Potential Application of Hybrid Belief Functions and Hilbert-Huang Transform in Layered Sensing

Multiple and layered sensor networks are currently used in various applications. This has led to efficient and extendable approach for providing information for monitoring infrastructure systems and surveillance for a variety of military and civilian applications. A major benefit of these type of networks, include higher quality of information from the combination of different and independent sensors and the presentation of different spatial perspectives. Correct assessment and interpretation of the output of these types of sensor networks are dependable on correct knowledge representation and information fusion. This paper uses hybrid Belief Function and Hilbert Huang Transform to combine 2-D and 1-D signals in layered sensor network. The methodology is then applied to search and rescue with camera images, noise and carbon dioxide sensors.