What are the most significant risks that could jeopardize the success of the
project? How are these risks being managed? What contingency plans are ready?
As we begin to test our system more extensively, significant risks that could jeopardize the success of the project include damage to the system during testing, which is especially risky for our project since we use borrowed components that cost over $1000 in total. Even if the components are not exposed to high temperatures except the temperature sensor, environmental conditions may hasten damage such as corrosion to the antenna, which is visible on the AWR1642 radar module that came with the green board. To prevent the same from happening to the AWR1843, we have chosen to enclose the system with a radome when testing in high-moisture conditions such as fog. The contingency plan is that we have two radar modules available in case one fails.
Another risk is the dataset not being sufficient to train the neural network to detect a human. Right now, the neural network has only been trained on the publicly available Smart Robot dataset that detects a corner reflector, which has a very different radar signature compared to a human. To mitigate this risk, our contingency plan is to train the neural network on our own dataset of 3D range-doppler-azimuth data of actual humans that is continually collected throughout the course of the semester.
Were any changes made to the existing design of the system (requirements,
block diagram, system spec, etc)?
No changes were made this week to the existing design of the system.
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