Multi-target tracking using joint probabilistic data association

Fortmann, T.; Bar‐Shalom, Yaakov; Scheffe, M.

doi:10.1109/cdc.1980.271915

Cited by 249 publications

(148 citation statements)

References 3 publications

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“…where the terms have the same meaning as explained in (8) and (10). And p j terms are analogous p i terms.…”

Section: Factor Formulationsmentioning

confidence: 93%

“…But for tracking multiple targets, additional task of data association is required. Various solutions exist for the same like Joint Probabilistic Data Association (JPDA) [10] by Fortmann et al, Probability Hypothesis Density (PHD) filter [11] by Mahler and Multi Hypothesis Tracking [12] by Reid. And there exist various improvements of these solutions like [13], [14] and [15].…”

Section: A Overviewmentioning

confidence: 99%

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Graph based vehicle infrastructure cooperative localization

Gulati

Zhang

Malovetz

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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Abstract-This paper presents a novel and an improved approach for estimating the position of a vehicle using vehicleinfrastructure cooperative localization. In our previous work we presented a Factor Graph based solution which added the topology (inter-vehicle distance) as a constraint while localizing the vehicle using data from sensors from both inside and outside the vehicle. This paper extends the work by reducing the error in calculating the precision of the position by almost 27% in the best case and lowering the computational time by at least 50% over our previously proposed solution. This is achieved by modifying current topology constraints to be also dependent on the previous state estimate. The proposed solution remains scalable for many vehicles without increasing the execution complexity. Finally, simulations indicate that incorporating the new topology information via Factor Graphs can improve performance over the traditional, state of the art, Kalman Filter approach.

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“…where the terms have the same meaning as explained in (8) and (10). And p j terms are analogous p i terms.…”

Section: Factor Formulationsmentioning

confidence: 93%

Section: A Overviewmentioning

confidence: 99%

Graph based vehicle infrastructure cooperative localization

Gulati

Zhang

Malovetz

et al. 2017

2017 20th International Conference on Information Fusion (Fusion)

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show abstract

“…Comparing this to (8), it is clear that the factor q 2 reduces the covariance improvement due to the term WSW*: the smaller q 2 is, the greater the degradation. Thus, q 2 provides a useful measure of tracking performance as a function of PD and C.…”

Section: Mk-mentioning

confidence: 99%

Detection thresholds for multi-target tracking in clutter

Fortmann

Bar‐Shalom

Scheffe

et al. 1981

1981 20th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes

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“…The first is an approach proposed by Fortmann et al [110] called joint probabilistic data association (JPDA) in which the belief state of each track is updated using a weighted combination of all the observations. The weights are chosen based on estimates of the probabilities that a given observation is associated with a given track, while ensuring that mutual exclusion of the tracks is maintained at all times.…”

Section: (B) Multi-object Tracking and Data Associationmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

110

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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Multi-target tracking using joint probabilistic data association

Cited by 249 publications

References 3 publications

Graph based vehicle infrastructure cooperative localization

Graph based vehicle infrastructure cooperative localization

Detection thresholds for multi-target tracking in clutter

Distributed inference in wireless sensor networks

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