High dynamic range DC coupled CW Doppler radar for accurate respiration characterization and identification

Abstract

Accurate radar characterization of respiration can allow sleep diagnostics, and unique identification. A low distortion DC coupled system with high signal to noise ratio is required for such characterization and classification. This is especially critical with small signals as with through wall measurements with poor signal to noise ratio (SNR). This paper proposes a technique to improve signal to noise ratio by DC offset management and using the method of zooming in the fractions of the respiratory cycle waveform. Experimental results show a gain increment of 195 and 42% reduction of error in unique identification by complexity analysis techniques. Unique identification of human subjects behind walls has many potential applications such as, security, health monitoring, IoT applications, and virtual reality, and this technique can also benefit respiratory health diagnostics applications.