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。     

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。     

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     

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     

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     

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

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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     

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。     

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.     

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     

Joint probabilistic data association for autonomous navigation

A new autonomous navigation scheme based on the joint probabilistic data association (JPDA) approach that processes landmark detections in the field of view (FOV) of an on-board sensor is developed. These detections-some true, some false-are associated to a set of stored landmarks and used to update the state of the vehicle. The results obtained from Monte Carlo simulations prove the ability of this navigation filter to perform in very high false alarm environments. In the different environmental conditions tested in the simulations, the performance of the JPDA navigation filter (JPDANF) is very close to that of the filter based on perfect data association. The very efficient cluster decomposition algorithm presented for the purpose of the navigation problem can also be used in many multitarget tracking applications     

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。     

Super resolution feature extraction of moving targets

High range resolution (HRR) moving target indicator (MTI) is becoming increasingly important for many military and civilian applications such as the detection and classification of moving targets in strong clutter background. We consider the problem of extracting the HRR features of moving targets with very closely spaced scatterers in the presence of strong stationary clutter, where the range migration and Doppler frequency are taken into account. A relaxation-based algorithm, which is robust and computationally simple, is proposed to deal with the above problem. Numerical results have shown that the proposed algorithm exhibits super resolution and excellent estimation performance     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.     

An interior point linear programming approach to two-scan dataassociation

We present an efficient two-scan data association method (TSDA) based on an interior point linear programming (LP) approach. In this approach, the TSDA problem is first formulated as a 3-dimensional assignment problem, and then relaxed to a linear program; the latter is subsequently solved by the highly efficient homogeneous, self-dual interior point LP algorithm. When the LP algorithm generates a fractional optimal solution, we use a technique similar to the joint probabilistic data association method (JPDA) to compute a weighted average of the resulting fractional assignments, and use it to update the states of the existing tracks generated by Kalman filters. Unlike the traditional single scan JPDA method, our TSDA method provides an explicit mechanism for track initiation. Extensive computer simulations have demonstrated that the new TSDA method is not only far more efficient in terms of low computational complexity, but also considerably more accurate than the existing single-scan JPDA method     

Dynamically adaptable m-best 2-D assignment algorithm andmultilevel parallelization

In recent years, there has been considerable interest within the tracking community in an approach to data association based on the m-best two-dimensional (2D) assignment algorithm. Much of the interest has been spurred by its ability to provide various efficient data association solutions, including joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT). The focus of this work is to describe several recent improvements to the m-best 2D assignment algorithm. One improvement is to utilize a nonintrusive 2D assignment algorithm switching mechanism, based on a problem sparsity threshold. Dynamic switching between two different 2D assignment algorithms, highly suited for sparse and dense problems, respectively, enables more efficient solutions to the numerous 2D assignment problems generated in the m-best 2D assignment framework. Another improvement is to utilize a multilevel parallelization enabling many independent and highly parallelizable tasks to be executed concurrently, including 1) solving the multiple 2D assignment problems via a parallelization of the m-best partitioning task, and 2) calculating the numerous gating tests, state estimates, covariance calculations, and likelihood function evaluations (used as cost coefficients in the 2D assignment problem) via a parallelization of the data association interface task. Using both simulated data and an air traffic surveillance (ATS) problem based on data from two Federal Aviation Administration (FAA) air traffic control radars, we demonstrate that efficient solutions to the data association problem are obtainable using our improvements in the m-best 2D assignment algorithm     

Bearings-only tracking of maneuvering targets using abatch-recursive estimator

We present a new batch-recursive estimator for tracking maneuvering targets from bearings-only measurements in clutter (i.e., for low signal-to-noise ratio (SNR) targets), Standard recursive estimators like the extended Kalman Iter (EKF) suffer from poor convergence and erratic behavior due to the lack of initial target range information, On the other hand, batch estimators cannot handle target maneuvers. In order to rectify these shortcomings, we combine the batch maximum likelihood-probabilistic data association (ML-PDA) estimator with the recursive interacting multiple model (IMM) estimator with probabilistic data association (PDA) to result in better track initialization as well as track maintenance results in the presence of clutter. It is also demonstrated how the batch-recursive estimator can be used for adaptive decisions for ownship maneuvers based on the target state estimation to enhance the target observability. The tracking algorithm is shown to be effective for targets with 8 dB SNR     

Target detection trials with a millimeter wave radar system

Unobstructed, large RCS targets, similar radar targets surrounded by moving foliage, and small targets in severe clutter have been used as test cases for two pre-processing algorithms and several threshold levels in an experimental millimeter wave radar system. The rather conventional "six-out-of-eight" pulse radar selection method with binary output has been compared to an algorithm that accepts a target if the pre-defined trigger level is crossed by the average of the eight consecutive pulses. In this case, however, the output is an analog value corresponding to the relative average video amplitude. In terms of plotted video, this process seems to give a slightly better combination of false alarm rate and detection probability. Large targets are easier to detect from foliage clutter with the conventional method.     

Multiple target detection using modified high order correlations

This work is concerned with the problem of multiple target track detection in heavy clutter. Using the “modified high order correlation” (HOC) process and a track scoring mechanism a new method is developed to perform data association and track identification in the presence of heavy clutter. Using this new scheme any number of very close, crossing or splitting target tracks can be resolved without increasing the computational complexity of the algorithm. The applicability of the method for continuous detection of target tracks that can originate and terminate at any scan is also demonstrated, In addition, the operating characteristics as a function of the clutter density are also provided. Simulation results on all the cases are presented