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Open Access Publications from the University of California

Vehicle Detection by Sensor Network Nodes


This report presents the algorithm development and experimental work of the sensor node signal processing for vehicle detection. The signals used for vehicle detection are acoustic and magnetic signals. The acoustic signals are characterized by short time FFT analysis and two acoustic vehicle detection algorithms are proposed: the Adaptive Threshold algorithm (ATA) and the Min-max algorithm (MMA). The ATA detects vehicle by searching for a sequence of 1's after slicing the acoustic energy curve using an adaptive threshold. The MMA detects vehicles by searching the local maximum in the acoustic energy curve. Real time tests and offline simulations demonstrate the effectiveness of the two algorithms. For magnetic signals, a simple threshold slicing algorithm is utilized and real time tests give good performance. Finally, FPGA implementation of ATA is also presented for power efficiency requirement and the implementation justifies the use of dedicated hardware for low power implementation.

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