Efficient and robust solutions for sensor network detection and localization

Jren-Chit Chin, Purdue University

Abstract

The ability to quickly detect and locate stealthy radiological dispersal devices (RDDs) allows authorities to disarm and remove the RDDs before they can be detonated. Traditionally, the detection of RDDs was accomplished by using expensive and cumbersome radiation sensors strategically located in the surveillance area. However, with recent advancements in wireless technologies and sensing hardware, deploying a large scale sensor network with small sensors is now becoming a reality. In this dissertation, we study methods to detect and locate radiation sources quickly and accurately using a network of sensors. Localization of a single radiation source can be achieved by using three sensors in a noise- and error-free environment. When both noise and errors are considered, we present a closed-form solution that outperforms existing algorithms. When more than three sensors are available, we present an efficient algorithm to exploit the additional sensor data, in order to further improve the robustness and accuracy of the localization. To localize multiple sources in a sensor network, we propose a hybrid formulation of a particle filter with a mean-shift technique, in order to achieve several important features which address major challenges faced by existing multiple source localization algorithms. First, our algorithm is able to maintain a constant number of estimation (source) parameters even as the number of radiation sources K increases. Second, our algorithm “learns” the number of sources from the estimated source parameters instead of relying on expensive statistical estimations. Third, the presence of obstacles may improve the localization accuracy of our algorithm. Unfortunately, the presence of obstacles significantly degrades the accuracy of existing algorithms. When no radiation source is present, the localization algorithms produce false positives as the algorithms assume that a radiation source is present. We propose the Localization Enhanced Detection (LED) method, that decides whether a source with the estimated parameters is present or absent, using a close-to-minimal number of measurements, while maintaining the false positive and false negative rates below a specified level. We evaluate the LED method using simulation and testbed experiments, and compare the effectiveness of the LED method with existing detection methods. We build a cross-platform, cross-language, and versatile software framework that provides an abstraction for interfacing with sensors and supports building applications on radiation source localization. The software framework implements various localization algorithms that are ready to be deployed in an actual system. The components in the software framework are loosely coupled and are general enough to support application domains beyond radiation source localization. We demonstrate the versatility of the software framework in building the Rapid Structural Assessment Network.

Degree

Ph.D.

Advisors

Yau, Purdue University.

Subject Area

Computer science

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