Low-Complexity Joint Extrapolation-MUSIC-Based 2-D Parameter Estimator for Vital FMCW Radar

In this paper, a low-complexity joint extrapolation-multiple signal classification (MUSIC)-based 2-D parameter estimator is proposed for vital frequency-modulated continuous-wave (FMCW) radar. Recently, an FMCW radar, which can detect the distance and vital Doppler information, has been considered for vital non-contact radar. In the conventional FMCW radar system, fast Fourier transform (FFT)-based algorithms with low complexity are used to extract multiple parameters. However, the resolution and accuracy of an FFT-based parameter estimator are considerably low. Thus, 2-D high-resolution algorithms, such as the 2-D estimation of signal parameters via rotational invariance techniques and 2-D MUSIC, have been suggested as an alternative method. However, a large computation power is required compared with the FFT-based methods. Therefore, this paper proposes a 2-D parameter estimator that combines extrapolated FFT and MUSIC to reduce the computational load for vital detection. The proposed method uses an extrapolated FFT to overcome the disadvantages of the low-resolution FFT for the distance information, and then, the 1-D MUSIC algorithm is applied to the Doppler domain direction only for the extracted magnitude and phase information of the target's extrapolated FFT results. Hence, the proposed algorithm combines the advantages of FFT and MUSIC. The performance of the proposed estimation is compared with that of other algorithms using Monte Carlo simulation results. The root-mean-square error of the proposed method is compared with that of 2-D MUSIC with various parameters. To verify the performance of the proposed combination method, the FMCW radar was used, and its performance was verified in an indoor environment.