Analysis of Dynamic Sensor Coverage Problem using Kalman Filters for Estimation
| Authors: | Tiwari Abhishek, California Institute of technology, United States
Jun Myungsoo, GERC, University of Florida, United States
Jeffcoat David E., AFRL, Munitions Directorate, Eglin Air Force Base, United States
Murray Richard M., California Institute of Technology, United States |
|---|
| Topic: | 1.4 Stochastic Systems |
|---|
| Session: | Control, Estimation and Analysis of Stochastic Systems |
|---|
| Keywords: | Dynamic Sensor Coverage, Markov Chain, Kalman Filter |
|---|
Abstract
We introduce a theoretical framework for the dynamic sensor coverage problem for the case with multiple discrete time linear stochastic systems placed at spacially separate locations. The objective is to keep an appreciable estimate of the states of the systems at all times by deploying a few mobile sensors. The sensors are assumed to have a limited range and they implement a Kalman filter to estimate the states of all the systems. In this paper we present results for a single sensor executing two different random motion strategies. Under the first strategy the sensor motion is an independent and identically distributed random process and a discrete time discrete state ergodic Markov chain under the second strategy. For both these strategies we give conditions under which a single sensor fails or succeeds to solve the dynamic coverage problem. We also demonstrate that the conditions for the first strategy are a special case of the main result for the second strategy.
