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PreSense is a team at the University of Illinois at Urbana-Champaign working on mmWave radar sensing. Under the guidance of Alchemy, a startup technology foundry led by Prof. Sanjay Patel, we focus on accelerating the maturity of mmWave radar platforms as a sensing modality with vast perception potential. Formed by five students with different backgrounds, the PreSense team has experience in DSP, machine learning, GPU programming and RF. Leveraging Texas Instruments’ mmWave radar platforms, PreSense has developed radar signal processing algorithms and demos suitable for various applications. We just released it as a Python-based open source package called OpenRadar on Oct 2nd 2019.

Currently, the PreSense team is concentrating on improving the OpenRadar package and developing new algorithms that apply machine learning techniques to a wide variety of radar sensing problems.. We are also working with industry-leading partners to unearth the potential of new radar hardware. If you are interested to learn more from us, please contact us at


OpenRadar is a Python-based open-source package for mmWave FMCW radar signal processing. It is based on Texas Instruments’ mmWave radar platform and tested on IWR1642 and IWR1843 evaluation boards. The code is built upon Numpy and optimized for matrix operations so it can run efficiently on any computer with Python and the associated packages installed. It is intended for people interested in radar signal processing, including:

1. Students who want to explore this new sensing platform but lack of radar signal processing background.

2. Researchers who prefer to focus on algorithm design with less stringent constraints on hardware.

3. Engineers who either want to evaluate the TI platform from the software level, or fast prototype algorithms without writing complex hardware specific C code.

OpenRadar is still under active development. The PreSense team is eager to support potential users in order to perfect the package. The team is currently also working on more radar algorithms and machine learning related demos. OpenRadar is released at and its associated documentation is hosted at Check the README on the GitHub for installation instructions and a detailed future plan. Please leave us any feedback or suggestion! If you encounter any problems, feel free to open up a new issue on our Github repo.


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