Late in the academic year of 2022 two of our members, Sirio Jansen-Sanchez and Logan Luna, were awarded the Student Internal Grant (SIG) at Embry-Riddle Aeronautical University to conduct research expanding the field of active sonar navigation. On the weekend on 10/26/24 the club utilized a developmental contingency to ensure the research could make progress.
Data Collection Contingency
Due to some developmental delays the sonar was put installed on the club’s ROV, Mako. Mako was created as a fully manual ROV to compete in the MateROV competition, you can learn more about this on the MateROV page of our website. During Mako’s development the Mako team kept in mind the needs of the club and the vehicle was created to also be a test platform for any experimental systems. This contingency was activated for the collection of sonar data to allow the research project to continue. Depicted in this section is a visual representation of the data collected during the test.
Research Proposal
The abstract for the research project is as follows:
“This research aims to develop a semi-supervised model leveraging contrastive learning for remote sensing, with a primary focus on sonar data processing and potential adaptation to radar systems. Remote sensing technologies like sonar and radar rely on the detection of objects and environments using reflected signals—sonar with sound waves for underwater mapping and radar with electromagnetic waves for atmospheric or terrestrial detection. The focus of this work is on contrastive learning, which enables the model to differentiate between objects detected in sonar scans, such as buoys or gates, by learning distinct representations for each object. The dataset comprises sonar scans representing a 400-gradian environment, capturing intensity readings at various distances, along with timestamp and angle data. These sonar scans are processed sequentially using Long Short-Term Memory (LSTM) layers, which capture temporal patterns while compressing and de-noising the data, thus reducing computational load while improving object detection accuracy. Additional features like normalized scan data, angle differences, and time shifts are incorporated to enhance the model’s performance. Although the research is currently centered on sonar, the contrastive learning framework, alongside the deep learning techniques employed, is highly applicable to radar systems. Both sonar and radar face similar challenges in signal processing and object detection. This research highlights how advancements in sonar data processing through contrastive learning and RNN Autoencoders offer a unified framework for enhanced object detection and environmental mapping across remote sensing technologies. “
Once the research project has concluded a formal post will be created detailing how Logan and Sirio’s proposed process works along with the publication.