Name
Abstract | ||
|---|---|---|
We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jump Markov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performance of these filters is compared with that of standard interacting multiple model (IMM)-based trackers such as IMM-EKF and IMM-UKF for three separate cases: (i) single-sensor case, (ii) multisensor case, and (iii) tracking with hard constraints. A conservative CRLB applicable for this problem is also derived and compared with the RMS error performance of the filters. The results confirm the superiority of the PFs for this difficult nonlinear tracking problem. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1155/S1110865704405095 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
particle filter | Cramér–Rao bound,Computer vision,BitTorrent tracker,Nonlinear system,Control theory,Computer science,Particle filter,Markov chain,Bearing (mechanical),Artificial intelligence,Root-mean-square deviation,Jump | Journal |
Volume | Issue | ISSN |
2004, | 15 | 1687-6180 |
Citations | PageRank | References |
43 | 2.52 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanjeev Arulampalam | 1 | 142 | 19.13 |
| Branko Ristic | 2 | 711 | 62.37 |
| Neil J. Gordon | 3 | 175 | 13.61 |
| Todd Mansell | 4 | 46 | 4.34 |