Doppler Radar Techniques for Accurate Respiration Characterization and Subject Identification

Abstract

A low distortion dc coupled CW radar system with high signal to noise ratio is capable of accurate representation of respiration in human subjects. We propose to test the hypothesis that a non-contact physiological radar monitoring system which measures and characterizes subtle body kinematics, can be made to resolve patterns accurately enough to recognize an individual's identity. This paper investigates a technique to attain the requisite signal to noise ratio by dc offset management. Detailed exploration of the unique features in respiration signals using noncontact CW Doppler radar are presented. A proposed dynamic segmentation technique allowed detection of various unique features and patterns. KMN nearest neighbor and majority vote algorithms were implemented in software for this radar-based unique identification system. The system was tested and validated for six test subjects with 95% success rate. Fractal analysis of minor components of linearly demodulated radar signal was also presented for additional improvement in accuracy. This paper is believed to be significant as radar unique identification of human subjects has many potential applications, including security, health monitoring, IoT applications, and virtual reality.