DeepRadar: Deep Learning for TI mmWave radars.

This projects makes an attempt to get insights from nearly-raw radar data, just lightly processed with FFT. Early experiments show that it's trivial to achieve 90+% accuracy for simple classifiers. The project goal is to establish boundaries of what's possible with this approach.

To build the firmware:

1. Get TI mmWave SDK and install it locally.

2. Check out this repository into ${MMWAVE_SDK_INSTALL_PATH}/packages/ti/demo/xwr14xx/DeepRadar.

3. Copy ${MMWAVE_SDK_INSTALL_PATH}/packages/ti/demo/xwr14xx/mmw/mmw.cfg into ${MMWAVE_SDK_INSTALL_PATH}/packages/ti/demo/xwr14xx/DeepRadar/mmw.cfg. This file is not released under open source license by TI and can't be included into this repository.

4. Set environment variables via ${MMWAVE_SDK_INSTALL_PATH}/packages/scripts/unix/setenv.sh as described in TI mmWave SDK User Guide.

5. Build the mss binary:

DeepRadar$ make all
Configuring RTSC packages...
<...>
******************************************************************************
Built the Millimeter Wave OUT and BIN Formats
******************************************************************************

6. Flash rss and mss binaries into your TI IWR1443BOOST board. See "How to flash an image onto xWR14xx/xWR16xx EVM" in the TI mmWave SDK User Guide.

Note: flashing might not properly work on Linux. Use a Windows machine for this operation as a workaround.