纬仪在目标分离段容易跟踪假目标的问题,提出应用多传感器数据融合方法实现目标分离段的假目标识别。在目标分离前,根据实测的目标轨迹数据通过理论弹道修正算法计算出理论弹道的误差修正量;在目标分离时,利用得出的误差修正量修正目标的理论弹道,并采用模糊聚类关联算法判断各传感器目标测量数据与修正后的目标理论弹道的相关性,从而识别出测量数据是否来自假目标。实验结果表明该方法能够有效地区分出真假目标,判别结果作为跟踪系统及目标识别系统决策依据,有效提高了目标分离段跟踪成功概率。     

改进概率关联算法在组合导航系统中的应用研究

针对组合导航系统中子系统提供的量测信息相互之间关联性不足的问题,在组合导航系统中引入改进概率关联算法,通过改进的概率关联算法将这些导航信息按一定关系联系起来并进行融合计算。首先通过推导短时间概率关联算法求解惯性导航系统(INS)得到信息的可信概率,并引入掩模算子对全球定位系统(GPS)信息进行精度判断;然后,推导出长时间概率关联算法对组合导航系统信息进行融合;最后,建立修正算法并利用长时间概率关联算法得到的信息对惯性导航系统进行误差修正。仿真结果表明,这一改进的概率关联算法可以有效判断GPS信息精度并提高组合导航系统的导航精度。     

基于多模型的航空发动机传感器混合故障诊断方法

本文在多模型架构下,提出一种航空发动机传感器在线混合故障检测与隔离算法。利用长短期记忆网络逼近航空发动机建模误差、健康参数变化、过程噪声和测量噪声等不确定性源引起的真实发动机与机载模型之间的偏差。将传感器测量输出与不确定性值的偏差用于一种基于多模型的混合卡尔曼滤波器组算法中,利用贝叶斯方法计算每个传感器在健康模式和不同故障模式下的条件概率,然后根据最大概率准则进行传感器故障检测与隔离,克服了阈值难以选取的问题。针对某型涡扇发动机传感器发生偏置故障、漂移故障和间歇性故障的情形进行仿真验证,并对比了不同传感器之间的检测与隔离精度。结果表明：所提出的方法可以在更高水平的退化下诊断出发动机传感器常见的故障,混合方法对不同不确定性源具有鲁棒性。     

一种改进的直方图概率多假设多目标跟踪方法

直方图概率多假设跟踪（H-PMHT）方法及其变形泊松分布直方图概率多假设跟踪（P-HPMHT）方法的一个主要缺点是其量测模型仅考虑了背景杂波而没有考虑传感器噪声,从而导致在低信噪比条件下检测概率较低。针对这一问题,提出了一种带传感器噪声模型的H-PMHT方法,通过将传感器噪声引入量测模型,从而明显提高了对低信噪比目标的跟踪检测能力。该方法的计算量与目标数保持线性关系,仍然适用于目标数目较多的情况。仿真实验表明：该方法在误跟踪比率为1‰,信噪比为6 dB时,检测比率可提升近20%,信噪比为3 dB时,可提升近10%。     

修正的概率数据互联算法

阐明了概率数据互联(PDA)算法能很好地解决密集环境下的目标跟踪问题,在该算法基础上,人们又提出了联合概率数据互联(JPDA)算法和一些基于 PDA 的修正算法。在概率数据互联算法中,有一个很重要的参数就是杂波数密度(或波门内虚假量测期望数)。然而在许多实际情况中,这个参数是很难获取的。针对这一问题,文中提出了一种修正的概率数据互联算法,该算法通过实时地调整这一参数来获得对目标较为准确的估计结果。最后,给出了算法的仿真分析。     

空间多分辨率模糊目标跟踪

提出了一种新的模糊目标跟踪算法--CPDA算法。这个算法在空间多分辨率框架下应用概率数据互联算法,在粗分辨率上实现模糊目标跟踪。在不同虚警密度的模糊目标环境下,利用仿真实验分析了CPDA算法的跟踪性能,同时将其与单分辨率上的联合概率数据互联方法进行了性能比较。仿真结果表明,CPDA算法的跟踪性能在达到与单分辨率上JPDA算法同样性能的条件下,能够以较小的计算量跟踪模糊目标。     

SEQUENTIAL ALGORITHM FOR MULTISENSOR PROBABILISTIC DATA ASSOCIATION

Jointprobabillsticdataassociation(JPDA)isanalgorithmusedinsinglesensormultipletargettrackingsystems.Itemploysthenon-uniqueassignmentof"allneighbor"strategytoadaptforthedensemultitargettrackingenvironments[1].Becauseofitswideapplications,itisnecessarytoextendJPDAintosomemultiplesensortrackingsystems.Suchamultisensorsystem,forexample,canbeformedbycollocatingradarandinfraredsearchandtrack(IRST)whichcantakeadvantagesofboththesensorsbodatafusion.Undertheconditionofthesamesensors,acommonmeasure…     

A class of near optimal JPDA algorithms

The crucial problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information If an incorrect hit is associated with a track, that track could diverge and prematurely terminate or cause other tracks to also diverge. Most methods for hit-to-track data association fall into two categories: multiple hypothesis tracking (MHT) and joint probabilistic data association (JPDA). Versions of MHT use all or some reasonable hits to update a track and delay the decision on which hit was correct. JPDA uses a weighted sum of the reasonable hits to update a track. These weights are the probability that the hit originated from the target in track. The computational load for the joint probabilities increases exponentially as the number of targets increases and therefore, is not an attractive algorithm when expecting to track many targets. Reviewed here is the JPDA filter and two simple approximations of the joint probabilities which increase linearly in computational load as the number of targets increase. Then a new class of near optimal JPDA algorithms is introduced which run in polynomial time. The power of the polynomial is an input to the algorithm. This algorithm bridges the gap in computational load and accuracy between the very fast simple approximations and the efficient optimal algorithms     

Tracking of multiple maneuvering targets using multiscan JPDA and IMM filtering

The paper considers the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique. Unlike the standard single-scan JPDA approach, the authors exploit a multiscan joint probabilistic data association (mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving tracking of four maneuvering targets and a multiscan data window of length two     

Suboptimal joint probabilistic data association

A significant problem in multiple target tracking is the hit-to-track data association. A hit is a received signal from a target or background clutter which provides positional information. If an incorrect hit is associated with a track, that track could diverge and terminate. Prior methods for this data association problem include various optimal and suboptimal two-dimensional assignment algorithms which make hit-to-track associations. Another method is to assign a weight for the reasonable hits and use a weighted centroid of those hits to update the track. The method of weighting the hits is known as joint probabilistic data association (JPDA). The authors review the JPDA approach and a simple ad hoc approximation and then introduce a new suboptimal JPDA algorithm. Examples which compare an optimal two-dimensional assignment algorithm with the ad hoc and the new suboptimal JPDA formulation are given     

Multiple scan joint probabilistic data association

This paper presents a multiple scan or n-scan-back joint probabilistic data association (JPDA) algorithm which addresses the problem of measurement-to-track data association in a multiple target and clutter environment. The standard single scan JPDA algorithm updates a track with weighted sum of the measurements which could have reasonably originated from the target in track. The only information the standard JPDA algorithm uses is the measurements on the present scan and the state vectors and covariance matrices of the present targets. The n-scan-back algorithm presented here uses multiple scans of measurements along with the present target information to produce better weights for data association. The standard JPDA algorithm can utilize a formidable amount of processing power and the n-scan-back version only worsens the problem. Therefore, along with the algorithm presentation, implementations which make this algorithm practical are discussed and referenced. An example is also shown for a few n-scan-back window lengths     

Neural solution to the multitarget tracking data associationproblem

The problem of tracking multiple targets in the presence of clutter is addressed. The joint probabilistic data association (JPDA) algorithm has been previously reported to be suitable for this problem in that it makes few assumptions and can handle many targets as long as the clutter density is not very high. However, the complexity of this algorithm increases rapidly with the number of targets and returns. An approximation of the JPDA that uses an analog computational network to solve the data association problem is suggested. The problem is viewed as that of optimizing a suitably chosen energy function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderately dense clutter     

一种基于模糊遗传算法的多传感器多目标跟踪数据关联算法

基于模糊遗传算法发展了一种新的数据关联算法。数据关联的静态部分靠一个模糊遗传算法来得出量测组合序列和S-D分配的m个最优解。在数据关联的动态部分，将得到的S-D分配的m个最优解在一个基于多种群模糊遗传算法的动态2D分配算法中依靠一个卡尔曼滤波估计器估计出移动目标各个时刻的状态。这一基于分配的数据关联算法的仿真试验内容为被动式传感器的航迹形成和维持的问题。仿真试验的结果表明该算法在多传感器多目标跟踪中应用的可行性。另外，对算法发展和实时性问题进行了简单讨论。