Random Set Tracker Experiment on a Road Constrained Network with Resource Management

Witkoskie, James B.; Kuklinski, Walter S.; Theophanis, Stephen; Otero, Michael

doi:10.1109/icif.2006.301659

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…In [7], the posterior expected number of targets (PENT)-based reward function is given. PENT has been successfully applied in some applications [8,9]. In [10], the Cauchy-Schwarz divergence is used, and the analytical solution is also derived.…”

Section: Introductionmentioning

confidence: 99%

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Wang

Mei

Peng

et al. 2019

Entropy

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Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has been quite popular in recent years. The most influential change produced by digital twin is the ability to enable real-time dynamic interactions between the simulation world and the real world. Digital twin can be regarded as a paradigm by means of which selected online measurements are dynamically assimilated into the simulation world, with the running simulation model guiding the real world adaptively in reverse. By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed. Two key algorithms are proposed for supporting the digital twin-based framework. First, the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements with detection uncertainty, data association uncertainty, noise, and clutters. Second, the computation of the reward function by using the results of the proposed data-assimilation algorithm is introduced to find the optimal control action. The results of three groups of experiments successfully verify the feasibility and effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Wang

Mei

Peng

et al. 2019

Entropy

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“…In [137], Witkoskie et al considered the problem of multiple target road monitoring systems fixed at road intersections. An MDP resource management algorithm is developed to manage the sensor activation.…”

Section: B Traffic Management and Road Safetymentioning

confidence: 99%

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Alsheikh

Hoang

Niyato

et al. 2015

IEEE Commun. Surv. Tutorials

187

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Abstract-Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.

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“…In this paper, we explore applications of finite set statistics (FISST) to road constrained multiple target tracking. FISST is a generalization of the Bayesian equations to sets, equations (1) and (2). The probability density, called the global density, is defined on the possible number and locations of targets.…”

Section: Description Of the Random Set Trackermentioning

confidence: 99%

“…The random set approach, as developed herein, provides estimates of parameter values at each time step; it does not explicitly define tracks. It avoids the association ambiguity by statistically weighing all possible hypotheses and associations 2,3,4 . It also incorporates all possible target trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Random set tracking and entropy based control applied to distributed sensor networks

et al. 2007

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This paper describes an integrated approach to sensor fusion and resource management applicable to sensor networks. The sensor fusion and tracking algorithm is based on the theory of random sets. Tracking is herein considered to be the estimation of parameters in a state space such that for a given target certain components, e.g., position and velocity, are time varying and other components, e.g., identifying features, are stationary. The fusion algorithm provides at each time step the posterior probability density function, known as the global density, on the state space, and the control algorithm identifies the set of sensors that should be used at the next time step in order to minimize, subject to constraints, an approximation of the expected entropy of the global density. The random set approach to target tracking models association ambiguity by statistically weighing all possible hypotheses and associations. Computational complexity is managed by approximating the posterior Global Density using a Gaussian mixture density and using an approach based on the Kulbach-Leibler metric to limit the number of components in the Gaussian mixture representation. A closed form approximation of the expected entropy of the global density, expressed as a Gaussian mixture density, at the next time step for a given set of proposed measurements is developed. Optimal sensor selection involves a search over subsets of sensors, and the computational complexity of this search is managed by employing the Mobius transformation. Field and simulated data from a sensor network comprised of multiple range radars, and acoustic arrays, that measure angle of arrival, are used to demonstrate the approach to sensor fusion and resource management.

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Random Set Tracker Experiment on a Road Constrained Network with Resource Management

Cited by 8 publications

References 15 publications

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey

Random set tracking and entropy based control applied to distributed sensor networks

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