A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

Abstract

Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic sensors and showed that the classification performance varies with the type of road due to the distinct spectral characteristics of the medium. Our proposed classifier fuses time and frequency-domain features and acoustic and seismic modalities to achieve the highest classification rate of 98.6% using a relatively small number of features.