Share Email Print
cover

Proceedings Paper

Fusion of multiple algorithms for detecting buried objects using fuzzy inference
Author(s): Amine Khalifa; Hichem Frigui
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

We present a fusion method, based on fuzzy inference, for detecting buried objects using ground-penetrating radar (GPR) data. The GPR sensor generates 3-dimensional data that correspond to depth, down-track, and cross-track. Most discrimination algorithms process only 2-D slices of the 3-D cube: (down-track, depth) or (cross-track, depth). The performance of the down-track and cross-track discrimination algorithms can vary significantly depending on the target shape, burial orientation, and other environmental conditions. In some cases, these algorithms can provide complementary evidence, while in other cases they provide contradicting evidence. Thus, effective fusion of these algorithms can achieve higher probability of detection with fewer false alarms. The proposed fusion method is capable of learning meaningful and simple fuzzy rules for different regions of the input space, generated by partial confidence values of the different discriminators as well as additional background information. To learn the rules, first, the input space is partitioned to identify local contexts. Second, input membership functions are learned based on the distribution of the partial confidence values of the individual discriminators within each context. Third, output membership functions are generated by considering the relative numbers of targets and non-targets within each context. Finally, the input and output membership functions are combined into a Mamdani-type fuzzy inference system. The output of the learning process is a fuzzy rule base adapted to different contexts. Results on large and diverse GPR data collections show that the proposed fusion can identify local, simple, and meaningful rules capable of non-linear fusion of different discriminators. We also show that the proposed fuzzy inference outperforms commonly used fusion methods.

Paper Details

Date Published: 29 May 2014
PDF: 10 pages
Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 90720V (29 May 2014); doi: 10.1117/12.2051217
Show Author Affiliations
Amine Khalifa, Univ. of Louisville (United States)
Hichem Frigui, Univ. of Louisville (United States)


Published in SPIE Proceedings Vol. 9072:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX
Steven S. Bishop; Jason C. Isaacs, Editor(s)

© SPIE. Terms of Use
Back to Top