Name
Title | ||
|---|---|---|
A particle filter for tracking two closely spaced objects using monopulse radar channel signals |
Abstract | ||
|---|---|---|
For the case of a single resolved target, monopulse-based radar sub-beam angle and sub-bin range measurements carry errors that are approximately Gaussian with known covariances, and hence, a tracker that uses them can be Kalman based. However, the errors accruing from extracting measurements for multiple unresolved targets are not Gaussian. We therefore submit that to track such targets, it is worth the effort to apply a nonlinear (non-Kalman) filter. Specifically, in this letter, we propose a particle filter that operates directly on the monopulse sum/difference data for two unresolved targets. Significant performance improvements are seen versus a scheme in which signal processing (measurement extraction from the monopulse data) and tracking (target state estimation from the extracted measurements) are separated. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/LSP.2006.871714 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
particle filtering (numerical methods),radar signal processing,radar tracking,monopulse sum-difference data,monopulse-based radar channel,particle filter,signal processing,subbin range measurement,tracking,unresolved target,Monopulse radar,particle filter,tracking,unresolved targets | Radar,Signal processing,Computer vision,Radar tracker,Amplitude-Comparison Monopulse,Particle filter,Kalman filter,Gaussian,Artificial intelligence,Mathematics,Monopulse radar | Journal |
Volume | Issue | ISSN |
13 | 6 | 1070-9908 |
Citations | PageRank | References |
3 | 0.51 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isaac, A. | 1 | 3 | 0.51 |
| Xin Zhang | 2 | 34 | 4.59 |
| Peter Willett | 3 | 1962 | 224.14 |
| Yaakov Bar-Shalom | 4 | 460 | 99.56 |