High-Precision Vital Signs Detection Method Based on Spectrum Refinement and Extended DCMA

In this paper,the spectral estimation algorithm is extended to the detection of human vi-tal signs by mm-wave frequency modulated continuous wave(FMCW)radar,and a comprehensive algorithm based on spectrum refinement and the extended differentiate and cross multiply al-gorithm(DCMA)has been proposed.Firstly,the improved DFT algorithm is used to accurately obtain the distance window of human body.Secondly,phase ambiguity in phase extraction is avoided based on extended DCMA algorithm.Then,the spectrum range of refinement is determ-ined according to the peak position of the spectrum,and the respiratory and heartbeat frequency information is obtained by using chirp z-transform(CZT)algorithm to perform local spectrum re-finement.For verification,this paper has simulated the radar echo signal modulated by the simu-lated cardiopulmonary signal according to the proposed algorithm.By recovering the simulated car-diopulmonary signal,the high-precision respiratory and heartbeat frequency have been obtained.The results show that the proposed algorithm can effectively restore human breathing and heart-beat signals,and the relative error of frequency estimation is basically kept below 1.5%.

A Tutorial on Quantized Feedback Control

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.