Millimeter wave (mm-Wave) radar has been widely employed for non-contact vital sign detection. This research proposes a novel processing scheme to improve the accuracy and stability of non-contact heart rate detection. Firstly, we propose an adaptive range bin selection method based on variance which can select accurate range bins containing phase information of vital signs. Secondly, the smooth spline is utilized to fit the original phase signal to obtain a relatively pure template signal. Subsequently, the pure template signal is used as the input signal for the matched filter to convolve with the phase signal. The matched filtering can eliminate interference and noise such as breathing and random body movements (RBM). Next, the heartbeat signal is extracted by the Variable Mode Extraction (VME) algorithm. Finally, a novel frequency measurement technique named as Double-Chirp Z-Transform (Double-CZT) is proposed. The frequency of the heartbeat signal is measured by the Double-CZT. Compared with the traditional frequency measurement techniques, the Double-CZT improves the accuracy of frequency measurement and allows for more accurate heart rate measurements. Many subjects were invited to participate in the experiment, and the mean absolute error (MAE) of heart rate was less than 1 beat per minute (bpm) on average. The experimental results demonstrate that the proposed scheme can improve the signal-to-noise ratio (SNR) of heartbeat signal and provide accurate heart rate estimation.

The technology of non-contact heart rate detection has been proven of great use. However, it is still limited by several challenges, particularly the influence of human motion and random disturbances, which significantly degrade the accuracy of the measurement. To address these challenges, this paper proposes a novel scheme for heart rate detection. Firstly, the proposed scheme utilizes the Multi-Channel Averaging (MCA) technique to improve the signal-to-noise ratio (SNR) of the extracted phase signal from the echoes in the multiple receivers, and a high-pass filter to roughly extract a heartbeat signal. Secondly, to suppress motion artifacts (MA) in the heartbeat signal caused by human motion, the Adaptive Parameter Selection for Expectation-Maximization (APSEM) method is proposed. Further, a novel processing sub-framework which combines the Kalman filtering, the Variational Mode Extraction (VME) algorithm, and the Rife spectral analysis method (called KFRV method) is proposed to mitigate the effect of random disturbances and achieve accurate frequency estimation. Experimental results using the Polar H10 heart rate sensor as a reference show that the proposed scheme achieves accurate heart rate detection in the presence of human motion, with the Mean Absolute Error (MAE) of less than 2.5 beats per minute (bpm), which is much better than traditional schemes. Compared to traditional methods, the proposed scheme exhibits negligible loss in heart rate detection under static state, with an average MAE of 0.99 bpm. Overall, the experimental results demonstrate the applicability of the proposed scheme for accurate heart rate detection in both human motion and static states.

This paper proposes a novel heart rate detection scheme to address key challenges in millimeter-wave radar-based vital sign monitoring, including weak signals, various types of interference, and the demand for high-precision and super-resolution frequency estimation under practical computational constraints. First, we propose a multi-dimensional coherent accumulation (MDCA) method to enhance the signal-to-noise ratio (SNR) by fully utilizing both spatial information from multiple receiving channels and temporal information from adjacent range bins. Additionally, we are the first to apply the fast iterative interpolated beamforming (FIIB) algorithm to radar-based heart rate detection, enabling super-resolution frequency estimation with low computational complexity. Compared to the traditional fast Fourier transform (FFT) method, the FIIB achieves an improvement of 1.08 beats per minute (bpm). A reordering strategy is also introduced to mitigate potential misjudgments by FIIB. Key parameters of FIIB, including the number of frequency components L and the number of iterations Q, are analyzed and recommended. Dozens of subjects were recruited for experiments, and the root mean square error (RMSE) of heart rate estimation was less than 1.12 bpm on average at a distance of 1 m. Extensive experiments validate the high accuracy and robust performance of the proposed framework in heart rate estimation.

Non-contact heart rate detection techniques have proven to be very useful. However, the interference of respiration signal and its harmonics during the detection process can result in a significant decrease in the accuracy of heart rate detection. To suppress the interference of respiration and its harmonics and achieve accurate heart rate estimation, a novel heart rate detection scheme is proposed in this paper. Firstly, after preprocessing the radar raw data, the respiratory harmonic suppression method which combines Chirp-Z transform (CZT) with Hough transform to identify and filter the respiratory signal and its harmonics on the 2-D spectrum. Subsequently, the respiratory signal and its harmonics are filtered by Gaussian band-stop filter. Secondly, to obtain a more accurate heartbeat signal, the variational mode decomposition (VMD) algorithm is employed to denoise the heartbeat signal. Finally, a high-precision frequency measurement method which combined the CZT with Rife algorithm (called as CZT-R method) is proposed. The CZT-R method can improve the accuracy of frequency measurement for heartbeat signal. Extensive experimental results show that the mean absolute error (MAE) of the proposed scheme for heartbeat rate detection were less than 1.16 (bpm), which is better than traditional heart rate detection schemes.

Conventional spaceborne synthetic aperture radar (SAR) is hard to achieve high-resolution and wide-swath simultaneously because of the trade-off between range and azimuth ambiguities suppression. As a promising candidate, alternate transmitting mode (ATM) based on waveform diversity suppresses range ambiguity without changing the pulse repetition frequency (PRF). However, it introduces the periodic amplitude-phase modulation in azimuth direction, which results in the severe azimuth ambiguity. To solve this problem, this letter proposes a novel range-azimuth ambiguity joint suppression scheme based on waveform diversity. In the proposed scheme, the desired signal is firstly focused by the modified matched filtering (MF) based algorithm, while range ambiguity is defocused due to the mismatch with transmitted waveforms. Then the constructed image-domain sparse SAR imaging model weakens the azimuth ambiguity energy and reconstructs the high-dimensional and unambiguous considered scene from the low-dimensional Doppler spectrum by solving the L₁-norm optimization problem. Experiments based on simulated data verify the proposed method.

Geosynchronous earth orbit (GEO) synthetic aperture radar (SAR) holds great potential for both civilian and military applications due to its short revisit period and large swath. However, the extremely high orbital altitude and long synthetic aperture time of GEO SAR render the stop-and-go model and the flat surface assumption invalid. Although capability to compensate for nonlinear motion of the back-projection (BP) algorithm makes it suitable for GEO SAR focusing, its computational cost is prohibitively high. In this letter, a modified fast BP algorithm based on partitioned Lagrange interpolation and nonstop-and-go model is proposed for GEO SAR imaging. In the proposed method, an equivalent slant range model is firstly derived based on the actual GEO SAR geometry. Then the subaperture projection is performed on a global curved grid by introducing subaperture reference points, and a novel subimage spectrum compression function is derived according to the proposed model. Finally, recursive fusion is performed on the subimages after phase compensation. Furthermore, a partitioned Lagrange interpolation method is implemented in the proposed method to reduce the computational complexity. Experiments based on simulated data verify the proposed method.