Approximate optimal periodic scheduling of multiple sensors with constraints

Authors:
Dawei Shi;Tongwen Chen
Affiliations:
-;-
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 8
Cited 0

On the time-varying Riccati difference equation of optimal filtering

SIAM Journal on Control and Optimization
Uniform observability and exponential convergence rate of the Kalman filter for the FIR deconvolution problem

Signal Processing - Special section on digital signal processing for multimedia communications and services
Brief paper: Communication and control co-design for networked control systems

Automatica (Journal of IFAC)
Periodic Systems: Filtering and Control

Periodic Systems: Filtering and Control
Sensor selection via convex optimization

IEEE Transactions on Signal Processing
Fast sensor scheduling for spatially distributed heterogeneous sensors

ACC'09 Proceedings of the 2009 conference on American Control Conference
Sensor selection strategies for state estimation in energy constrained wireless sensor networks

Automatica (Journal of IFAC)
On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.14

Visualization

Abstract

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.