CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multi-Person Activity Sensing with FMCW Microwave Radar

• Published in: IEEE transactions on instrumentation and measurement p. 1
• Main Authors: Raeis, Hossein, Kazemi, Mohammad, Shirmohammadi, Shervin
• Format: Journal Article
• Language: English
• Published: IEEE 15.02.2024
• Subjects: Convolutional Autoencoder; Data mining; Feature extraction; FMCW Radar; Human activity recognition; Human Activity Sensing; Interference; Interference cancellation; Multiple Subjects Monitoring; Radar; Sensors
• ISSN: 0018-9456
• DOI: 10.1109/TIM.2024.3366575

Human Activity Sensing is a crucial component of health monitoring and smart environment applications. Frequency Modulated Continuous Wave (FMCW) radars can be used for target tracking, but their collected data is usually accompanied by a significant amount of interference, especially in indoor environments hosting multiple human subjects, leading to a decrease in accuracy. In this paper, we propose a method that compensates that interference and can detect individual activities of multiple humans, overcoming existing methods' limitation of detecting single human activities. To this end, a range-doppler map of the data is extracted with a FWCW radar, and the interference effect of this map is mitigated by a Convolutional Autoencoder (CAE). The CAE network learns to attenuate false positive regions to strengthen the target areas. This is followed by a Gaussian filter and then the targets are revealed by applying derivatives on both dimensions of the map. Evaluation results show that our method reaches activity recognition accuracies of 97.13% and 73.37% in the cases of one and two humans, respectively.