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Sensors 2016, 16(1), 126; doi:10.3390/s16010126

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals

1
Key Lab of Optoelectronic Technology and Systems, Chongqing University, 174 Shazheng Street, Chongqing 400044, China
2
Technology Center of Sichuan Changhong Electric Co. Ltd, 199 Tianfu Road, Chengdu 610000, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Vincenzo Spagnolo and Dragan Indjin
Received: 10 November 2015 / Revised: 14 January 2016 / Accepted: 14 January 2016 / Published: 20 January 2016
(This article belongs to the Special Issue Infrared and THz Sensing and Imaging)
View Full-Text   |   Download PDF [4298 KB, uploaded 20 January 2016]   |  

Abstract

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms. View Full-Text
Keywords: empirical mode decomposition; symbolic dynamics; pyroelectric infrared signals; feature extraction; pattern classification empirical mode decomposition; symbolic dynamics; pyroelectric infrared signals; feature extraction; pattern classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, J.; Gong, W.; Tang, Y.; Li, W. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals. Sensors 2016, 16, 126.

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