Anti-Fixed-Interference Fall Detection Based on Doppler-Time-Range Maps Using Millimeter-Wave Radar

—With the growth of the number of elderly people, fall detection based on radio frequency signals for those vulnerable groups emerged. In radar-based fall detection methods, different heatmaps are generated from the echo signal. However, the noise and interference existing in the environment have a significant impact on heatmaps, which eventually lead to bad performances in fall detection. In this article, an anti-fixed-interference algorithm based on range-Doppler maps cancellation and a denoising algorithm based on Rayleigh probability distribution are proposed to ensure the consistency of the target’s Doppler information in a Doppler-time (DT) map. To leverage the micro-Doppler information extracted from the movements of human limbs during falls, we construct a DT-range (DTR) map, which fuses 3-D information in a single map. Moreover, a neural network composed of 3-D convolutional neural networks and a bidirectional long short-term memory network is designed to extract 3-D features in DTR maps. The experiment is conducted in three different scenes, where a giant metal chassis, pipe vibration, and a fan are presented as an interference, respectively. Finally, based on the proposed methods, we achieve 96.16%, 94.21%, and 93.06% precision of fall detection in the above scenes, respectively.