A Low Complexity Based Spectrum Partitioning - ESPRIT for Noncontact Vital Radar
This paper proposes a low complexity based spectrum partitioning (SP) - ESPRIT for noncontact vital radar as a diagnostic tool for sleep apnea and other respiratory disorders. In the vital radar, the high precision and accuracy of the Doppler frequency is needed for the heart and respiration rates of the human body. However, because of smearing problems caused by limited data length and low SNR environments of the heartbeat signal, conventional fast Fourier transform (FFT) suffers from decreased performance of the Doppler frequency. To improve the parameters of radar measurement data such as the precision and accuracy, many super-resolution based algorithms, e.g., the SP method, have been proposed. Nevertheless, in order to apply the SP based super resolution algorithm into vital radar systems, a number of practical issues related to increased computational load should be addressed. Especially, compared with the conventional super-resolution algorithm such as estimation of signal parameters via rotational invariance techniques (ESPRIT), the complexity of the SP-ESPRIT is increased dramatically by performing multiple algorithms. Therefore, in this paper, we propose a scheme that is modified from the conventional SP-ESPRIT technique with the aim of reducing the computational load for vital detection. From Monte-Carlo simulation results with a SNR of 6 dB, the root mean square error (RMSE) of the proposed method is about 11 times lower than that of the conventional ESPRIT method.
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