Abstract
In biomedical Doppler radar applications, the return signal is a nonlinear frequency-modulation (NLFM) random process whose phase conveys heart and respiration vital-sign information. These signatures modulate the phase of the signal as two oscillating components with frequencies less than a few hertz. Due to the nonstationary nature of these signals, their analysis by 1-D techniques, temporal and spectral, may not be very useful, and time-frequency techniques may be incapable of accurately extracting their instantaneous frequency (IF) trajectory. In this paper, we present a bootstrap-based generalized warblet transform (GWT) signal processing method. The presented signal processing tool is a parametric method that has a kernel with Fourier-series components. The coefficients of the kernel are estimated by an iteration procedure that converges to the IF of the radar signal. We show theoretically and experimentally that the bootstrap-based GWT can extract the amplitude and frequency of the two vital-sign components at a range of 3 m in the face of low signal-to-noise ratio and in the presence of phase noise and body motion artifacts, achieving an accuracy that is potentially better than conventional methods can provide.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Instrumentation and Measurement
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.