The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

When the desired signal data exists in the array received data or the steering vector has a mismatch problem, the current traditional adaptive beamformers will suffer from the effect of the desired signal cancelation phenomenon, resulting in a sharp decline in performance. To address the occurrence of desired signal cancelation, an improved matrix projection-based efficient beamforming method is proposed. Firstly, based on spatial partitioning (SP) technology, a significant projection matrix for interference-plus-noise space (INS) is constructed. Secondly, using the constructed key projection matrix, the sample data covariance matrix is projected into the INS to achieve the goal of suppressing the desired signal data information. Finally, the weight data vector is calculated by Capon beamformer. The proposed algorithm does not require an iterative search for the optimal solution, which has the advantage of a small amount of calculation. Simulation experiments have verified that the proposed method has significant advantages in suppressing the desired data signals. Especially when the desired data signal has large power, the signal-to-interference-plus-noise ratio (SINR) of the proposed algorithm is better than that of the compared algorithms under the conditions of random directionality errors or local scattering errors between the desired signal and interference.

This paper addresses the two-dimensional (2D) direction-of-arrival (DOA) estimation problem with two novel methods for mixed noncircular and circular signals. The first proposed method is named the two-stage direction-of-arrival matrix (TSDOAM) method, and the other is called the two-stage rank reduction (TSRARE) method. The proposed methods utilize both the circularity and the direction-of-arrival differences between the noncircular and circular sources to estimate the 2D directions-of-arrival (DOAs). The maximum detectable 2D angle parameters of the TSDOAM and TSRARE methods are twice those of the existing methods. Moreover, the TSRARE method can detect more incident signals than the TSDOAM method due to the array aperture of two parallel uniform linear arrays (ULAs) being fully utilized. Simulation results show that compared to the existing methods for the small angle separation of 2D directions-of-arrival, the two proposed methods perform well in terms of the signal-to-noise ratio (SNR) and snapshots.

Doppler radar-based fast heart rate (HR) extraction has great application potential in stress and emotion recognition, anxiety treatments, etc. However, fast extraction of HR is still a great challenge for the vital sign detection applying the traditional discrete Fourier transform (DFT) method based on a continuous-wave (CW) Doppler radar sensor. When applying traditional DFT to the baseband signal analysis, the spectrum resolution will become insufficient if the time window is less than 10 s. In this paper, an interpolated DFT algorithm based on a poly-item cosine window is introduced to achieve fast extraction of HR with only 3 s data length. In addition, the leakage and grid effect phenomenon is presented to show the defects caused by the traditional DFT method. To verify the effectiveness of the proposed method, simulations are performed and experiments are executed using a 10-GHz CW Doppler radar sensor platform. Compared with the traditional DFT method, the interpolated DFT method reduces the average HR error from 8.48% to 1.87% based on the Hanning window and from 8.48% to 1.45% based on the rectangular window.