Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers

This paper presents the first initial results of using radar raw I & Q data and range profiles combined with Long Short Term Memory layers to classify human activities. Although tested only on simple classification problems, this is an innovative approach that enables to bypass the conventional usage of Doppler-time patterns (spectrograms) as inputs of the Long Short Term Memory layers, and adopt instead sequences of range profiles or even raw complex data as inputs. A maximum 99.56% accuracy and a mean accuracy of 97.67% was achieved by treating the radar data as these time sequences, in an effective scheme using a deep learning approach that did not require the pre-processing of the radar data to generate spectrograms and treat them as images. The prediction time needed for a given input testing sample is also reported, showing a promising path for real-time implementation once the Long Short Term Memory layers network is properly trained.