Vehicle Detection by Sensor Network Nodes
Vehicle Detection by Sensor Network Nodes
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发表时间：
2004-10
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通讯作者：
J. Ding;S. Cheung;Chin-Woo Tan;P. Varaiya
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作者：
J. Ding;S. Cheung;Chin-Woo Tan;P. Varaiya
本文介绍了车辆检测传感器节点信号处理的算法开发和实验工作。用于车辆检测的信号是声学信号和磁信号。对声信号进行了短时FFT分析，提出了两种声学车辆检测算法：自适应阈值算法(ATA)和最小最大算法(MMA)。该算法在对声能曲线进行自适应阈值分割后，通过搜索1‘S序列来检测车辆。MMA通过在声能曲线上搜索局部最大值来检测车辆。实时测试和离线仿真验证了这两种算法的有效性。对于磁信号，采用了简单的阈值分割算法，并进行了实时测试，取得了良好的效果。最后，针对ATA的功耗要求，给出了ATA的FPGA实现，证明了使用专用硬件实现低功耗是合理的。
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.