基于最近邻的集中式多传感器多目标跟踪算法

经典的集中式多传感器多目标跟踪算法通常计算量较大,经常难以满足系统的实时性要求,工程上实现起来比较困难,为进一步扩大集中式多传感器的应用范围,使其在对算法实时性要求较高、跟踪精度要求较小的实际场合中广泛应用。文章基于最近邻域思想,研究了并行处理结构的集中式多传感器最近邻域算法,并从算法跟踪精度、实时性、有效跟踪率3个方面对其与经典的顺序多传感器联合概率数据互联算法进行了仿真比较。经仿真验证,并行处理结构的集中式多传感器最近邻域算法实时性提高了60%以上,且在跟踪背景杂波适中的情况下能够有效跟踪目标。     

Tracker and signal processing for the benchmark problem with unresolved targets

Radar signal processing is particularly important in tracking closely spaced targets and targets in the presence of sea-surface-induced multipath. Closely spaced targets can produce unresolved measurements when they occupy the same range cell of the radar. These issues are the salient features of the benchmark problem for tracking unresolved targets combined with radar management, for which this paper presents the only complete solution to date. In this paper a modified version of a recently developed maximum likelihood (ML) angle estimator, which can produce two measurements from a single (unresolved) detection, is presented. A modified generalized likelihood ratio test (GLRT) is also described to detect the presence of two unresolved targets. Sea-surface-induced multipath can produce a severe bias in the elevation angle measurement when the conventional monopulse ratio angle extractor method is used. A modified version of a recently developed ML angle extractor, which produces nearly unbiased elevation angle measurements and significantly improves the track accuracy, is presented. Efficient radar resource allocation algorithms for two closely spaced targets and targets flying close to the sea surface are also presented. Finally, the IMMPDAF (interacting multiple model estimator with probabilistic data association filter modules) is used to track these targets. It is found that a two-model IMMPDAF performs better than the three-model version used in the previous benchmark. Also, the IMMPDAF with a coordinated turn model works better than the one using a Wiener process acceleration model. The signal processing and tracking algorithms presented here, operating in a feedback manner, form a comprehensive solution to the most realistic tracking and radar management problem to date.     

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。     

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     

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。     

Multisensor covariance control strategies for reducing bias effects in interacting target scenarios

Algorithms are presented for managing sensor information to reduce the effects of bias when tracking interacting targets. When targets are close enough together that their measurement validation gates overlap, the measurement from one target can be confused with another. Data association algorithms such as the joint probabilistic data association (JPDA) algorithm can effectively continue to track targets under these conditions, but the target estimates may become biased. A modification of the covariance control approach for sensor management can reduce this effect. Sensors are chosen based on their ability to reduce the extent of measurement gate overlap as judged by a set of heuristic parameters derived in this work. Monte Carlo simulation results show that these are effective methods of reducing target estimate bias in the JPDA algorithm when targets are close together. An analysis of the computational demands of these algorithms shows that while they are computationally demanding, they are not prohibitively so.     

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     

Monopulse DOA estimation of two unresolved Rayleigh targets

This paper provides for new approaches to the processing of unresolved measurements as two direction-of-arrival (DOA) measurements for tracking closely spaced targets rather than the conventional single DOA measurement of the centroid. The measurements of the two-closely spaced targets are merged when the target echoes are not resolved in angle, range, or radial velocity (i.e., Doppler processing). The conditional Cramer Rao lower bound (CRLB) is developed for the DOA estimation of two unresolved Rayleigh targets using a standard monopulse radar. Then the modified CRLB is used to give insight into the boresight pointing for monopulse DOA estimation of two unresolved targets. Monopulse processing is considered for DOA estimation of two unresolved Rayleigh targets with known or estimated relative radar cross section (RCS). The performance of the DOA estimator is studied via Monte Carlo simulations and compared with the modified CRLB     

Multitarget tracking in clutter: fast algorithms for dataassociation

Three fast algorithms have been developed to solve the problem of data association in multitarget tracking in clutter. In the first algorithm, the problem of data association is identified as an exhaustive search problem in general. Subsequently, a mathematical model is proposed for the problem of data association in the joint probabilistic data association filter (JPDAF). Based on the model, a depth-first search (DFS) approach is developed for the fast generation of data association hypotheses and the computation of the conditional probabilities of the hypotheses in the JPDAF. When the density of targets is moderate, a second algorithm is developed to directly compute a posteriori probabilities in the JPDAF without generating the data association hypotheses. In the third algorithm, the effect of interference due to closely spaced targets is simplified. An approach to approximately compute the a posteriori probabilities in the JPDAF is developed. The computational complexity of the algorithms is analyzed in the worst case, as well as in the average case     

组合式快速JPDA算法

基于有序状态空间搜索方法,定义联合事件为问题节点,将多目标数据关联问题求解转化为状态空间问题求解。定义联合数据关联概率函数对数表达为节点的估价函数,减少扩展节点数,迅速产生和搜索Ｎ个最大联合概率事件。给出快速计算Ｎ个最大联合概率事件的ＪＰＤＡ公式,并提出一种确定新生节点是否是已生成节点的简便方法。算法的搜索次数不随回波数增加而增加,有效地解决了传统ＪＰＤＡ算法的实时性问题,兼有贝叶斯方法和非贝叶斯方法的各自优点。     

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     

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。     

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     

Comparison of Two Target Classification Techniques

It has been shown that radar returns in the resonance region carry information regarding the overall dimensions and shape of targets. Two radar target classification techniques developed to utilize such returns are discussed. Both of these techniques utilize resonance region backscatter measurements of the radar cross section (RCS) and the intrinsic target backscattered phase. A target catalog used for testing the techniques was generated from measurements of the RCS of scale models of modern aircraft and naval ships using a radar range at The Ohio State University. To test the classification technique, targets had their RCS and phase taken from the data base and corrupted by errors to simulate full-scale propagation path and processing distortion. Several classification methods were then used to determine how well the corrupted measurements fit the measurement target signatures in the catalog. The first technique uses nearest neighbor (NN) algorithms on the RCS magnitude and (range corrected) phase at a number (e.g., 2, 4, or 8) of operating frequencies. The second technique uses an inverse Fourier transformation of the complex multifrequency radar returns to the time domain followed by cross correlation. Comparisons are made of the performance of the two techniques as a function of signal-to-error noise power ratio for various processing options.     

Maximum likelihood angle extractor for two closely spaced targets

In a scenario of closely spaced targets special attention has to be paid to radar signal processing. We present an advanced processing technique, which uses the maximum likelihood (ML) criterion to extract from a monopulse radar separate angle measurements for unresolved targets. This processing results in a significant improvement, in terms of measurement error standard deviations, over angle estimators using the monopulse ratio. Algorithms are developed for Swerling I as well as Swerling III models of radar cross section (RCS) fluctuations. The accuracy of the results is compared with the Cramer Rao lower bound (CRLB) and also to the monopulse ratio technique. A novel technique to detect the presence of two unresolved targets is also discussed. The performance of the ML estimator was evaluated in a benchmark scenario of closely spaced targets - closer than half power beamwidth of a monopulse radar. The interacting multiple model probabilistic data association (IMMPDA) track estimator was used in conjunction with the ML angle extractor     

基于二维资源管理的多功能雷达任务调度算法

动态孔径分割技术为相控阵雷达针对不同任务灵活分配孔径资源提供了可能,而传统的资源调度方法仅基于单一孔径条件研究了时间资源的优化分配问题。针对雷达搜索、跟踪与成像任务的自适应调度问题,提出了一种基于时间-孔径二维资源管理的雷达资源调度算法。该算法建立了雷达孔径分割条件下的二维资源调度模型,确立了能量资源约束条件;利用基于压缩感知的稀疏孔径逆合成孔径雷达（ISAR）成像技术,使雷达在完成目标搜索和跟踪任务的同时实现对目标的成像;定义了调度算法性能的评价指标。在仿真实验中将该算法与另外2种算法进行对比,验证了所提算法在高度成功率、二维资源利用率与任务并行度这3种性能指标上具有优越性。     

Multiple hypothesis track maintenance with possibly unresolved measurements

In surveillance problems dense clutter/dense target situations call for refined data association and tracking techniques. In addition, closely spaced targets may exist which are not resolved. This phenomenon has to be considered explicitly in the tracking algorithm. We concentrate on two targets which temporarily move in close formation and derive a generalization of MHT methods on the basis of a simple resolution model.     

STOCHASTIC NEURAL NETWORK AND ITS APPLICATION TO MULTI－MANEUVERING TARGET TRACKING

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基于数据关联的故障快速检测

 多数情况下,快速实时地进行故障检测是很重要的。将故障看做是通过多传感器观测的动态模型,进行多传感器多模型概率数据关联,以各个模型的关联结果和设定的阈值为依据,可以有效地实现故障检测。联合概率数据关联（JPDA）算法是解决多传感器多目标跟踪的一个有效方法,文中通过分析概率数据关联算法,对联合概率数据关联算法进行了改进：（1）通过正确地选择阈值,移除小概率事件,进而建立一个近似的确认矩阵;（2）根据被跟踪目标故障跟踪门的相交情况,将跟踪空间进行数学划分,形成若干相互独立的区域;（3）对同一区域内公共有效量测的概率密度值进行衰减,计算出关联概率。仿真对比表明,本文的改进算法能显著减少计算时间,有效提高故障检测的快速性和实时性。