Kristin Tighe’s research while affiliated with The University of Texas at Arlington and other places

Accurate detection of oscillatory electrical signals emitted from remote sources is necessary in many applications but poses several challenges. The major challenge is attributed to the source voltage and conductivity of the medium through which signals must transmit before they can be sensed by the receiving electrodes/sensors. This study introduces a novel algorithm to optimize source identification where low-voltage (mV range) signals transmit through a conductive medium. The proposed algorithm uses the measured data from different oscillatory signal sources and solves an inverse problem by minimizing a cost function to estimate all the signal properties, including the locations, frequencies, and phases. To increase the overall signal accuracy for a wide range of initial guess frequencies, we have utilized the Lomb-Scargle spectral analysis along with the Least Squares error optimization method. The data utilized in this study comes from an experimental setup that includes a bucket filled with salt-water as the conductive medium, multiple low-voltage signal sources and 32 remotely located sensors. The sources generate sine waves with amplitude of 10 mV and frequencies between 10 – 40 Hz. The average signal-to-noise ratio is approximately 10 dB. The algorithm has been validated using a single-source and multi-source setup. We observed that our algorithm can identify the source location within 10 mm from the actual source immersed inside the bucket of radius =~ 90 mm. Moreover, the frequency estimation error is nearly zero, which justifies the effectiveness of our proposed method.