Vehicle classification in sensor networks using time-domain signal processing and neural networks

被引:45
作者
Mazarakis, Georgios P. [1 ]
Avaritsiotis, John N. [1 ]
机构
[1] Natl Tech Univ Athens, Dept Elect & Comp Engn, Athens, Greece
关键词
vehicle classification; wireless sensor networks; time domain; neural networks; TESPAR;
D O I
10.1016/j.micpro.2007.02.005
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA's SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:381 / 392
页数:12
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