Abstract
To monitor human activities, such as pedestrian motion and detection of intruders in a secure region we are widely using the Unattended ground sensors (UGS). The efficiency of UGSsystems is often limited by high false alarm rates, possibly dueto inadequacies of the underlying algorithms and limitationsof onboard computation. In this regard, this paper presents awavelet-based method for target detection and classification. Theproposed method has been validated on data sets of seismic andPassive Infrared sensors (PIR) for target detection and classification,as well as for payload and movement type identification ofthe targets. The proposed method has the advantages of fastexecution in less time and low memory requirements and is potentiallywell- suited for real-time implementation with onboard UGSsystems.
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