Name
Title | ||
|---|---|---|
Distributed Adaptive State Estimation And Tracking Scheme For Nonlinear Systems Using Active Passive Sensor Networks |
Abstract | ||
|---|---|---|
This paper proposes a novel adaptive neural network (NN) based distributed state estimation scheme for a heterogeneous sensor network (HSN), to estimate the state vector of an unknown nonlinear process/target by using sensed output when the target input remains unknown. The active nodes in the HSN can sense the target output based on the detection range. By using a connected graph, the active nodes will communicate their estimated state vector from their adaptive NN observer to other passive nodes in the neighborhood that cannot sense the target, so that they can estimate the target state vector. Next, a subset of nodes in the HSN, referred to as the mobile nodes, track the moving target by using their estimated state information and a state feedback controller. For the communication topology considered, it is shown that the distributed state estimation, the NN observer weight estimation, and the tracking errors are uniformly ultimately bounded. Simulation results verify the theoretical claims. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/ACC45564.2020.9147258 | 2020 AMERICAN CONTROL CONFERENCE (ACC) |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akhilesh Raj | 1 | 0 | 0.34 |
| Sarangapani Jagannathan | 2 | 1136 | 94.89 |
| Tansel Yucelen | 3 | 177 | 34.55 |