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Adaptive and Learning Algorithms for Seismic Detection of Personnel

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1 Author(s)
C. H. Chen ; Department of Electrical Engineering, Southeastern Massachusetts University, North Dartmouth, MA 02747.

This correspondence is concerned with adaptive digital processing to extract impulse-like signal features from the correlated background noise for detection of intruders with the seismic sensor data. Both the adaptive digital filtering and the adaptive Kalman filtering methods are developed and shown to perform nearly the same for a short data segment. For continued processing of a long duration seismic record, the adaptive Kalman filtering considered has better capability to learn the nonstationary data characteristics than the considered adaptive filtering and to adaptively remove the background noise. Detailed experimental results are presented. Other considerations such as the hardware implementation and the relationships among the parameters are also examined.

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-4 ,  Issue: 2 )