Bayesian Approach to Multiple-Target Tracking*

Stone, Lawrence

doi:10.1201/9781420053098.ch12

Cited by 59 publications

(89 citation statements)

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“…Similar models have been used in monopulse radar applications [29] and to model clutter and target returns in turbulent environments [30]. In the case of nonthresholded measurements, and the cell measurement likelihood is, for (6) where is referred to as the signal-to-noise ratio (SNR). For thresholded measurements, with corresponding to a target detection in the th cell at time .…”

Section: Notation and Modelingmentioning

confidence: 99%

“…A more promising approach is to adopt a fully Bayesian perspective where the quantity of interest is a Markov process, the multitarget state, which is the concatenation of several individual target states. Stone [6] develops a mathematical theory of MTT from a Bayesian point of view. Srivistava, Miller [7], Kastella [8], and Mahler [9] also did early work in this area.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

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Section: Notation and Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

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“…Both of them are extensions of the Kalman filter and they are applicable to deal with non-linear systems.Also the Grid-based methods [4] provide another approach for approximating non-linear probability density functions, although they rapidly become computationally difficult to deal with in high dimensions.…”

Section: Techniquesmentioning

confidence: 99%

Multi-modal mobile sensor data fusion for autonomous robot mapping problem

Kassem

Shehata

Morgan

2016

MATEC Web of Conferences

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Abstract-Perception is the first step for a mobile robot to perform any task and for it to gain perception mobile robots use sensors to measure the states which represent the surrounding environment. Sensors measurements are always combined with some sort of uncertainty and noise. Which can make the system very unstable and unreliable. In order to get better readings we can always use better types of sensors where we come to a trade off between price and quality. And that's why our proposed approach to solve this problem was to use data fusion techniques to eliminate the noise and reduce the uncertainty in the readings. The topic of data fusion has been under extensive research in the past decade many approaches had been suggested and yet the research on data fusion is increasing and this because of its importance and applications. This study discuss the use of probabilistic data fusion techniques to reduce the uncertainty and eliminate the noise of the measurements from range finder active sensors to improve the task of mapping for mobile robots. The data fusion methods used were Kalman filter and Bayes filter.

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“…radars) is a well-studied problem (e.g. [95][96][97][98]). Many of the basic problems, such as trajectory modelling, nonlinear filtering and data association, carry over to the problem of tracking in WSNs with a large number of sensors.…”

Section: Trackingmentioning

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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Bayesian Approach to Multiple-Target Tracking*

Cited by 59 publications

References 0 publications

A Bayesian Approach to Multiple Target Detection and Tracking

A Bayesian Approach to Multiple Target Detection and Tracking

Multi-modal mobile sensor data fusion for autonomous robot mapping problem

Distributed inference in wireless sensor networks

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