New Efficient Schemes for Adaptive Selection of the Update Time in the IMMJPDAF

Benoudnine, H.; Keche, Mokhtar; Ouamri, Abdelaziz; Woolfson, M.S.

doi:10.1109/taes.2012.6129630

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…As a result, the tracker may confuse two targets in close interaction, eventually causing a tracker switching problem. Other advances of such filtering approaches to multiple target tracking include multiple detection JPDAF (MD-JPDAF) [13] and interacting multiple model JPDAF (IMM-JPDAF) [14].…”

Section: A Motivationmentioning

confidence: 99%

“…is the association probability that the target is associated with the measurement and is the association probability that the target remains undetected. With prediction and likelihood functions defined by (12) and (13), respectively, the JPDAF estimates the posterior probability of the state of target (14) The association probability can be defined as (27) in [10]…”

Section: A the Jpdaf Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Target Tracking and Occlusion Handling With Learned Variational Bayesian Clusters and a Social Force Model

ur-Rehman

Naqvi

Mihaylova

et al. 2016

IEEE Trans. Signal Process.

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This paper considers the problem of multiple human target tracking in a sequence of video data. A solution is proposed which is able to deal with the challenges of a varying number of targets, interactions, and when every target gives rise to multiple measurements. The developed novel algorithm comprises variational Bayesian clustering combined with a social force model, integrated within a particle filter with an enhanced prediction step. It performs measurement-to-target association by automatically detecting the measurement relevance. The performance of the developed algorithm is evaluated over several sequences from publicly available data sets: AV16.3, CAVIAR, and PETS2006, which demonstrates that the proposed algorithm successfully initializes and tracks a variable number of targets in the presence of complex occlusions. A comparison with state-of-the-art techniques due to Khan et al., Laet et al., and Czyz et al. shows improved tracking performance.

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Section: A Motivationmentioning

confidence: 99%

Section: A the Jpdaf Frameworkmentioning

confidence: 99%

Multi-Target Tracking and Occlusion Handling With Learned Variational Bayesian Clusters and a Social Force Model

ur-Rehman

Naqvi

Mihaylova

et al. 2016

IEEE Trans. Signal Process.

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“…Therefore, orbital maneuver and object correlation are feasible between two arbitrarily observed orbits. To solve the aforementioned problem of object correlation and maneuver detection of the uncorrelated tracks, the multiple hypothesis testing (MHT) method is a reasonable and well-known solution (Benoudnine et al 2012). Let Z = {Z 0 , Z 1 } denote the observed orbital elements, where…”

Section: Principle Of Maneuver Detection and Object Correlationmentioning

confidence: 99%

An object correlation and maneuver detection approach for space surveillance

Jian

Xin

et al. 2012

Res. Astron. Astrophys.

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Object correlation and maneuver detection are persistent problems in space surveillance and maintenance of a space object catalog. We integrate these two problems into one interrelated problem, and consider them simultaneously under a scenario where space objects only perform a single in-track orbital maneuver during the time intervals between observations. We mathematically formulate this integrated scenario as a maximum a posteriori (MAP) estimation. In this work, we propose a novel approach to solve the MAP estimation. More precisely, the corresponding posterior probability of an orbital maneuver and a joint association event can be approximated by the Joint Probabilistic Data Association (JPDA) algorithm. Subsequently, the maneuvering parameters are estimated by optimally solving the constrained non-linear least squares iterative process based on the second-order cone programming (SOCP) algorithm. The desired solution is derived according to the MAP criterions. The performance and advantages of the proposed approach have been shown by both theoretical analysis and simulation results. We hope that our work will stimulate future work on space surveillance and maintenance of a space object catalog.

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“…Synthesizing the problems of manoeuvering and association, Shalom et al proposed the IMMPDA algorithm [6]. Benoudnine et al studied the method of combining IMM with JPDA [7]. Ma et al introduced Feature Aided Tracking into GPDA [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved IMM-GPDA Tracking Algorithm

Xie

Wang

2023

J. Phys.: Conf. Ser.

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To improve the accuracy and efficiency of multi-maneuvering target tracking in dense clutter environments, an improved interactive multi-model generalized probabilistic data association (IMM-GPDA) algorithm is proposed in this paper. The algorithm introduces Doppler information into the measurement data. Firstly, the measurement data is pre-processed by double-threshold regional density clutter elimination, and then the pre-processed data is put into the improved IMM-GPDA algorithm with square-root cubature Kalman filter (SRCKF) and model transition probability adaptation for interaction, association, and filtering. Simulation results show that the algorithm has advanced accuracy, instantaneity, and robustness in tracking environments with multi-maneuvering targets and dense clutters.

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New Efficient Schemes for Adaptive Selection of the Update Time in the IMMJPDAF

Cited by 7 publications

References 25 publications

Multi-Target Tracking and Occlusion Handling With Learned Variational Bayesian Clusters and a Social Force Model

Multi-Target Tracking and Occlusion Handling With Learned Variational Bayesian Clusters and a Social Force Model

An object correlation and maneuver detection approach for space surveillance

An Improved IMM-GPDA Tracking Algorithm

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