Name
Abstract | ||
|---|---|---|
We consider the problem of sensing a concealed or distant target by interrogation from multiple sensors situated on a single platform. The available actions that may be taken are selection of the next relative target-platform orientation and the next sensor to be deployed. The target is modeled in terms of a set of states, each state representing a contiguous set of target-sensor orientations over which the scattering physics is relatively stationary. The sequence of states sampled at multiple target-sensor orientations may be modeled as a Markov process. The sensor only has access to the scattered fields, without knowledge of the particular state being sampled, and, therefore, the problem is modeled as a partially observable Markov decision process (POMDP). The POMDP yields a policy, in which the belief state at any point is mapped to a corresponding action. The nonmyopic policy is compared to an approximate myopic approach, with example results presented for measured underwater acoustic scattering data |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TSP.2007.893747 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
nonmyopic multiaspect sensing,markov process,nonmyopic policy,distant target,belief state,next relative target-platform orientation,multiple target-sensor orientation,particular state,partially observable markov decision,next sensor,multiple sensor,contiguous set,entropy,partially observable markov decision process,sensor fusion,helium,hidden markov model,hidden markov models,physics,underwater acoustics,parameter estimation,markov processes | Mathematical optimization,Markov process,Observable,Partially observable Markov decision process,Markov model,Underwater acoustics,Markov decision process,Sensor fusion,Hidden Markov model,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 6 | 1053-587X |
Citations | PageRank | References |
18 | 0.95 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shihao Ji | 1 | 596 | 32.11 |
| Ronald Parr | 2 | 2428 | 186.85 |
| L. Carin | 3 | 4603 | 339.36 |