This paper 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 MSP-430 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.