Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Abstract

Microwave doppler radar offers significant improvements for unobtrusive heart and respiration measurement. Radar monitoring enables non-contact measurement, through clothing, of heart and respiration rate, which is desired in several applications ranging from medical sleep laboratory measurements to home health care measurements and stress monitoring. The use of high frequency radar (>; 10 GHz) instead of lower frequencies (~2.4 GHz) increases the signal-to-noise-ratio of the signal and enables the utilization of commercial radar modules. However, if high frequency radar is used, linear combining of quadrature radar channels is inadequate. Instead, a nonlinear channel combining algorithm is needed. The combining can be performed with an arctangent function if center, amplitude error, and phase error are estimated accurately and corrected. In this paper, we show that the Levenberg-Marquardt (LM) center estimation algorithm outperforms the state-of-the-art center estimation algorithm precision-wise and is computationally less complex. The simulated results show that the root mean squared error with the LM method is always less than 1%, while it is around 5%-13% with the compared method, depending on the breathing signal model used. In addition, the computational complexity of the LM method stays almost constant as the size of the data set increases, whereas with the reference method, it increases exponentially. In this paper, the LM method is validated both with simulations and with real data.