Name
Title | ||
|---|---|---|
Point-Based Value Iteration and Approximately Optimal Dynamic Sensor Selection for Linear-Gaussian Processes |
Abstract | ||
|---|---|---|
The problem of synthesizing an optimal sensor selection policy is pertinent to a variety of engineering applications ranging from event detection to autonomous navigation. We consider such a synthesis problem in the context of linear-Gaussian systems over an infinite time horizon with a discounted cost criterion. We formulate this problem in terms of a value iteration over the continuous space of covariance matrices. To obtain a computationally tractable solution, we subsequently formulate an approximate sensor selection problem, which is solvable through a point-based value iteration over a finite "mesh" of covariance matrices with a user-defined bounded trace. We provide theoretical guarantees bounding the suboptimality of the sensor selection policies synthesized through this method and provide numerical examples comparing them to known results. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/ACC50511.2021.9482822 | 2021 AMERICAN CONTROL CONFERENCE (ACC) |
Keywords | DocType | Volume |
Estimation, Kalman filtering, Sensor networks | Conference | 5 |
ISSN | Citations | PageRank |
0743-1619 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Hibbard | 1 | 0 | 0.68 |
| Kirsten Tuggle | 2 | 0 | 0.34 |
| Takashi Tanaka | 3 | 34 | 12.22 |