Stochastic sensor scheduling via distributed convex optimization
-
Chong Li [1] ; Nicola Elia [1]
-
[1] Iowa State University
Iowa State University
Township of Franklin, Estados Unidos
-
- Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 58, 2015, págs. 173-182
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
-
Resumen
In this paper, we propose a stochastic scheduling strategy for estimating the states of NN discrete-time linear time invariant (DTLTI) dynamic systems, where only one system can be observed by the sensor at each time instant due to practical resource constraints. The idea of our stochastic strategy is that a system is randomly selected for observation at each time instant according to a pre-assigned probability distribution. We aim to find the optimal pre-assigned probability in order to minimize the maximal estimate error covariance among dynamic systems. We first show that under mild conditions, the stochastic scheduling problem gives an upper bound on the performance of the optimal sensor selection problem, notoriously difficult to solve. We next relax the stochastic scheduling problem into a tractable suboptimal quasi-convex form. We then show that the new problem can be decomposed into coupled small convex optimization problems, and it can be solved in a distributed fashion. Finally, for scheduling implementation, we propose centralized and distributed deterministic scheduling strategies based on the optimal stochastic solution and provide simulation examples.
