Multiple-input multiple-output (MIMO) is a technical hotspot in physical layer with numerous applications in wireless communications, radars, sonars, and well beyond. In this paper, we focus on the multi-dimensional angle estimation problem in a bistatic electromagnetic vector sensors (EMVS) MIMO system. Namely, we need to simultaneously estimate two-dimensional (2D) direction-of-arrival (DOA), 2D direction-of-departure (DOD), 2D receive polarization angle (RPA) and 2D transmit polarization angle (TPA). To tackle this issue, a parallel factor (PARAFAC) analysis-based estimator is proposed. Firstly, a third-order PARAFAC analysis data model is established, which can efficiently exploit the tensor structure of the array measurement. After performing PARAFAC decomposition on the tensor measurement, the factor matrices are achieved. By combining the estimation method of signal parameters via rotational invariance technique (ESPRIT) with the vector cross-product method, joint estimates of 2D-DOD, 2D-DOA, 2D-TPA and 2D-RPA are obtained without further pairing calculation. Compared with the state-of-the-art ESPRIT-Like approach, the proposed method can achieve better performance by enforcing the third-order structure information, and it is suitable for arbitrary array manifolds. Theoretical analyses are given and numerical results corroborate our analysis.

Joint 2D-DOD, 2D-DOA, and Polarization Angles Estimation for Bistatic EMVS-MIMO Radar via PARAFAC Analysis

Fast direction finding for bistatic EMVS-MIMO radar without pairing

The issue of two-dimensional (2D) direction-of-departure and direction-of-arrival estimation for bistatic multiple-input multiple-output (MIMO) radar with a coprime electromagnetic vector sensor (EMVS) is addressed in this paper, and a tensor-based subspace algorithm is proposed. Firstly, the covariance measurement of the received data is arranged into a fourth-order tensor, which can maintain the multi-dimensional characteristic of the received data. Then, the higher-order singular value decomposition is followed to get an accurate signal subspace. By utilizing the uniformity of the subarrays in coprime EMVS–MIMO radar, the rotation invariant technique is adopted to achieve ambiguous elevation angle estimation. Thereafter, the unambiguous elevation angles are recovered by exploring the coprime characteristic of the subarrays. Finally, all azimuth angles are achieved by using the vector cross-product strategy. The tensor nature inherited from the array measurement is fully explored, and the coprime geometry enables EMVS–MIMO radar to achieve larger array aperture than the existing uniform linear configuration; thus, the proposed method offers better estimation performance than current state-of-the-art algorithms. Several computer simulations validate the effectiveness of the proposed algorithm.

Joint 2D-DOD and 2D-DOA Estimation for Coprime EMVS–MIMO Radar

Closed-form estimation algorithm for EMVS-MIMO radar with arbitrary sensor geometry

2D-DOD and 2D-DOA estimation using the electromagnetic vector sensors

Fast communication: Target localization using MIMO electromagnetic vector array systems

The invention discloses non-contact type vital sign monitoring equipment, which comprises a radar signal transmitting module, a radar signal receiving module and a digital signal processing module, wherein the radar signal receiving module is respectively connected with the radar signal transmitting module and the digital signal processing module, the radar signal transmitting module is used for transmitting a continuous wave radar signal to a monitored person, the continuous wave radar signal transmitted by the radar signal transmitting module is transmitted by the body of the monitored person to obtain a radar echo signal, the radar echo signal is received by the radar signal receiving module and is demodulated into two paths of in-phase and orthogonal baseband signals, and the obtained two paths of in-phase and orthogonal baseband signals processed by the radar signal receiving module are subjected to inverse tangent processing and frequency detection by the digital signal processing module. The non-contact type vital sign monitoring equipment enables medical staff to take corresponding medical response according to actual situations of a patient in time. The non-contact type vital sign monitoring equipment has the advantages that the safety is high, the accuracy is high, the small scale and the intelligence are realized, and the vital sign condition of a patient can be conveniently controlled by a medical staff in a real-time and continuous way.

Non-contact type vital sign monitoring equipment

The invention discloses a signal processing algorithm of a non-contact type vital sign monitoring system. The signal processing algorithm comprises the following steps of step 1, respectively carrying out heartbeat signal and breathing signal separation on the received signals of a path I and a path Q; step 2, extracting the main components of heartbeat and breathing by a main component analyzing algorithm; and step 3, respectively carrying out smooth conversion and Fourier conversion on the heartbeat and breathing components obtained in step 2, so as to obtain the waveforms and the frequencies of heartbeat and breathing. The signal processing algorithm has the advantages that the effectivity and the reliability are realized, the vital sign signals can be monitored in a real-time way, the monitoring precision is high, and the real-time performance is high.

Signal processing algorithm of non-contact type vital sign monitoring system

The invention provides a signal processing algorithm for a non-contact life sign monitoring system. The algorithm comprises the following steps: performing arc tangent transformation on I path and Q path signals obtained by a non-contact life sign monitor to solve an orthogonal demodulation signal; separating heart beat and respiration signals by using a local integral average empirical mode decomposition algorithm; performing Hilbert transformation on the respiration and heart beat signals. The heart beat and respiration signals are separated by using the local integral average empirical mode decomposition algorithm, and the Hilbert transformation is performed on the respiration and heart beat signals, so heart beat and respiration frequency and waveform are sequentially obtained according to the amplitude of the frequency under the condition that the heaving of a chest caused by heart beat is not obvious, the calculation accuracy is high, the actual applicability is high, and accurate, real-time and continuous mastering of the respiration and heart beat conditions of a patient by a medical worker is facilitated.

Signal processing algorithm for non-contact life sign monitoring

The invention provides a respiration and heartbeat signal separation algorithm based on a non-contact vital sign monitoring system. The respiration and heartbeat signal separation algorithm comprises the steps that on the basis of the arc tangent processing principle, two I/Q signals received by a simulation receiving end are subjected to arc tangent processing to obtain a quadrature demodulation signal; a waveform reconstructing algorithm based on interpolation operation is adopted, and an inner node is selected for fitting a respiration signal; the waveform of the fitted respiration signal in the quadrature demodulation signal is removed so that a heartbeat signal waveform can be obtained; the fitted respiration signal waveform and the heartbeat signal waveform are subjected to signal smoothing and frequency extracting operation, and accordingly the real-time frequency of the respiration signal and the real-time frequency of a heartbeat signal are obtained. By the adoption of the method, the respiration signal and the heartbeat signal in the quadrature demodulation signal can be effectively separated and extracted, the frequency information of the respiration signal and the frequency information of the heartbeat signal are acquired accurately, and thus medical staff can accurately and continuously grasp the respiration and heartbeat situation of a patient in real time.

Li Hongtao

Papers

Fast communication: Target localization using MIMO electromagnetic vector array systems

Non-contact type vital sign monitoring equipment

Signal processing algorithm of non-contact type vital sign monitoring system

Signal processing algorithm for non-contact life sign monitoring

Respiration and heartbeat signal separation algorithm of non-contact vital sign monitoring system