Collaborative mobile target imaging in ultra-wideband wireless radar sensor networks

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2008

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Arık, Muharrem

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Wireless sensor networks (WSN) have thus far been used for detection and tracking of static and mobile targets for surveillance and security applications. However, detection and tracking do not suffice for a complete satisfaction of these applications and an accurate target classification. To address this need, among various target classification methods, imaging of target yields the most valuable information. Nevertheless, imaging of mobile targets moving over an area requires networked and collaborative detection, tracking and imaging capabilities. With this regard, ultra-wideband (UWB) radar technology stands as a promising approach for networked target imaging over an area due to its unique features such as having no line-of-sight (LoS). However, the UWB wireless radar sensor network (WRSN) is yet to be developed for high quality imaging of mobile targets. In this thesis, an architecture for UWB wireless radar sensor network and a new collaborative mobile target imaging (CMTI) algorithm for UWB wireless radar sensor networks (WRSN) are presented. It is intended to accurately and efficiently obtain an image of mobile targets based on the collaborative e ort of deployed UWB wireless radar sensor nodes. CMTI enables detection, tracking and imaging of mobile targets with a complete WRSN solution. CMTI exploits mobility of the target in the sensor field to build its own multi-static radar aperture. Performance evaluations reveal that CMTI obtains high quality radar image of mobile targets in WRSN with very low communication overhead and energy expenditure.

Subject Keywords

Electrical engineering.

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http://etd.lib.metu.edu.tr/upload/3/12610145/index.pdf
https://hdl.handle.net/11511/17898

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. Arık, “Collaborative mobile target imaging in ultra-wideband wireless radar sensor networks,” M.S. - Master of Science, Middle East Technical University, 2008.