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。     

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     

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     

Monte Carlo filtering for multi target tracking and data association

We present Monte Carlo methods for multi-target tracking and data association. The methods are applicable to general nonlinear and non-Gaussian models for the target dynamics and measurement likelihood. We provide efficient solutions to two very pertinent problems: the data association problem that arises due to unlabelled measurements in the presence of clutter, and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets. We develop a number of algorithms to achieve this. The first, which we refer to as the Monte Carlo joint probabilistic data association filter (MC-JPDAF), is a generalisation of the strategy proposed by Schulz et al. (2001) and Schulz et al. (2003). As is the case for the JPDAF, the distributions of interest are the marginal filtering distributions for each of the targets, but these are approximated with particles rather than Gaussians. We also develop two extensions to the standard particle filtering methodology for tracking multiple targets. The first, which we refer to as the sequential sampling particle filter (SSPF), samples the individual targets sequentially by utilising a factorisation of the importance weights. The second, which we refer to as the independent partition particle filter (IPPF), assumes the associations to be independent over the individual targets, leading to an efficient component-wise sampling strategy to construct new particles. We evaluate and compare the proposed methods on a challenging synthetic tracking problem.     

Tracking of Spawning Targets with Multiple Finite Resolution Sensors

In this paper the problem of tracking multiple spawning targets with multiple finite-resolution sensors is considered and a new algorithm for measurement-to-track association with possibly unresolved measurements is presented. The goal is to initialize new tracks of spawned targets before they are resolved from the mother platform so that one has the ability to carry out early discrimination when they become resolved. The multiple scan data association problem is first formulated as a multidimensional assignment problem with explicit new constraints for the unresolved measurements. Then the top M hypotheses tracking (TMHT) is presented where the state estimates and their covariances are modified based on the M best hypotheses through the assignment solutions. A modification to the assignment problem is developed that leads to a linear programming (LP) where the optimal solution can be a noninteger in [0,1]. The fractional optimal solution is interpreted as (pseudo) probabilities over the N - 1 frame sliding window. The best hard (binary) decision assignment solution and the M best via TMHT are compared with the soft decision solution for 2-D tracking scenarios with various sensor configurations. Based on the simulation results, the soft assignment approach has better track maintenance capability than the single best hard assignment and a performance nearly as good as the TMHT. Its computational load is slightly higher than the single best hard assignment but much lighter than TMHT.     

Tracking in Clutter using IMM-IPDA?Based Algorithms

We describe three single-scan probabilistic data association (PDA) based algorithms for tracking manoeuvering targets in clutter. These algorithms are derived by integrating the interacting multiple model (IMM) estimation algorithm with the PDA approximation. Each IMM model a posteriori state estimate probability density function (pdf) is approximated by a single Gaussian pdf. Each algorithm recursively updates the probability of target existence, in the manner of integrated PDA (IPDA). The probability of target existence is a track quality measure, which can be used for false track discrimination. The first algorithm presented, IMM-IPDA, is a single target tracking algorithm. Two multitarget tracking algorithms are also presented. The IMM-JIPDA algorithm calculates a posteriori probabilities of all measurement to track allocations, in the manner of the joint IPDA (JIPDA). The number of measurement to track allocations grows exponentially with the number of shared measurements and the number of tracks which share the measurements. Therefore, IMM-JIPDA can only be used in situations with a small number of crossing targets and low clutter measurement density. The linear multitarget IMM-IPDA (IMM-LMIPDA) is also a multitarget tracking algorithm, which achieves the multitarget capabilities by integrating linear multitarget (LM) method with IMM-IPDA. When updating one track using the LM method, the other tracks modulate the clutter measurement density and are subsequently ignored. In this fashion, LM achieves multitarget capabilities using the number of operations which are linear in the: number of measurements and the number of tracks, and can be used in complex scenarios, with dense clutter and a large number of targets.     

Fast data association using multidimensional assignment withclustering

We present a fast data association technique based on clustering and multidimensional assignment algorithms for multisensor-multitarget tracking Assignment-based methods have been shown to be very effective for data association. Multidimensional assignment for data association is an NP-hard problem and various near-optimal modifications with (pseudo-)polynomial complexity have been proposed. In multidimensional assignment, candidate assignment tree building consumes about 95% of the time. We present the development of a fast data association algorithm, which partitions the problem into smaller sub-problems. A clustering approach, which attempts to group measurements before forming the candidate tree, is developed for various target-sensor configurations. Simulation results show significant computational savings over the standard multidimensional assignment approach without clustering     

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     

Multi-Target Tracking in Clutter without Measurement Assignment

When tracking targets using radars and sonars, the number of targets and the origin of data is uncertain. Data may be false measurements or clutter, or they may be detections from an unknown number of targets whose possible trajectories and detection processes can only be described in a statistical manner. Optimal all-neighbor multi-target tracking (MTT) in clutter enumerates all possible joint measurement-to-track assignments and calculates the a posteriori probabilities of each of these joint assignments. The numerical complexity of this process is combinatorial in the number of tracks and the number of measurements. One of the key differences between most MTT algorithms is the manner in which they reduce the computational complexity of the joint measurement-to-track assignment process. We propose an alternative approach, using a form of soft assignment, that enables us to bypass this step entirely. Specifically, our approach treats possible detections of targets followed by other tracks as additional clutter measurements. It starts by approximating the a~priori probabilities of measurement origin. These probabilities are then used to modify the clutter spatial density at the location of the measurements. A suitable single target tracking (STT) filter then uses the modified clutter intensity for updating the track state. In effect, the STT filter is transformed into an MTT filter with a numerical complexity that is linear in the number of tracks and the number of measurements. Simulations show the effectiveness of this approach in a number of different multi-target scenarios.     

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。     

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。     

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。     

Data association for deghosting in Y-shaped passive linear array sonars

This paper deals with data association using three sets of passive linear array sonars (PLAS) geometrically positioned in a Y-shaped configuration, fixed in an underwater environment. The data association problem is directly transformed into a 3D assignment, which is known to be NP hard. For generic passive sensors, it can be solved using conventional algorithms, while in PLAS, it becomes a formidable task due to the presence of bearing ambiguity. Thus, the central issue of the problem in PLAS is how to eliminate the bearing ambiguity without increasing tracking error. To solve this problem, the 3D assignment algorithm used the likelihood value of only those observed bearing measurements is modified by incorporating frequency information in consecutive time-aligned scans. The region of possible ghost targets is first established by the geometrical relation of PLAS with respect to target. The ghost targets are then confirmed and eliminated by generating multiple observations in consecutive scans. Representative simulations demonstrate the effectiveness of the proposed approach.     

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.     

Directed Subspace Search ML-PDA with Application to Active Sonar Tracking

The maximum likelihood probabilistic data association (ML-PDA) tracking algorithm is effective in tracking Very Low Observable targets (i.e., very low signal-to-noise ratio (SNR) targets in a high false alarm environment). However, the computational complexity associated with obtaining the track estimate in many cases has precluded its use in real-time scenarios. Previous ML-PDA implementations used a multi-pass grid (MPG) search to find the track estimate. Two alternate methods for finding the track estimate are presented-a genetic search and a newly developed directed subspace (DSS) search algorithm. Each algorithm is tested using active sonar scenarios in which an autonomous underwater vehicle searches for and tracks a target. Within each scenario, the problem parameters are varied to illustrate the relative performance of each search technique. Both the DSS search and the genetic algorithm are shown to be an order of magnitude more computationally efficient than the MPG search, making possible real-time implementation. In addition, the DSS search is shown to be the most effective technique at tracking a target at the lowest SNR levels-reliable tracking down to 5 dB (postprocessing SNR in a resolution cell) using a 5-frame sliding window is demonstrated, this being 6 dB better than the MPG search.     

基于相对位置矢量的群目标灰色精细航迹起始算法

为解决群内目标精细航迹起始的难题,基于对传统航迹起始算法及现有群目标航迹起始算法优缺点的分析,给出了完整的群目标航迹起始框架,并提出了一种基于相对位置矢量的群目标灰色精细航迹起始算法。首先基于循环阈值模型、群中心点进行群的预分割、预关联,然后对预关联成功的群搜索对应坐标系,建立群中各量测的相对位置矢量,基于灰色精细互联模型完成群内量测的互联,最后基于航迹确认规则得到群目标状态矩阵。经仿真数据验证,与修正的逻辑法、基于聚类和Hough变换的多编队航迹起始算法相比,该算法在起始真实航迹、抑制虚假航迹及杂波鲁棒性等方面综合性能更优。     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

On the Sensitivity of a Fixed-Point Smoothing Algorithm

This paper presents a simple approach to the derivation of sensitivity measures of smoothing algorithms. It is proposed that the known sensitivity results of the Kalman filtering algorithm be utilized along with the state augmentation approach for this purpose. The sensitivity measures so obtained are easier to compute than the ones available in the literature. It is then shown that the fixed-point smoothing algorithm, derived recently by Biswas and Mahalanabis [1], [2], is less sensitive to model parameter variations than the algorithm studied by Griffin and Sage [7], [8]. The case of a satellite tracking problem is presented by way of illustrating the results.     

Statistical characterization of clutter scenes based on a Markov random field model

The problem of clutter region identification based on Markov random field (MRF) models is addressed. Observations inside each clutter region are assumed homogenous, i.e., observations follow a single probability distribution. Our goal is to partition clutter scenes into homogenous regions and to determine this underlying probability distribution. Metropolis-Hasting and reversible jump Markov chain (RJMC) algorithms are used to search for solutions based on the maximum a posteriori (MAP) criterion. Several examples illustrate the performance of our algorithm.