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Proceedings Paper

Enhanced ATR algorithm for high resolution multi-band sonar imagery
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Paper Abstract

An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering (SACF), normalization, detection, data regularization, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. A new improvement was made to the processing string, data regularization, which entails computing the input data mean, clipping the data to a multiple of its mean and scaling it, prior to feature extraction. The classified objects of 3 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution three-frequency band sonar imagery. The ATR processing strings were individually tuned to the corresponding three-frequency band data, making use of the new processing improvement, data regularization, which resulted in a 3:1 reduction in false alarms. Two significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the ATR processing strings outperforms baseline summing and single-stage Volterra feature LLRT algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.

Paper Details

Date Published: 29 April 2008
PDF: 10 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 69530H (29 April 2008); doi: 10.1117/12.773304
Show Author Affiliations
Tom Aridgides, Lockheed Martin (United States)
Manuel Fernández, Lockheed Martin (United States)


Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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