The SME filter for multiple extended targets tracking

The symmetric measurement equation (SME) filter approach to track maintenance in multiple target tracking (MTT) is extended to the case when the position measurements are obtained from a radar. A key property of the SME filter is that there is no need to consider target/measurement associations to carry out target state estimation, which results in a substantial reduction in the computational complexity of the MTT problem. In the paper the SME filter is developed for the case of N targets moving in three-dimensional space whose motions consist of random perturbations about constant-velocity trajectories. It is assumed that noisy measurements of the spherical co-ordinate positions (ranges, elevation and azimuth angles) of the N targets are available. The work is focused on the case when the symmetric measurement equations are formed by taking sums of products of the Cartesian coordinate position measurements that are generated from the spherical co-ordinate position measurements. The performance of the SME filter is investigated by generating a computer simulation in the six-target case.

The continued development of the symmetric measurement equation (SME) filter for track maintenance in multiple target tracking (MTT) is considered, focusing on the case in which the SMEs are generated by forming sums of products of the original position measurements. The SME filter is developed for the case of N targets whose motions consist of random perturbations about constant-velocity trajectories. It is assumed that measurements of x-coordinate positions are available, and that the number of measurements is equal to the number of targets. Various analytical properties of the SME filter are studied. It is shown that under a very weak condition, the estimation error equation is locally exponentially stable. The performance of the SME filter is investigated by comparing it with an optimal (minimum-variance) estimator and by generating a computer simulation in the six-target case.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Tracking of multiple extended three-dimensional targets in the presence of partial occlusions from point cloud measurement sets remains a challenge but has obvious applications in autonomous vehicles. We demonstrate that existing range-image based approaches to point cloud segmentation and motion detection can be fused with fast point cloud registration techniques to aid track association. Consequently, multiple generic extended targets in point clouds can be tracked. We also show that robust occlusion handling can be achieved using temporally accumulated geometric information from co-registered point clouds in a static local reference frame. The occlusion logic is based on a novel probabilistic occlusion reasoning approach combined with a simulated geometric handling of self-occlusions. The proposed tracking pipeline was tested on synthetic target geometries superimposed on real-world backgrounds, as well as against measured data from benchmark autonomous driving datasets. The targets tracked encountered both artificially introduced and naturally occurring foreground occlusions as well as self-occlusions. Results show notable robustness to foreground occlusions. Incorrect track associations are possible when multiple targets in proximity display similar dynamics and possess nearly identical geometries. Our sequence of tracking algorithms relies on multiple tunable hyper-parameters. These parameters require further automated runtime-optimization before robust, real-time field application can be achieved.

The paper centers on the continued development of the symmetric measurement equation (SME)filter developed by Kamen' for track maintenance in multiple target tracking. In this approach there is noneed to correctly associate measurements and targets before target state estimation can take place. Rather the data association problem is embedded in the process of target state estimation. The first order version of the SME filter is an extended Kalman filter (EKF), and thus the computational requirementsfor filter implementation are comparable to that for a standard Kalman filter. In addition, in contrast toprobabilistic data association filters, the estimator does not rely on the computation of probabilities forcorrect measurement/target associations. The SME filter is based on a standard state model for the targetstate trajectories. However, in contrast to existing approaches, the measurements are defmed in terms of nonlinear symmetric functionals of the target positions, except for one of the measurements which is

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

In a multitarget tracking (MTT) scenario, the prior knowledge of parameters such as clutter rate and detection probability are usually uncertain or estimated offline from training data. However, significant parameters mismatch in the clutter and detection model will result in biased estimates. In such cases, the ability to adaptively online estimate clutter rate and detection probability is critical in MTT under the random finite sets (RFS) framework. In this article, we propose a robust Poisson multi-Bernoulli mixture (PMBM) filter that can accommodate model mismatch in clutter rate and detection probability for multiple extended target tracking (ETT) problems. Moreover, the closed-form solution to the proposed method is derived by the use of Beta and Gamma Gaussian inverse-Wishart (GGIW) distribution, where Beta is used to describe unknown detection probability and GGIW is used to model extended target extension as an ellipse. Simulation results are presented to verify the effectiveness of the proposed method.

In practical target tracking scenarios, targets of different sizes (or extents) may be near or far away from the sensor, which may result in targets appearing as point sources or as extended targets spanning one or more resolution cells, respectively, depending on distance and sensor resolution. In this paper, a new Gaussian Process (GP) measurement model is proposed to explicitly describe the observation about each basis point of GP by an individual dynamic Poisson measurement rate. By employing this model, a novel algorithm to track multiple point targets and extended targets, simultaneously and seamlessly, in the presence of clutter and missed detections is proposed within the Probabilistic Multi-Hypothesis Tracker (PMHT) framework. The proposed algorithm can adapt to spatio-temporally varying target sizes or extents of extended targets and temporally varying target cardinality. In addition, the posterior Cramer-Rao lower bound (PCRLB) for extended targets, which quantifies the accuracies of estimates of multiple extended target states in scenarios with clutter, is derived. Simulations with a scenario consisting of multiple extended targets and point targets are used to verify the effectiveness of the proposed algorithm and to compare its performance with the extended target PCRLB and with those of existing extended target tracking algorithms.

The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

This paper evaluates the performance of multiple target tracking (MTT) algorithms in real-life stressful radar tracking environments. Real closely spaced maneuver radar data, generated by six F-18 fighters and other targets, were collected jointly by the defence departments of Canada and United States to support this practical MTT algorithm evaluation study. A set of performance metrics was defined here to compare the suboptimal nearest neighbor (SNN), global nearest neighbor (GNN), and various variants of the joint probabilistic data association (JPDA) MTT trackers. Results reveal an interesting result that all these MTT algorithms exhibited very close performance. In addition, the weighted sum approach of the PDA/JPDA trackers which are theoretically effective were observed to perform poorly in tracking closely spaced targets. Overall speaking, the GNN filter based on the Munkres algorithm had the best performance in terms of both tracking performance and robustness.

This paper presents an energy-aware, sleep scheduling algorithm called SSMTT to support multiple target tracking sensor networks. SSMTT leverages the awakening result of interfering targets to save the energy consumption on proactive wake-up communication. For the alarm message-miss problem introduced by multiple target tracking, we present a solution that involves scheduling the sensor nodes' sleep pattern. We compare SSMTT against three sleep scheduling algorithms for single target tracking: the legacy circle scheme, MCTA, and TDSS. Our experimental evaluations show that SSMTT achieves better energy efficiency than handling multiple targets separately through single target tracking algorithms.

One of the big challenges in multi-target tracking is the track management and correct data association between measurements and tracks. Major reason for tracking errors are detection failures such as merged, split, incomplete or missed detections as well as clutter-based detections (phantom objects). Those effects combined with uncertainties in existence and number of objects in the scene as well as uncertainties in their observability and dynamic object state lead to gross tracking errors. In this contribution we present an algorithm for visual detection and tracking of multiple extended targets which is capable of coping with occlusions and split and merge effects. Unlike most of the state-of-the-art approaches we utilize information about the measurements' composition gained through tracking dedicated feature points in the image and in 3D space, which allows us to reconstruct the desired object characteristics from the data even in the case of detection errors due to above-mentioned reasons. The proposed Feature-Based Probabilistic Data Association approach resolves data association ambiguities in a soft threshold-free decision based not only on target state prediction but also on the existence and observability estimation modeled as two additional Markov chains. A novel measurement reconstruction scheme allows for a correct innovation in case of split, merged and incomplete measurements realizing thus a detection-by-tracking approach. This process is assisted by a grid based object representation which offers a lower abstraction level of targets extent and is used for detailed occlusion analysis.

This paper describes the use of a simulated real time system of a feed-forward neural network, a recurrent neural network and a set of expert rules in solving the problem of multiple target tracking. It is assumed that data is provided in the from of blips, taken off 3 consecutive focal plane arrays, operating at visible or infrared wavelengths. In this paper, the task of multiple target tracking is transformed from one of blip-frame data association to one of target clustering, which in turn is broken down and solved in four stages. Each stage is described and mapped with the use of a feed-forward, a recurrent neural network or a set of fuzzy rules. The first and second stages of the solution procedure involve the use of two feed-forward neural network modules, while the third and forth stages use a recurrent neural network module and a set of expert rules module. The multiple target tracking solution procedure is simulated through use of FORTRAN code. In principle the number of targets that can be tracked with the routine is unlimited. However, in reality, the number of targets is dictated by the number of neurons, which in turn is constrained by hardware requirements. Software simulation results shows that the multiple target tracking code is capable of tracking an arbitrary number of targets very efficiently. The program was tested and debugged for use in the tracking of sets of multiple targets; ranging from 2 to 14. Results indicated that once the average acceleration of the targets is adequately evaluated, track files could be developed with 100% accuracy.

Visual target tracking is one of the most important research areas in the field of computer vision. Within this realm, multiple targets tracking (MTT) under complicated scene stands out for its great availability in real life applications, such as urban traffic surveillance and sports video analysis. However, in MTT, main difficulties arise from large variation in target saliency and significant motion heterogeneity, which may result in the failure of tracking weak targets. To tackle this challenge, a novel hierarchical layered tracking structure is proposed to perform tracking sequentially layer-by-layer. Upon this layered structure, we establish an intertarget mutual assistance mechanism on basis of intertarget correlation exploited among targets. The tracking results of a subset of targets can be utilized as additional prior information for tracking other targets. Specifically, a nonlinear motion model as well as a target interaction model basing on the intertarget correlation are proposed to effectively estimate the possible target region-of-interest to facilitate the prediction-based tracking. Moreover, the concept of motion entropy is introduced to quantitatively measure the degree of motion heterogeneity within the tracking scene for layer construction. Compared to other existing methods, extensive experiments demonstrated that the proposed method is capable of achieving higher tracking performance in complicated scenes, where targets are characterized with great heterogeneity.

In real world multiple extended target tracking problems, the presence of merged measurements is a frequently occurring phenomenon, however, most existing tracking algorithms in the literature assume that each target generates independent measurements. When this measurement merging phenomenon occurs, it increases the computational complexity of the tracking algorithms. Recently, the conditional joint decision and estimation (CJDE) algorithm based on the generalized Bayes risk was proposed to solve problems of joint detection, tracking and classification (JDTC) of targets. In this paper, we develop a principled Bayesian solution to the important problem involving inter-dependent decision and estimation conditioned on data based on the theory of random finite sets (RFS), and a tractable implementation based on the recently proposed generalized labeled multi-Bernoulli (GLMB) filter. The performance of the proposed technique is demonstrated by simulation of a multi-target bearings-only tracking scenario, where measurements become merged due to finite resolution effects.