Paper Details: Downloads: 2905
Serial Number: P1111713560
Title: Applying Hyper-Fuzzy Extended Kalman Filter to Indoor Security Monitoring
Authors: Ali Alqudaihi and Mohamed Zohdy Ph.D. and Hoda Abdel-Aty-Zohdy Ph.D.
Abstract: This paper presents a new monitoring security system, based on data fusion (DF) from three heterogeneous of sensors which are pressure (PR), active sonar (AS) and infrared (IR). Sensors similarity and complementarity concepts are applied after proper data in real-time. Data is then fed to a Fuzzified Extended Kalman Filter (FEKF) which perform the data fusion. Based on the risk/suspiciousness degree of the monitoring system, moving agents are assessed. A Hyper-Fuzzy logic is utilized in the system as a decision making sub-system that allows the monitored area to be carefully and completely checked for any abnormal activities. The system is then animated to show how the fusion and visualize monitoring.
Keywords: Multi-Sensor Data Fusion, Feature Extraction, Fuzzified Extended Kalman Filter, Hyper-Fuzzy, Similarity and Complementarity, Measurements Fusion, State Vector Fusion.
Journal/Conference: International Journal of Automatic Control and System Engineering
Volume: 17
Issue: 1
Submission Date: 3/30/2017 12:00:00 AM
Review Date: 5/2/2017 12:00:00 AM
Publishing Date: 5/31/2017 12:00:00 AM
Article Downloads: 2905
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