Research On Pyroelectric Infrared Signal Characteristics Analysis And Human Recognition

With the development of technology and economy, people put forward higher requirement for the safety of public and home evironment. The Urban Safety Program carried out by government agencies accelerates the development of safe protection engineering. The key parts of the Urban Safety Program consist of television monitoring system, electronic patrol system, human intrusion detection system, and so on. The Urban Safety Program brings great business opportunities, and raises higher technical demands on all kinds of security products at the same time. The pyroelectric infrared (PIR) detectors are most widely used in home and public security system for their low cost, low power consumption, statble performance and excellent environmental adaptability. However, the high false alarm rate of the existing PIR detectors has limited their applications. Based on thoroughly analyses, it's found out that besides the limitation of their mechanism and structure design, there is no effective analysis and feature extraction for PIR signals of different infrared sources. Therefore, introducing the method of signal processing and pattern recognition into the analysis of PIR signal is not only valuable for improving the performance of PIR detector, but also significant for analyzing one dimension signals in security system.The research proposed in this dissertation is supported by the National High-Tech Research and Development Plan of China, and by the Basic Research Project of the'Eleventh Five-Year-Plan'of China, and by Key Research Project of the Natural Science Foundation of Chongqing Municipality of China. This dissertation focus on the false alarm rate of the widely used PIR detectors, the study subjects are the PIR signals of different infrared sources collected in different experiments. The preprocessing method, feature extraction method and feature fusion method for PIR signal are studied systematically. The human and non-human recognition mthod for decreasing the false alarm rate has been proposed, so that the practical implementation method for improving the performance is presented.Four main explorative researches on PIR signal recognition are made in this papar:(1) Based on deep research on performances of the PIR detector, the equivalent model of different infrared radiation sources are created, and the relational expressions of effective radiation area and position are derived, and feature differences between human and non-human PIR signal are analyzed. Then the ideal output signal of a PIR detector is simulated. The simulation signal and the collected signal has good similarity, which can supply pure original signal for selecting denoising method, and can provide reference for designing PIR detectors with better performance. At last, the PIR signal is proved to be non-stationary which provides significant reference for feature extraction.(2) In view of the fact there are differences between human and non-human PIR signal in power distribution in time-frequency domain, wavelet packet entroy (WPE) is proposed for feature extraction.Wavelet packet decomposition has finer and adjustable resolution at high frequency bands which extracts more detail features of different infrared sources. Combining the Shannon entropy with the wavelet packet decompaositon, the aquired features descript the complexity of different PIR signals. Experimental results show that db1 wavelet which holds similary symmetry with a PIR signal has best recognition ability. The WPE value of human body is significiantly smaller than that of non-human body in frequency band from 0Hz to 2.5Hz, which demonstrates that the human PIR signal is more orderly.(3) Because the real wavelet decomposition is shift sensitve, that is, small fluctuation will lead to unpredictable results. Double density dual tree complexity wavelet transform (DD-DT CWT) wavelet entropy is proposed for extracting features of PIR signals. DD-DT CWT has good properties of shift-invariant, anti aliasing and high calculation efficiency. Therefore, DD-DT CWT wavelet entropy preserves the properties of approximately periodic of PIR signal, and extracts features which are can be used as discrimination information for different infrared soruces. Experimental results show that the recognition rate is 87.3% when the decomposition level is 4.(4) In order to improve the recognition ability, canonical correlation analysis (CCA) for PIR signal feature fusion is proposed. The proposed method uses correlation features of two groups of feature vectors as effective discriminant information, so it is not only suitable for information fusion, but also eliminates the redundant information among the features. This is a new way to classifiacation and recongiton for PIR signals. Better feature description for classification can be obtained by fusing the local and global features of PIR signals. Experimental results show the recognition rate of DD-DT CWT wavelet entropy fusing with its own sub-pattern based on CCA method can reach to 94.3%, which is 7.0% higher than that of using single feature.The three feature extraction and recognition methods proposed in this dissertation are effective for improving the performance of the existing PIR detector. In addition, by the comparison of the three methods, the CCA feature fusion method has the best human recognition ability and the lowest false alarm rate.