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This paper presents a new scheme for radar target recognition, in which we fuse sequential radar echoes from multiple target-radar aspect angles. The nonstationary hidden Markov model (NSHMM) is employed to characterize the sequential information contained in multiaspect radar echoes. Features from echoes are extracted via the multirelax algorithm, and moments are used to reduce the extracted-feature dimensionality. The proposed NSHMM has many parameters and states to be estimated, so the Markov chain Monte Carlo sampling algorithm is adopted. Finally, this new scheme is demonstrated with experiments on inverse synthetic aperture radar data
Published in:
Signal Processing, IEEE Transactions on
(Volume:55
,
Issue:
5
)
Date of Publication: May 2007
- Page(s):
- 2203 - 2214
- ISSN :
- 1053-587X
- INSPEC Accession Number:
- 9427744
- Digital Object Identifier :
- 10.1109/TSP.2007.892708
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 23 April 2007
- Issue Date :
- May 2007
- Sponsored by :
- IEEE Signal Processing Society
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