Adaptive underwater target classification using a K-NN-based multi-aspect decision feedback unit

Mahmood R. Azimi-Sadjadi; Arta A. Jamshidi; Gerald J. Dobeck; Khashayar Khorasani

doi:10.1117/12.479117

13 August 2002 Adaptive underwater target classification using a K-NN-based multi-aspect decision feedback unit

Mahmood R. Azimi-Sadjadi, Arta A. Jamshidi, Gerald J. Dobeck, Khashayar Khorasani

Proceedings Volume 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII; (2002) https://doi.org/10.1117/12.479117
Event: AeroSense 2002, 2002, Orlando, FL, United States

Abstract

This paper presents a new sequential decision feedback approach for pattern classification in a changing environment. An adaptive classification system is developed that uses the decisions of multiple aspects that may not be separated uniformly. A tap delay mechanism is used to impact the final decision at the current aspect of the object. This system minimizes the error of the classifier while it maps the new feature vector to a familiar feature space for the classifier. The test results on an acoustic backscattered data set collected from six different objects: two mine-like and four non-mine-like at 72 aspect angles with 5 degrees of separation and with varying signal-to-reverberation ratio (SRR) from 4 to 16 dB are presented.

Citation Download Citation

Mahmood R. Azimi-Sadjadi, Arta A. Jamshidi, Gerald J. Dobeck, and Khashayar Khorasani "Adaptive underwater target classification using a K-NN-based multi-aspect decision feedback unit", Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); https://doi.org/10.1117/12.479117

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