Branch and bound method for sensor scheduling in discrete time

Feng, Zhi; Teo, Kok Lay; Zhao, Yining

doi:10.3934/jimo.2005.1.499

Cited by 21 publications

(13 citation statements)

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“…Note that f k À Y uk is a positive semi-definite matrix according to Theorem 3 of Feng et al (2005) because Y uk is a symmetry matrix, and then it is easy to prove using Theorem 2 and Lemma 1 of Feng et al (2005) that L 1 (C k:H À1 ) is the lower bound of long-term accuracy reward of the sequence C k:H À1 .…”

Section: Branch and Bound Methods For Decision Tree Problemmentioning

confidence: 99%

Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP

Shan¹,

Zhang

2016

Transactions of the Institute of Measurement and Control

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This paper addresses a non-myopic sensor-scheduling problem of how to select and assign active sensors for trading off the tracking accuracy and the radiation risk, where the radiation risk is incurred by the fact that the emission energy originating from active sensors for target tracking can be intercepted by the enemy target. This problem is formulated as a mixed partially observable Markov decision process (POMDP) composed of a continuousstate POMDP for target tracking and a discrete-state POMDP for emission control. Based on the idea of foresight optimization, the long-term accuracy reward is evaluated by the combination of unscented transformation sampling and Kalman filtering, whereas the long-term radiation cost is derived from hidden Markov model filter. Because the problem can be converted into a decision tree, a branch and bound algorithm is developed for problem solution. A simulation example illustrates the effectiveness of our approach. KeywordsBranch and bound algorithm, foresight optimization, non-myopic sensor scheduling, partially observable Markov decision process, unscented transformation sampling.

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Section: Branch and Bound Methods For Decision Tree Problemmentioning

confidence: 99%

Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP

Shan¹,

Zhang

2016

Transactions of the Institute of Measurement and Control

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“…Impulsive systems can be viewed as a subclass of hybrid systems in which the states behave according to a continuous-time dynamics and which are also subjected to time-driven or event-driven impulsive effects where the states of the system are changed instantaneously. These systems can be used for modeling biological systems [4], intelligent vehilce/highway systems [5] and satellite rendezvous [6]. In the past few years research has been conducted on stability [7], controllability and observability of these systems [8], [9], to just name a few.…”

Section: Fault Detection and Isolation Of Linear Impulsive Systemsmentioning

confidence: 99%

“…In particular, a predictive control method [3], branch and bound methods [4], [5], a stochastic method [6], a dynamic programming method [7], and a continuous-time method [8] have been proposed for sensor scheduling. They assume that sensor characteristics are different from each other, which means that each sensor observes a distinct state or the covariance matrices in the sensor model are different from each other.…”

Section: Technical Notes and Correspondence I Introductionmentioning

confidence: 99%

Fast Sensor Scheduling for Spatially Distributed Sensors

Arai

Iwatani

Hashimoto

2011

IEEE Trans. Automat. Contr.

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This technical note addresses a sensor scheduling problem for a class of networked sensor systems whose sensors are spatially distributed and measurements are influenced by state dependent noise. A concept of sensor types is introduced without loss of generality to reduce combinatorial complexity. The computation time of the proposed algorithm increases exponentially with the number of the sensor types, while that of standard algorithms is exponential in the number of the sensors. This confirms high speed performance of the proposed algorithm, since the number of sensor types is usually far less than the number of sensors.Index Terms-Communication networks, control over communications, sensor networks, sensor scheduling.

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“…In particular, a predictive control method [3], a branch and bound method [5], and a sub-optimal method based on relaxed dynamic programming [1] have been proposed for sensor scheduling. In addition, a sensor scheduling strategy for continuous-time systems has been provided in [9].…”

Section: Introductionmentioning

confidence: 99%

Fast and optimal sensor scheduling for networked sensor systems

Arai

Iwatani

Hashimoto

2008

2008 47th IEEE Conference on Decision and Control

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This paper addresses a sensor scheduling problem for a class of networked sensor systems whose sensors are spatially distributed and measurements are influenced by state dependent noise. Sensor scheduling is required to achieve power saving since each sensor operates with a battery power source. The scheduling problem is formulated as a model predictive control problem with single sensor measurement per time. It is assumed that all sensors have state dependent noise and have the same characteristics, which follows from the properties of networked sensor systems. We propose a fast and optimal sensor scheduling algorithm for a class of networked sensor systems. Computation time of the proposed algorithm is proportional to the number of sensors and does not depend on the prediction horizon. In addition, we provide a fast sensor scheduling algorithm for a general class of systems by using a linear approximation of the sensor model.

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Branch and bound method for sensor scheduling in discrete time

Cited by 21 publications

References 0 publications

Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP

Non-myopic sensor scheduling for low radiation risk tracking using mixed POMDP

Fast Sensor Scheduling for Spatially Distributed Sensors

Fast and optimal sensor scheduling for networked sensor systems

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