This thesis proposes a novel signal analysis based node localization strategy for sensor networks used in structural health monitoring (SHM) applications. The key idea is to analyze location-dependent multipath signal patterns in inter-node ultrasonic signals, and use machine-learning mechanisms to detect such patterns for accurate node localization on metal substrates on target structures. Majority of the traditional mechanisms rely on radio based Time Delay of Arrival (TDOA), coupled with multilateration, and multiple reference nodes. The proposed mechanism attempts to solve the localization problem in an ultrasonic sensor network (USN), avoiding the use of multiple reference beacon nodes. Instead, it relies on signal analysis and multipath signature classification from a single reference node that periodically transmits ultrasonic localization beacons. The approach relies on a key observation that the ultrasonic signal received at any point on the structure from the reference node, is a superposition of the signals received on the direct path and through all possible multi-paths. It is hypothesized that if the location of the reference node and the substrate properties are known a-priori, it should be possible to train a receiver (source node), to identify its own location by observing the exact signature of the received signal. To validate this hypothesis, steps were taken to develop a TI MSP430 based module for implementing a run-time system from a proposed architecture. Through extensive experimentation within an USN on the 2024 Aluminum substrate, it was demonstrated that localization accuracies up to 92% were achieved in the presence of varying spatial resolutions.
Read
