TerraWave is seeking to enhance its data analysis capabilities by leveraging radio-wave sensors to collect and process field data. The primary goal of this project is to apply signal processing techniques to extract meaningful features from the collected data. This will involve analyzing the raw data to identify patterns, trends, and anomalies that can inform future sensor deployments and improve data accuracy. The project will provide learners with the opportunity to apply their classroom knowledge of signal processing and data analysis in a practical setting. By focusing on feature extraction, the project aims to optimize the performance of TerraWave's sensors and contribute to the development of more efficient data processing algorithms. The tasks are closely related and designed to be completed by a team of learners within a single academic program, ensuring a cohesive approach to problem-solving.

The project deliverables will include a comprehensive report detailing the signal processing techniques applied and the features extracted from the radio-wave sensor data. Additionally, the team will provide a presentation summarizing their findings and recommendations for improving data processing efficiency. The deliverables will also include any developed algorithms or scripts used in the analysis, ensuring that TerraWave can replicate and build upon the work completed by the learners.