Multi-target Tracking In Clutter Research Articles

Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

Improved Multitarget Tracking in Clutter Using Bearings-Only Measurements

Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.

A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter

This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1)the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.

A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter

A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter

PMHT Approach for Underwater Bearing-Only Multisensor–Multitarget Tracking in Clutter

In this work, we apply the probabilistic multihypothesis tracker (PMHT) for the problem of underwater bearing-only multisensor–multitarget tracking in clutter. The PMHT is a batch tracking algorithm that can efficiently process a large number of measurements from multiple sensors. We investigate both the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) for dealing with the high degree of nonlinearity in the measurement model. Due to multiple sensors, the unobservability of single sensor bearing-only target tracking is avoided. Simulation results show that the PMHT works very well in a highly cluttered environment and its computational load is low.

Smoothing joint integrated probabilistic data association

The authors extend the smoothing integrated probabilistic data association algorithm to multi-target tracking in clutter, or alternatively, use smoothing to improve the joint integrated probabilistic data association (JIPDA) algorithm. The predictions of forward and backward JIPDA are fused to form the smoothing prediction, which is used for smoothing multi-target data association.

다중표적 추적필터와 자료연관 기법동향

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

A Hybrid Multi-sensor Multi-target Tracking Scheme with MLE and ANFIS

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.

Sensor Fusion and Target Tracking Using Evidential Data Association

The problem of multitarget tracking in clutter has been shown to be very challenging for both the measurement of track association and the estimation of the targets' state. Several approaches have been suggested to solve this problem. In this paper, we present a new method for data association in multitarget tracking in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal introduces a method of computing mass assignment using a normalized Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses, the method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases using Monte Carlo simulations. The results demonstrate the feasibility of the proposal, and show improved performance compared to the aforementioned alternative commonly used methods.

Class-Dependent Gating Algorithm in Data Association

Most conventional tracking gate algorithms only use the targets’ kinematic measurement information, which is typically resulted in great uncertainties of measurement-to-track association for multi-target tracking in clutter. The problem of constructing tracking gates using targets' class information is considered. The proposed algorithm integrates targets' identity information into the traditional tracking gating techniques. First, a class-dependent gate corresponding to each class of targets is developed. Second, the algorithm for constructing the class-dependent gate is given. Simulations are carried out to examine the proposed algorithm, where the simulation scenario shows that the measurement-to-track association using the class-dependent gating algorithm is significantly better than traditional method.

Gaussian mixtures in multi-target tracking: a look at Gaussian mixture probability hypothesis density and integrated track splitting

Multi-target tracking in clutter, assuming linear target trajectory propagation and linear target measurement equation, naturally leads to a Gaussian mixture (GM) target tracking solution. This study examines and compares two prominent methods that use the GMs: the probability hypothesis density and the integrated track splitting. Both are recursive Bayes methods and both incorporate the false track discrimination capabilities. They are represented in the form of GM target density filters. The modelling assumptions are translated in the algorithmic requirements. The authors compare the algorithms on the basis of these requirements with the future work indicated to reconcile algorithms and requirements.

Point target probabilistic multiple hypothesis tracking

Probabilistic Multiple Hypothesis Tracking (PMHT) is an algorithm for multi-target tracking in clutter with computational requirements, which are linear in the number of targets and the number of measurements. In order to achieve this, the PMHT removes the point target constraint, and uses the expectation maximisation procedure to optimise both data association probabilities and the target trajectory state estimates. However, PMHT is known to have high track-loss percentage in comparison with Probabilistic Data Association, at least in point target tracking. The authors propose a new PMHT-like algorithm to solve some problems of PMHT. In this study they revert the point target constraint. The authors call the new algorithm Point target PMHT. Simulation results show the efficiency of this method.

Multi-target tracking in clutter with sequential Monte Carlo methods

For multi-target tracking (MTT) in the presence of clutters, both issues of state estimation and data association are crucial. This study tackles them jointly by Sequential Monte Carlo methods, a.k.a. particle filters. A number of novel particle algorithms are devised. The first one, which we term Monte-Carlo data association (MCDA), is a direct extension of the classical sequential importance resampling (SIR) algorithm. The second one is called maximum predictive particle filter (MPPF), in which the measurement combination with the maximum predictive likelihood is used to update the estimate of the multi-target's posterior. The third, called proportionally weighting particle filter (PWPF), weights all feasible measurement combinations according to their predictive likelihoods, and uses them proportionally in the importance sampling framework. We demonstrate the efficiency and superiority of our methods over conventional approaches through simulations.

Multiscan Multitarget Tracking in Clutter with Integrated Track Splitting Filter

A fully automatic tracking algorithm must be able to deal with an unknown number of targets, unknown target initiation and termination times, false measurements and possibly time-varying target trajectory behaviour. The approach presented in this paper follows the previously published integrated track splitting (ITS) framework which integrates a recursive calculation of the probability of target existence with multiscan trajectory estimation. This paper combines this framework with two multitarget tracking techniques. The first technique, joint multitarget tracking, enumerates and evaluates all feasible global measurement to track assignments resulting in a conditionally optimal but potentially computationally expensive technique. The second technique, linear multitarget (LM), achieves multitarget functionality by modulating clutter measurement density. LM is a suboptimal, but computationally very efficient technique. A simulation study is presented to show the effectiveness of this approach in the presence of nonuniform clutter when tracking targets in an environment where the targets perform violent manoeuvres.

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.

Joint integrated probabilistic data association: JIPDA

A new recursive filter for multi-target tracking in clutter is presented. Multiple tracks may share the same measurement(s). Joint events are formed by creating all possible combinations of track-measurement assignments and the probabilities for these joint events are calculated. The expressions for the joint event probabilities incorporate the probabilities of target existence of individual tracks, an efficient approximation for the cluster volume and a priori probability of the number of clutter measurements in each cluster. From these probabilities the data association and target existence probabilities of individual tracks are obtained, which allows track state update and false track discrimination. A simulation study is presented to show the effectiveness of this approach.

Alternatives to Monte-Carlo simulation evaluations of two multisensor fusion algorithms

Parallel and sequential multisensor extensions of the probabilistic data association filter for multitarget tracking in clutter are presented, and a non-simulation technique is developed and used to compare tracking performance of the two algorithms in case of a single target. While requiring only a fraction of the time for Monte-Carlo simulation evaluation, the non-simulation technique is shown to accurately predict the average superior performance of the sequential implementation in terms of RMS position error and track lifetime which has been observed in simulations.

Multitarget tracking in clutter: fast algorithms for data association

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A comprehensive analysis of 'Neural solution to the multitarget tracking data association problem' by D. Sengupta and R.A. Iltis (1989)

A comprehensive analysis reveals several drawbacks of the neural network probabilistic data association (NPDA) algorithm, which is an application of the Hopfield neural network to the data association problem for multitarget tracking in clutter. >

Improved multi-target tracking in clutter by PDA smoothing

An algorithm is presented for the recursive tracking of multiple targets in cluttered environment by making use of the joint probabilistic data association fixed-lag smoothing (JPDAS) techniques. It is shown that a significant improvement in the accuracy of track estimation of both nonmaneuvering and maneuvering targets may be achieved by introducing a time lag of one or two sampling periods between the instants of estimation and latest measurement. Results of simulation experiments for a radar tracking problem that demonstrate the effects of fixed-lag smoothing are also presented. >