Name
Abstract | ||
|---|---|---|
The Histogram PMHT is a parametric track-before-detect method that has good detection performance and low computation complexity. However, the method assumes a known clutter distribution. This paper introduces a method for learning a non-uniform clutter map where the map is represented as a mixture of parameterised components. The modified Histogram PMHT is compared with the uniform clutter model using airborne imagery. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SSP.2014.6884598 | Statistical Signal Processing |
Keywords | Field | DocType |
Gaussian processes,computational complexity,image representation,mixture models,object detection,probability,target tracking,airborne imagery,clutter distribution,histogram PMHT,low computation complexity,nonuniform clutter map representation,parametric track-before-detect method,probabilistic multihypothesis tracker | Histogram,Computer vision,Pattern recognition,Clutter,Artificial intelligence,Constant false alarm rate,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel J. Davey | 1 | 103 | 10.27 |
| Han X. Vu | 2 | 12 | 1.69 |
| Arulampalam, S. | 3 | 0 | 0.34 |
| Fletcher, F. | 4 | 0 | 0.34 |
| Cheng-chew Lim | 5 | 963 | 65.94 |