Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Through-Ice Acoustic Source Tracking Using Vision Transformers With Ordinal Classification, Steven Whitaker, Andrew Barnard, George D. Anderson, Timothy C. Havens
Through-Ice Acoustic Source Tracking Using Vision Transformers With Ordinal Classification, Steven Whitaker, Andrew Barnard, George D. Anderson, Timothy C. Havens
Michigan Tech Publications
Ice environments pose challenges for conventional underwater acoustic localization techniques due to theirmultipath and non-linear nature. In this paper, we compare different deep learning networks, such as Transformers, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Vision Transformers (ViTs), for passive localization and tracking of single moving, on-ice acoustic sources using two underwater acoustic vector sensors. We incorporate ordinal classification as a localization approach and compare the results with other standard methods. We conduct experiments passively recording the acoustic signature of an anthropogenic source on the ice and analyze these data. The results demonstrate that Vision Transformers are …
Target Localization And Tracking By Fusing Doppler Differentials From Cellular Emanations With A Multi-Spectral Video Tracker, Casey D. Demars, Michael Roggemann, Adam Webb, Timothy C. Havens
Target Localization And Tracking By Fusing Doppler Differentials From Cellular Emanations With A Multi-Spectral Video Tracker, Casey D. Demars, Michael Roggemann, Adam Webb, Timothy C. Havens
Michigan Tech Publications
We present an algorithm for fusing data from a constellation of RF sensors detecting cellular emanations with the output of a multi-spectral video tracker to localize and track a target with a specific cell phone. The RF sensors measure the Doppler shift caused by the moving cellular emanation and then Doppler differentials between all sensor pairs are calculated. The multi-spectral video tracker uses a Gaussian mixture model to detect foreground targets and SIFT features to track targets through the video sequence. The data is fused by associating the Doppler differential from the RF sensors with the theoretical Doppler differential computed …