Joint probabilistic data association-feedback particle filter for multiple target tracking applications (original) (raw)
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International Journal of Artificial Intelligence & Applications, 2012
Joint multiple target tracking and classification is an important issue in many engineering applications. In recent years, multiple sensor data fusion has been extensively investigated by researchers in a variety of disciplines. Indeed, combining results issued from multiple sensors can provide more accurate information than using a single sensor. In the present paper we address the problem of data fusion for joint multiple maneuvering target tracking and classification in cluttered environment where centralized versus decentralized architectures are often opposed. The proposal advocates a hybrid approach combining a Particle Filter (PF) like method to deal with system nonlinearities and Fitgerald's Cheap Joint Probabilistic Data Association Filter CJPDAF for the purpose of data association and target estimation problems, yielding CJPDA-PF algorithm. While the target maneuverability is tackled using a combination of a Multiple Model Filter (MMF) and CJPDAF, which yields CJPDA-MMPF algorithm. Consequently, at each particle level (of the particle filter), the state of the particle is evaluated using the suggested CJPDA-MMF. In case of several sensors, the centralized fusion architecture and the distributed architecture in the sense of Federated Kalman Filtring are investigated and compared. The feasibility and the performances of the proposal have been demonstrated using a set of Monte Carlo simulations dealing with two maneuvering targets with two distinct operation modes and various clutter densities.
Particle Filters for Multiple Target Tracking
Procedia Technology, 2016
Multiple target tracking has immense application in areas such as surveillance, air traffic control, defense and computer vision. The aim of a target tracking algorithm is to estimate the target position precisely from the partial noisy observations available. The real challenges of multiple target tracking are to accomplish the same in the presence of measurement origin uncertainty and clutter. Optimal solutions are available by way of Kalman filters for the special case of linear dynamical systems with Gaussian noise. For a more general scenario, we resort to the suboptimal solutions like Particle filters which implement stochastic filtering through a sequential Monte Carlo approach. Measurement origin uncertainty is resolved by using a suitable data association technique prior to the filtering. This paper explores the possibilities of applying a variant of Ensemble Square Root Filters (EnSRF) in a multiple target tracking scenario and its tracking performance is compared with those of conventional Bootstrap and Auxiliary Bootstrap particle filters. The filtering scheme proposed here incorporates Sample based Joint Probabilistic Data Association (SJPDA) in the EnSRF framework for dealing with measurement origin uncertainty.
Monte Carlo filtering for multi-target tracking and data association
IEEE Transactions on Aerospace and Electronic Systems, 2005
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 in [1] and [2]. 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.
Data Association for an Adaptive Multi-target Particle Filter Tracking System
This paper presents a novel approach to improve the accuracy of tracking multiple objects in a static scene using a particle filter system by introducing a data association step, a state queue for the collection of tracked objects and adaptive parameters to the system. The data association step makes use of the object detection phase and appearance model to determine if the approximated targets given by the particle filter step match the given set of detected objects. The remaining detected objects are used as information to instantiate new objects for tracking. State queues are also used for each tracked object to deal with occlusion events and occlusion recovery. Finally we present how the parameters adjust to occlusion events. The adaptive property of the system is also used for possible occlusion recovery. Results of the system are then compared to a ground truth data set for performance evaluation. Our system produced accurate results and was able to handle partially occluded objects as well as proper occlusion recovery from tracking multiple objects.
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
Sensors (Basel, Switzerland), 2017
We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tra...
Joint particle filtering of multiple maneuvering targets from unassociated measurements
Journal of Advances in Information Fusion, 2006
The problem of maintaining tracks of multiple maneuvering targets from unassociated measurements is formulated as a problem of estimating the hybrid state of a Markov jump linear system from measurements made by a descriptor system with independent, identically distributed (i.i.d.) stochastic coefficients. This characterization is exploited to derive the exact equation for the Bayesian recursive filter, to develop two novel Sampling Importance Resampling (SIR) type particle filters, and to derive approximate Bayesian filters which use for each target one Gaussian per maneuver mode. The two approximate Bayesian filters are a compact and a trackcoalescence avoiding version of Interacting Multiple Model Joint Probabilistic Data Association (IMMJPDA). The relation of each of the four novel filter algorithms to the literature is well explained. Through Monte Carlo simulations for a two target example, these four filters are compared to each other and to the approach of using one IMMPDA filter per target track. The Monte Carlo simulation results show that each of the four novel filters clearly outperforms the IMMPDA approach. The results also show under which conditions the IMMJPDA type filters perform close to exact Bayesian filtering, and under which conditions not.
In this paper we propose to use Regularized Monte Carlo-Joint Probabilistic Data Association Filter (RMC-JPDAF) to the classical problem of multiple target tracking in a cluttered area. We have used the Monte Carlo methods in order to the fact that they have the ability to model any state-space with nonlinear and non-Gaussian models for target dynamics and measurement likelihood. To encounter with the data association problem that arises due to unlabeled measurements in the presence of clutter, we have used the Joint Probabilistic Data Association Filter (JPDAF). Due to the resampling stage in the MC-JPDAF, the sample impoverishment phenomenon is unavoidable and the tracking performance will decrease. So we propose to use the Regularized resampling stage instead, to counteract this effect. Finally we have used the target dynamics model as the proposal distribution in MC-JPDAF, in order to decrease the computational cost while the performance of the tracking system is nearly maintained.
Nonlinear multiple model particle filters algorithm for tracking multiple targets
Archives of Control Sciences, 2011
The paper addresses multiple targets tracking problem encountered in number of situations in signal and image processing. In this paper, we present an efficient filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, which we aim to contribute in solving the problem of multiple targets tracking using bearings-only measurements. The idea of this algorithm consists of the combination between the multiple model approach and particle filtering methods, which give a nonlinear multiple model particle filters algorithm. This algorithm is used to estimate the trajectories of multiple targets assumed to be nonlinear, from their noisy bearings.