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Content Provider | IEEE Xplore Digital Library |
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Author | Jingjing Cui Gudnason, J. Brookes, M. |
Copyright Year | 2005 |
Description | Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll., London, UK (Jingjing Cui; Gudnason, J.; Brookes, M.) |
Abstract | Automatic target recognition from high range resolution radar profiles remains an important and challenging problem. In this paper, we present a novel feature set for this task that combines a representation of the target's radar shadow with a noise-robust superresolution characterisation of the target scattering centres derived from the MUSIC algorithm. Using an HMM to represent aspect dependence, we demonstrate that the inclusion of the shadow features results in a significant improvement in recognition performance. We evaluate our proposed feature set on a closed-set identification task using targets from the MSTAR database and show that it results in lower recognition error rates than previously published methods using the same data. |
Sponsorship | IEEE Signal Processing Soc |
File Size | 262282 |
File Format | |
ISBN | 0780388747 |
ISSN | 15206149 |
DOI | 10.1109/ICASSP.2005.1416372 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-03-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Target recognition Radar scattering Radar imaging Signal resolution Hidden Markov models Synthetic aperture radar Radar cross section Noise robustness Spatial databases Image recognition |
Content Type | Text |
Resource Type | Article |
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