Bespoke Simulator for Human Activity Classification with Bistatic Radar

Abstract

Radar is now widely used in human activity classification because of its contactless sensing capabilities, robustness to light conditions and privacy preservation compared to plain optical images. It has great value in elderly care, monitoring accidental falls and abnormal behaviours. Monostatic radar suffers from degradation in performance with varying aspect angles with respect to the target. Bistatic radar may offer a solution to this problem but finding the right geometry can be quite resource-intensive. We propose a bespoke simulation framework to test the radar geometry for human activity recognition. First, the analysis focuses on the monostatic radar model based on the Doppler effect in radar. We analyse the spectrogram of different motions by Short-time Fourier analysis (STFT), and then the classification data set was built for feature extraction and classification. The results show that the monostatic radar system has the highest accuracy, up to 98.17%. So, a bistatic radar model with separate transmitter and receiver was established in the experiment, and results show that bistatic radar with specific geometry configuration (CB2.5) not only has higher classification accuracy than monostatic radar in each aspect angle but also can recognise the object in a wider angle range. After training and fusing the data of all angles, it is found that the accuracy, sensitivity, and specificities of CB2.5 have 2.2%, 7.7% and 1.5% improvement compared with monostatic radar.