Nonlinear processing of radar data for landmine detection

Elizabeth E. Bartosz; Keith DeJong; Herbert A. Duvoisin; Geoff Z. Solomon; William J. Steinway; Albert Warren

doi:10.1117/12.544312

21 September 2004 Nonlinear processing of radar data for landmine detection

Elizabeth E. Bartosz, Keith DeJong, Herbert A. Duvoisin, Geoff Z. Solomon, William J. Steinway, Albert Warren

Proceedings Volume 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX; (2004) https://doi.org/10.1117/12.544312
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Outstanding landmine detection has been achieved by the Handheld Standoff Mine Detection System (HSTAMIDS system) in government-run field tests. The use of anomaly detection using principal component analysis (PCA) on the return of ground penetrating radar (GPR) coupled with metal detection is the key to the success of the HSTAMIDS-like system algorithms. Indications of nonlinearities and asymmetries in Humanitarian Demining (HD) data point to modifications to the current PCA algorithm that might prove beneficial. Asymmetries in the distribution of PCA projections of field data have been quantified in Humanitarian Demining (HD) data. An initial correction for the observed asymmetries has improved the False Alarm Rate (FAR) on this data.

Citation Download Citation

Elizabeth E. Bartosz, Keith DeJong, Herbert A. Duvoisin, Geoff Z. Solomon, William J. Steinway, and Albert Warren "Nonlinear processing of radar data for landmine detection", Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); https://doi.org/10.1117/12.544312

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