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Target Classification in Sparse Sampling Acoustic Sensor Networks using DTW-Cosine Algorithm  

Kim, Young-Soo (한국정보통신대학교 공학부)
Kang, Jong-Gu (한국정보통신대학교 공학부)
Kim, Dae-Young (한국정보통신대학교 공학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.14, no.2, 2008 , pp. 221-225 More about this Journal
Abstract
In this paper, to avoid the frequency analysis requiring a high sampling rate, time-warped similarity measure algorithms, which are able to classify objects even with a low-rate sampling rate as time- series methods, are presented and proposed the DTW-Cosine algorithm, as the best classifier among them in wireless sensor networks. Two problems, local time shifting and spatial signal variation, should be solved to apply the time-warped similarity measure algorithms to wireless sensor networks. We find that our proposed algorithm can overcome those problems very efficiently and outperforms the other algorithms by at least 10.3% accuracy.
Keywords
Sensor network; Target classification; DTW; Cosine algorithm;
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