Multi target human gait classification using LSTM recurrent neural networks applied to micro Doppler
A Recurrent Neural Network (RNN) with a Long Short Term Memory (LSTM) implementation can be effectively utilized to classiiy the radar signature of zero to three human gaits, based on their micro Doppler return. The LSTM RNN outperforms the current state of the art with an accuracy of 89.1% The use of a sequence to sequence classification method like RNN additionally brings the benefit of being able to classify measurements with a variable observation time which, on average, is notably less than the state of the art. Another advantage of the LSTM RNN is that it can also be used with measurements that include transitions between classes over time.
2017 European Radar Conference (EURAD)