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.
HRR Automatic Target Recognition from Superresolution Scattering Center Features
IEEE Transactions on Aerospace and Electronic Systems ; 45 , 4 ; 1512-1524
2009-10-01
1560270 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
HRR Automatic Target Recognition from Superresolution Scattering Center Features
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