Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Classify radar returns with both machine and deep learning approaches. The machine learning approach uses wavelet scattering feature extraction coupled with a support vector machine. Additionally, two deep learning approaches are illustrated: transfer learning using SqueezeNet and a Long Short-Term Memory (LSTM) recurrent neural network. Note that the data set used in this example does not require advanced techniques but the workflow is described because the techniques can be extended to more complex problems.

Classify radar waveform types of generated synthetic data using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).