다양한 센서 기반의 침입체 탐지, 분류 및 추적 알고리즘 개발

다국어 초록 (Multilingual Abstract)

Surveillance is one of the major applications in wireless sensor network areas, and it is important to detect, classify and localize the targets. In this paper, we divide the research into two sections: (1) detecting and classifying the targets and (2) localizing them. To detect and classify multiple moving targets, we use acoustic and seismic sensors, and we analyze raw data from the sensors in both time and frequency domains. In this process, we must decide which features are useful for the classification to improve the performance and make it work in real time. Thus, we exploit Weibull likelihood and short-time Fourier transform (STFT) to extract the features as a sampling method. Then, we implement a support vector machine (SVM) and a neural network to classify the type of targets based on those features. Using the suggested algorithms, the proposed classifiers provide more accurate performance than the method that analyzes the raw data from only the frequency or time domain. For localization, Gaussian Process Regression (GPR) is used to estimate the relative location that corresponds to the received signal strength indication (RSSI) data. We also demonstrate the simultaneous localization with the process of detection and classification in real time. Finally, experimental results validate the suggested algorithm.

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