Distributed joint probabilistic data association filter with hybrid fusion strategy

This paper investigates the problem of distributed multitarget tracking (MTT) over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbors for information fusion. The conventional fusion strategies, i.e., consensus on measurement (CM) and consensus on information (CI), are extended to MTT scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed MTT algorithm using a hybrid fusion strategy, e.g., a mix of CM and CI, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralized version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm

Joint probabilistic data association filter with unknown detection probability and clutter rate

This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of application

Information-theoretic joint probabilistic data association filter

This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback–Leibler divergence to approximate the posterior Gaussian mixture probability density function. Theoretical analysis of mean performance and error covariance performance with ideal detection probability is presented to provide insights of the proposed approach. Extensive empirical simulations are undertaken to validate the performance of the proposed multitarget tracking algorithm

Distributive JPDAF for multi-target tracking in wireless sensor networks

Copyright © 2005 IEEEIn this paper we present the development of a distributive joint probabilistic data association filter (JPDAF) for multi-target tracking in wireless sensor networks. We adopt sequential Monte Carlo (SMC) method to implement the JPDAF, and use Gaussian mixture model (GMM) to develop the distributive JPDAF. Simulation results are also provided.Hui Ma, Brian W.-H. N

A new formulation of probabilistic data association filter

In this paper, we show that the actual PDAF state covariance equation is wrong in no measurement assumption . A new formulatio n was developped, introducing a corrective term in the state covariance update equation . The computing cost is the same and th e track performances are enhanced specially for high false alarm probability .Cet article montre que les équations du Filtre à Association Probabiliste de Données (PDAF) utilisées jusqu'à présent ne tennaient pas compte de l'influence sur le vecteur d'état de l'absence d'observation utile ou d'observation validée. De nouvelles équations ont été développées introduisant un terme correctif au niveau de la mise à jour de la matrice de covariance de l'état prédit. Cette modification, qui n'entraîne aucune charge de calcul supplémentaire, est particulièrement efficace lorsque le taux de fausses alarmes est élevé. Les performances sont identiques à celles du PDAF lorsque le taux de fausses alarmes est faible

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Gating Techniques for Rao-Blackwellized Monte Carlo Data Association Filter

This paper studies the Rao-Blackwellized Monte Carlo data association (RBMCDA) filter for multiple target tracking. The elliptical gating strategies are redesigned and incorporated into the framework of the RBMCDA filter. The obvious benefit is the reduction of the time cost because the data association procedure can be carried out with less validated measurements. In addition, the overlapped parts of the neighboring validation regions are divided into several separated subregions according to the possible origins of the validated measurements. In these subregions, the measurement uncertainties can be taken into account more reasonably than those of the simple elliptical gate. This would help to achieve higher tracking ability of the RBMCDA algorithm by a better association prior approximation. Simulation results are provided to show the effectiveness of the proposed gating techniques