Probabilistic SVM for open set automatic target recognition on high range resolution radar data

Jason D. Roos; Arnab K. Shaw

doi:10.1117/12.2262840

1 May 2017 Probabilistic SVM for open set automatic target recognition on high range resolution radar data

Jason D. Roos, Arnab K. Shaw

Author Affiliations +

Proceedings Volume 10202, Automatic Target Recognition XXVII; 102020B (2017) https://doi.org/10.1117/12.2262840
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

The Eigen-Template (ET) based closed-set feature extraction approach is extended to an open-set HRR-ATR framework to develop an Open Set Probabilistic Support Vector Machine (OSP-SVM) classifier. The proposed ET-OSP-SVM is shown to perform open set ATR on HRR data with 80% PCC for a 4-class MSTAR dataset.

Conference Presentation

Citation Download Citation

Jason D. Roos and Arnab K. Shaw "Probabilistic SVM for open set automatic target recognition on high range resolution radar data", Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020B (1 May 2017); https://doi.org/10.1117/12.2262840

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