Maneuvering target tracking using adaptive M/sup 3/JPDA

A single-sensor, single-target mixture reduction (MR) data association algorithm is extended for use in multisensor, multitarget tracking situations. MR is extended for tracking an arbitrary number of targets using an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. Like the single-sensor, single-target MR algorithm, which gives better performance than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions give similar improvements in performance compared to the joint PDA (JPDA) and multisensor JPDA (MSJPDA) algorithms. Further, in the formulations for the multisensor and multitarget MR algorithms, the equations for the calculation of the data association probabilities have been put in the same form as for the JPDA, thus allowing previously developed fast JPDA computational techniques to be applicable. N the problem of tracking targets in random clutter, the optimal Bayesian solution leads to Gaussian mixture distributions that consist of an exponentially increasing number of components.1 In practice, the number of components in each mixture is kept in check by approximating it with fewer components. The nearest neighbor (NN) approximation reduces each target's mixture to its largest component.13 The joint probabilistic data association (JPDA) approximation merges the components of each target's mixture into a single Gaussian component.1'4 However, when there are several significant, well-spaced components in the original mixture, important information may be lost by using the NN or JPDA methods. A more flexible approximation method for single-sensor, single-target tracking, known as mixture reduction (MR), has been proposed by Salmond.5 This MR algorithm preserves the mean and covariance of the original mixture of components, successively merging components until the number of components is reduced to some userspecified limit. In this paper, we first review Salmond's MR algorithm for single-sensor tracking of a single target in clutter. We then derive extensions to Salmond's MR algorithm for use in tracking multiple targets and when there are measurements available from multiple sensors. Just as the single-sensor, single-target MR algorithm gives better performance5 than the probabilistic data association (PDA) filter, the multisensor, multitarget MR extensions are shown to give similar improvements in performance as compared to the JPDA and multisensor JPDA (MSJPDA)6 algorithms. Extension of the MR algorithm for tracking of multiple targets is necessary for use in realistic tracking scenarios where there are usually many targets that need to be tracked. Moreover, many current tracking systems have been designed with multiple sensors, thus requiring the further extension to a multisensor algorithm. The paper is organized as follows. In Sec. II, the target dynamics and measurement equations are defined. We review the singlesensor, single-target MR algorithm5 in Sec. Ill, and we extend the algorithm to the multitarget and multisensor cases in Sec. IV and V, respectively. In Sec. VI, simulation results evaluating the performances of the MR extensions are presented. Finally, concluding remarks are given in Sec. VII.

Multiple target tracking (MTT) has received much attention recently for various applications in the military as well as the Strategic Defense Initiative areas. Data association is one of the critical computation in MTT problems, because erroneous data associations often result in lost tracks. The joint probabilistic data association (JPDA) algorithm is a good approach to solve the data association problem. However, the computation complexity of this algorithm increases rapidly with the number of targets and radar returns. Neural networks have been considered to approximate the JPDA and ease the computation burden through the parallel processing. In this paper, we propose a neural network data association (NNDA) algorithm for the solution of the data association problems. Simulation results show the following three controversial issues: first, NNDA can track multiple targets with performance compatible with JPDA. Second, when the prediction filter can not provide an enough accurate prediction data ( This sometimes occurs when the prediction model can not match the tracking environment precisely enough), the neural computation provides better performace than JPDA. Third, the performance of NNDA is not affected by the number while that of JPDA degrades with the increase of target number. As a whole, this paper presents a neural network which not only possesses the intrinsic ability of parallel computation but also provides conditionally better tracking performance than JPDA.

Joint probabilistic data association (JPDA) is a powerful tool for solving data association problems. However, the exact computation of association probabilities {/spl beta//sub jk/} in JPDA is NP-hard, where /spl beta//sub jk/ is the probability that j-th observation is from k-th track. Hence, we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless P = NP. In this paper, we present a simple Markov chain Monte Carlo data association (MCMCDA) algorithm that finds an approximate solution to JPDA in polynomial time. For /spl epsiv/ > 0 and 0 < /spl eta/ < .5, we prove that the algorithm finds good estimates of /spl beta//sub jk/ with probability at least 1 - /spl eta/ in time complexity O(/spl epsiv//sup -2/ log /spl eta//sup -1/N(N log N + log /spl epsiv//sup -1/))), where N is the number of observations.

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 increase exponentially as the number of targets increases and therefore, is not an attractive algorithm when expecting to track many targets. This paper reviews 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.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper we extend a multitarget tracking algorithm for use in multisensory tracking situations. The algorithm we consider is Joint Probabilistic Data Association (JPDA). JPDA is extended to handle an arbitrary number of sensors under the assumption that the sensor measurement errors are independent across sensors. We also show how filtering can be handled in multisensory JPDA (MSJ PDA) without leading to an exponential increase in filtering complexity. Simulation results are presented comparing the performance of the MSJPDA with another multisensory fusion algorithm and with the single-sensor JPDA algorithm.

The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relationbetween the density of a random finite set and the ordinary density of a state vector to improve on the JointProbabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of theGaussian approximations of the JPDA, which results in avoidance of track coalescence. Another advantage is animproved estimation accuracy in terms of a measure which disregards target identity. In this paper we extend thefilter to also consider multiple motion models. As a basis for the extension we use the Interacting Multiple Model(IMM) algorithm. We derive three alternative filters that we jointly refer to as Set IMMJPDA (SIMMJPDA).They are based on two alternative descriptions of the IMMJPDA filter. In the paper, we also present simulationresults for a two-target tracking scenario, which show improved tracking performance for the Set IMMJPDAfilter when evaluated with a measure that disregards target identity.

Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to maintain tracks of objects. The results of JPDA are employed to improve the observation model in the particle filter. Based on the ability of handling missing detections and clutter of JPDA, tracks of objects can be maintained after merging or splitting. Conversely, the particle filter also improves the performance of JPDA by fusing other observations such as color and background subtraction information. Hence, our method can take advantages from both JPDA and particle filter to track objects through merging and splitting.

Recent literature has introduced several methods for processing an out-of-sequence measurement (OOSM) in tracking for both the 1-step-lag and ¿-step-lag case. However, in a realistic tracking application, a data association algorithm, such Joint Probabilistic Data Association (JPDA) must be used in order to associate measurements with tracks in a cluttered environment. This paper investigates the OOSM problem in tracking for both the 1-step-lag and ¿-step-lag cases using JPDA. The OOSM algorithms presented in the literature are extended to support JPDA and the results are analyzed. The resulting algorithms are then applied to an automotive frontal pre-crash system, where OOSM and JPDA are vital factors in improving the performance of such a system.

The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation between the density of a random finite set and the ordinary density of a state vector to improve on the Joint Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the Gaussian approximations of the JPDA, which result in avoidance of track coalescence. In the original presentation of the SJPDA filter, the focus was on problems where target identity is not relevant, and it was shown that the filter performs better than the JPDA filter for such problems. The improved performance of the SJPDA is due to its relaxation of the labeling constraint that hampers most tracking approaches. However, if track identity is of interest a record of it may be kept even with a label-free approach such as the SJPDA: label-free targets are localized via the SJPDA, and then the identities are recalled as an overlay.

Aiming at the problem of tracking instability or misalignment when the target is occluded or disturbed by similar targets, this paper proposes an improved occluded target tracking method based on JPDA (Joint Probability Data Association) based on the analysis of JPDA algorithm which is prone to misalignment when the target is occluded. The algorithm judges by adding multi-dimensional attributes (area, gray level, location, etc.). Accurate correlation of inter-frame targets is achieved. the accuracy of occluded target position prediction is improved to enhance the anti-occlusion ability. The measurement image sequence is used to simulate at finally. The simulation results show that the accuracy of occluded target location extraction is significantly improved by using the improved multi-feature based algorithm JPDA, which can meet the target tracking requirements in most occluded scenarios.

To improve the accuracy of multiple target tracking in the clutter environment, a new joint probabilistic data association (JPDA) algorithm based on variational Bayesian adaptive moment estimation is proposed. First, considering the existence of measurements, the posterior distribution of the target state in JPDA is composed of two parts of probability weighting, that is, the posterior distribution of the target state that the real measurement exists in the association gate and the posterior distribution of the target state that the real measurement does not exist in the association gate. By combining the conjugate properties of the prior and posterior distributions, the prior distributions of the target state in the two cases are classified to provide more accurate a priori information to filter, so as to improve the accuracy of data association. Second, considering the coupling effect between state estimation and data association process, combined with variational Bayesian inference, the problem of minimizing Kullback–Leibler divergence is transformed into the problem of maximizing the evidence lower bound, thereby effectively measuring the distance between the posterior distribution of target state estimation and the real posterior distribution, so as to improve the accuracy of data association again from the perspective of optimizing nonlinear filter. Finally, the adaptive momentum estimation strategy is introduced to iteratively solve the variable distribution that meets the maximization of the evidence lower bound, and the optimization of the posterior distribution of the target state is completed. Theoretical derivation and simulation experiments are conducted to verify the feasibility and effectiveness of the algorithm.

An important step in multitarget tracking (MTT) is the assignment of measurement/target pairing probabilities, i.e., the probability that a certain measurement is associated with a particular target. This is one of the more computationally intensive steps of the Joint Probabilistic Data Association (JPDA) and other MTT algorithms. Determining such assignment probabilities in the conventional JPDA algorithm involves the generation and analysis of all possible binary feasibility matrices, where each feasibility matrix represents the feasible event that each measurement is due to only one target (or clutter) and that each target causes at most one measurement. We discuss a new and most efficient technique for obtaining the measurement/target pairing probabilities directly (without first producing the feasibility matrices) and its implementation on a new analog optical computer.

This paper addresses an IMM (interacting multimodel)-JPDA (joint probabilistic data association) tracker with sub-event decomposition and one-point prime initialization. In the original JPDA, the number of joint feasible association event is about the factorial size of the target numbers, which may cause the huge computational burden. The proposed sub-event JPDA can significantly reduce the number of hypotheses without reducing tracking performance. One-point prime initialization method estimates initial velocities using initial range rates and azimuths. A scenario of 10 multiple targets is tested and the performance is evaluated in terms of root mean-squared errors of position and velocity and the averaged mode probabilities. The computational time of the sub-event IMMJPDA tracker is compared with that of the IMM-JPDA showing the significantly improved time efficiency of the proposed method.

The JDPA (joint probabilistic data association) algorithm has been reported to be suitable for tracking multiple targets in the presence of extraneous observations (i.e., clutter, thermal false alarms, other targets) and no reports (missed target detections). In the present work, a novel method for highly maneuvering target tracking is presented which is an extension of the JPDA. Computer simulation indicated that this method can track a highly maneuvering target (7 g) in the presence of extremely dense clutter (with a probability of a false alarm of 0.1) with a sampling interval of 0.5 s.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The Joint Probabilistic Data Association (JPDA) solves single sensor multi-target tracking in clutter, but it cannot be used directly in multi-sensor multi-target tracking (MMT) and has high computational complexity with the number of targets and the number of returns. This paper presents a hybrid method to implement MMT by combing Maximum Likelihood Estimation (MLE) with Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLE is applied to classify the same source observations at one time into the same set, then the cheap JPDA(CJPDA) approach is used to calculate the data association probability, and ANFIS is used to realize the MMT. The computer simulations indicate that this scheme achieves MMT perfectly with higher precision and easy realization.