Fuzzy Logic for Unattended Ground Sensor Fusion
Abstract
This report summarizes our research for the year January 2005 to Decenber 2005. We have developed multi-category classifiers based on seismic data to classify heavy-tracked, light-tracked, heavy-wheeled and light-wheeled ground vehicles. We focused on data collected in the normal terrain. We also developed fusion algorithms for type-1 and type-2 Fuzzy logic Rule-Based Classifiers (FL-RBCs) based on the Choquet Fuzzy Integral (CFI). We conducted experiments to evaluate the performance of the classifiers and to evaluate the effectiveness of seismic data for dassification. We also conducted experiments to evaluate the performances of fused classifiers (both seismic and acoustic) and determine if performance could be improved. Our results show that binary classification between tracked and wheeled vehicles is effective using seismic data. However, due to the inherent unreliability of the seismic data, the performance of the classifiers based on seismic data was poor when compared to the performance of the classifiers based on acoustic data. Fusing the two classifiers also did not show any appreciable improvement in performance. We note that FL-RBCs performed better than the Bayesian equivalent for all the experiments. This shows that FL-RBCs are better suited to handle uncertainties in the data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2006
- Accession Number
- ADA444339
Entities
People
- Arjun Bharadwaj
- Jerry M. Mendel
Organizations
- University of Southern California