A nuclear magnetic resonance (NMR) experiment for measurement of time-dependent magnetic fields was introduced. To improve the signal-to-interference-plus-noise ratio (SINR) of NMR data, a new method for interference cancellation and noise reduction (ICNR) based on singular value decomposition (SVD) was proposed. The singular values corresponding to the radio frequency interference (RFI) signal were identified in terms of the correlation between the FID data and the reference data, and then the RFI and noise were suppressed by setting the corresponding singular values to zero. The validity of the algorithm was verified by processing the measured NMR data. The results indicated that, this method has a significantly suppression of RFI and random noise, and can well preserve the FID signal. At present, the major limitation of the proposed SVD-based ICNR technique is that the threshold value for interference cancellation needs to be manually selected. Finally, the inversion waveform of the applied alternating magnetic field was given by fitting the processed experimental data.

The high precision multiple degree-of-freedom (DoF) capacitance sensor operating in milli-hertz band is critical for satellite platform inertial sensing of space gravitational wave detection mission, and the amplitude-phase characteristics inconsistency among the capacitive sensing channels may seriously deteriorate the displacement sensing performance. In this paper, a novel architecture of 6-DoF capacitive sensing circuits (CSCs) with digital quadrature demodulation is proposed, and the impact of 6-channel displacement sensing front-ends (DSFs) parameters inconsistency on the output signals and displacement sensing performance is also analyzed. The amplitude-phase inconsistency extraction and in-orbit in-situ calibration technique for the 6-channel capacitance sensor is proposed and verified by an invented ground-equivalent evaluation scheme. Finally, a 6-DoF CSCs prototype for the inertial sensors of “China TianQin Project” has been developed and tested. The results show the mean values and the standard deviations of the translational and rotational displacement detection errors are reduced from 1.32 μm, 5.67 mdeg to 30.86 nm, 86.92 μdeg, and from 18.22 nm, 76.51 μdeg to 12.23 nm, 32.97 μdeg, at the specific carrier frequency, respectively. The method may be utilized to periodically calibrate CSCs to significantly improve the prolonged in-orbit running performance of the inertial sensor.

Recently, nonlinear blind compensation technique has attracted growing attention in array signal processing application. However, due to the nonlinear distortion stemming from array receiver which consists of multi-channel radio frequency (RF) front-ends, it is too difficult to estimate the parameters of array signal accurately. A novel nonlinear blind compensation algorithm aims at the nonlinearity mitigation of array receiver and its spurious-free dynamic range (SFDR) improvement, which will be more precise to estimate the parameters of target signals such as their two-dimensional directions of arrival (2-D DOAs). Herein, the suggested method is designed as follows: the nonlinear model parameters of any channel of RF front-end are extracted to synchronously compensate the nonlinear distortion of the entire receiver. Furthermore, a verification experiment on the array signal from a uniform circular array (UCA) is adopted to testify the validity of our approach. The real-world experimental results show that the SFDR of the receiver is enhanced, leading to a significant improvement of the 2-D DOAs estimation performance for weak target signals. And these results demonstrate that our nonlinear blind compensation algorithm is effective to estimate the parameters of weak array signal in concomitance with strong jammers.

The cognitive radio wireless sensor network (CR-WSN) is experiencing more and more attention for its capacity to automatically extract broadband instantaneous radio environment information. Obtaining sufficient linearity and spurious-free dynamic range (SFDR) is a significant premise of guaranteeing sensing performance which, however, usually suffers from the nonlinear distortion coming from the broadband radio frequency (RF) front-end in the sensor node. Moreover, unlike other existing methods, the joint effect of non-constant group delay distortion and nonlinear distortion is discussed, and its corresponding solution is provided in this paper. After that, the nonlinearity mitigation architecture based on best delay searching is proposed. Finally, verification experiments, both on simulation signals and signals from real-world measurement, are conducted and discussed. The achieved results demonstrate that with best delay searching, nonlinear distortion can be alleviated significantly and, in this way, spectrum sensing performance is more reliable and accurate.

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

Capacitive displacement sensing circuits (CDSCs) are widely used in electrostatic accelerometers and space inertial sensors. High-precision milli-hertz band CDSCs are critical for achieving ultra-quiet and ultra-stable satellite platforms and drag-free control in space-borne gravitational wave (GW) detection missions. In this paper, a kind of software radio architecture-based CDSC scheme with digital quadrature demodulation is proposed, and the near-carrier noise characteristics of the front-end analog receiver of the scheme are also analyzed. The ultra-long continuous-time transient response analysis technique with milli-hertz resolution bandwidth is deeply studied, and a fast time-frequency domain-based transient response calculation method is proposed. Both algorithms can calculate the dynamic response and the hyperfine spectrum of the CDSC analog front end. The time consumed for producing 1024-second transient response is approximately 39,800 seconds and 16 seconds, respectively. Finally, a six-channel CDSC prototype has been developed. The experimental results show that the capacitance detection resolution of the CDSC is lower than 0.126 aF/Hz1/2@1mHz offset carrier frequency, and the output signal-to-noise ratio (SNR) corresponding to input 1-aF dynamic differential capacitance is higher than 40 dB. The test results are in good agreement with the simulated ones and verify the above two analysis techniques.