This work describes Sparsity-Aware Super-Resolved Ranging using Multiple FMCW Radars. In our work, we exploit the joint sparsity of FMCW base-band signals in the range-doppler domain to detect and localize target objects more accurately. Our main objective is to systematically blend base-band measurements from multiple FMCW radar sensors to perform super-resolved high-resolution estimation of range and doppler of target objects in a 3D scene. The primary challenge is that the base-band chirp data generated by multiple FMCW radar sensors must be brought to a common frame of reference. We use Feed Forward Networks (FFNs) to align the chirp data across the sensors. In conclusion, we simulated FMCW radar to generate test data for ML model training and radar visualization. We have proposed a method to align 3D FMCW radar data into one common frame.
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- Matlab
- Python
- Setup python environment using either virtual env or anaconda
- Setup matlab
If using pip for python then:
pip install -R requirement.txt
if using conda environment then:
conda env create --file environment.yaml