Intrusion identification using GMM-HMM for perimeter monitoring based on ultra-weak FBG arrays

Intrusion identification has been an intractable task for perimeter security. One of the primary challenges is to possess high identification rate over a long-distance range monitoring. This paper proposes an intrusion identification scheme based on ultra-weak fiber Bragg grating (UWFBG) arrays. The scheme is acquired by the combination of a Gaussian mixture model (GMM) and a hidden Markov model (HMM). The time dependencies are obtained by the analysis of relevant sensors in UWFBG arrays from the procedure of intrusions. The features extracted from vibration signals with time dependencies are used as the input of GMM-HMM. The GMM-HMM simultaneously analyzes features and time dependencies to identify intrusion. Experimental demonstration verifies that the proposed scheme can identify three intrusions (walking, knocking and climbing) and two non-intrusions (heavy truck passing and wind blowing) with the average identification rate of 98.2%. By the comparison tests with other six classifiers, the proposed GMM-HMM scheme shows a solid performance in the integrated evaluation for intrusion identification.