Approximate optimal periodic scheduling of multiple sensors with constraints

A constrained periodic multiple-sensor scheduling problem is considered in this paper. For each sensor, constraints on dwell time and activation times are imposed. At each time instant, only one sensor can update its measurement with the estimator; and the objective is to minimize the average state...

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Bibliographic Details
Published in:Automatica (Oxford) Vol. 49; no. 4; pp. 993 - 1000
Main Authors: Shi, Dawei, Chen, Tongwen
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.04.2013
Elsevier
Subjects:
Activation
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Approximation
Computer science; control theory; systems
Control theory. Systems
Dynamic programming
Exact sciences and technology
Kalman filters
Mathematical analysis
Mathematical programming
Modelling and identification
Operational research and scientific management
Operational research. Management science
Optimization
Riccati equations
Schedules
Scheduling
Scheduling, sequencing
Sensors
Theoretical computing
Traveling salesman problem
Wireless sensor networks
Wireless sensor networks
Traveling salesman problem
Dynamic programming
Kalman filters
Riccati equations
Error estimation
Kalman filter
Periodic scheduling
Travelling salesman problem
Updating
Observer
Switching time
System identification
State estimation
Estimation error
Multisensor
Stopping time
Bounded error
Measurement sensor
Computational complexity
Polynomial complexity
Upper bound
Approximation error
Wireless network
Objective function
Riccati equation
Sensor array
ISSN:0005-1098, 1873-2836
Online Access:Get full text
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Description
Summary:A constrained periodic multiple-sensor scheduling problem is considered in this paper. For each sensor, constraints on dwell time and activation times are imposed. At each time instant, only one sensor can update its measurement with the estimator; and the objective is to minimize the average state estimation error. An approximation framework is proposed to calculate the objective function, which transforms the original scheduling problem into an Approximate Optimal Scheduling Problem (AOSP). An upper bound on the approximation error is presented to evaluate the performance of the framework. To solve the AOSP, a necessary condition is first proposed on the optimal schedules. When no constraints on activation times exist, a dynamic programming based algorithm is devised to identify the optimal schedule with polynomial computational complexity. When activation-time constraints exist, we show that the AOSPs can be solved by solving traveling salesman problems. Examples are provided to illustrate the proposed results.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2013.01.024