&lt;title&gt;Information-based sensor management for multitarget tracking&lt;/title&gt;

Kreucher, Chris; Kastella, Keith; Hero, Alfred O.

doi:10.1117/12.502699

Cited by 52 publications

(30 citation statements)

References 6 publications

(7 reference statements)

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“…In the sequel to this paper, 7 we show how this framework can be used to address the sensor management problem. We directly exploit the fact that the JMPD provides a representation that captures all of the information about the states of the targets.…”

Section: Discussionmentioning

confidence: 99%

<title>Tracking multiple targets using a particle filter representation of the joint multitarget probability density</title>

2003

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This paper addresses the problem of tracking multiple moving targets by estimating their joint multitarget probability density (JMPD). The JMPD technique is a Bayesian method for tracking multiple targets that allows nonlinear, non-Gaussian target motions and measurement to state coupling. JMPD simultaneously estimates both the target states and the number of targets. In this paper, we give a new grid-free implementation of JMPD based on particle filtering techniques and explore several particle proposal strategies, resampling techniques, and particle diversification methods. We report the effect of these techniques on tracker performance in terms of tracks lost, mean squared error, and computational burden.

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Section: Discussionmentioning

confidence: 99%

<title>Tracking multiple targets using a particle filter representation of the joint multitarget probability density</title>

2003

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“…The material in the first half of this section is largely drawn from a series of previously published papers [14], [40], [37]. It provides the background and notational conventions necessary before introducing the main topic of this paper, multiplatform sensor resource allocation via maximizing information flow.…”

Section: Information Theory For Single Sensor Managementmentioning

confidence: 99%

“…In other problems where the mode is to be estimated [36], we have used [x,ẋ, y,ẏ, m] and when the class is to be estimated [37] we have used [x,ẋ, y,ẏ, c].…”

Section: A Formulation Of the Jmpdmentioning

confidence: 99%

An Information-Based Approach to Sensor Management in Large Dynamic Networks

et al. 2007

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. An effective sensor management algorithm must combine prior knowledge, sensor models, environment models, and measurements to predict the best actions to take.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.Index Terms-multiplatform sensor management, information theory, particle filtering, joint multitarget probability density, multitarget tracking.

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“…This section describes a method of sensor management based on maximizing information flow [4], [5]. We focus here on the single platform case and describe the multiplatform case in the following section.…”

Section: Information Theoretic Sensor Managementmentioning

confidence: 99%

Network sensor management for tracking and localization

Hero

Kreucher

2007

2007 10th International Conference on Information Fusion

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.

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<title>Information-based sensor management for multitarget tracking</title>

Cited by 52 publications

References 6 publications

<title>Tracking multiple targets using a particle filter representation of the joint multitarget probability density</title>

<title>Tracking multiple targets using a particle filter representation of the joint multitarget probability density</title>

An Information-Based Approach to Sensor Management in Large Dynamic Networks

Network sensor management for tracking and localization

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