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HRR Automatic Target Recognition from Superresolution Scattering Center Features

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3 Author(s)
Gudnason, J. ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Jingjing Cui ; Brookes, M.

The work presented here introduces a procedure for the automatic recognition of ground-based targets from high range resolution (HRR) profile sequences that may be obtained from a synthetic aperture radar (SAR) platform. The procedure incorporates an adaptive target mask and uses a superresolution algorithm to identify the cross-range positions of target scattering centers. These are used to generate a pseudoimage of the target whose low-order discrete cosine transform coefficients form the recognizer feature vector. Within the recognizer, the states of a hidden Markov model (HMM) are used to represent the target orientation and a Gaussian mixture model is used for the feature vector distribution. In a closed-set identification experiment, the misclassification rate for ten MSTAR targets was 2.8%. Also presented are results from open-set experiments and investigates the effect on recognizer performance of variations in feature vector dimension, azimuth aperture, and target variants.

Published in:

Aerospace and Electronic Systems, IEEE Transactions on  (Volume:45 ,  Issue: 4 )

Date of Publication:

Oct. 2009

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