The traditional temperature modeling method is based on the full heating of the accelerometer to achieve thermal balance, which is not suitable for the cold start-up phase of the micro-accelerometer. For decreasing the complex temperature drift of the cold start-up phase, a new temperature compensation method based on a high-order Fourier transform combined model is proposed. The system structure and repeatability test of the micro digital quartz flexible accelerometer are provided at first. Additionally, we analyzed where the complex temperature drift of the cold start-up phase comes from based on the system structure and repeatability test. Secondly, a high-order temperature compensation model combined with K-means clustering and the symbiotic organisms search (SOS) algorithm is established with repeatability test data as training data. To verify the proposed temperature compensation model, a test platform was built to transmit the measured values before and after compensation with the proposed Fourier-related model and the other time-related model, which is also a model aiming at temperature compensation in the cold start-up phase. The experimental results indicate that the proposed method achieves better compensation accuracy compared with the traditional temperature compensation methods and the time-related compensation model. Furthermore, the compensation for the cold start-up phase has no effect on the original accuracy over the whole temperature range. The stability of the accelerometer can be significantly improved to about 30 μg in the start-up phase of different temperatures after compensation.

Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time–frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

Cables or slip-rings are often used to power loads on a rotating unit in the rotation modulated inertial navigation system (RMINS). However, these power supply methods have the disadvantages of cable winding and slip ring friction and wear, which reduces the reliability and life of the RMINS. Therefore, this paper applies magnetic coupling resonant wireless power transfer (MCRWPT) technology to the RMINS to avoid the shortcomings of the above power supply methods. Furthermore, according to the structure and working characteristics of the RMINS, a simple design method of the MCRWPT system without any feedback control is proposed. Based on the ANSYS simulation, the magnetic shielding structure is designed to reduce magnetic leakage, and the efficiency of the MCRWPT system is optimized by designing the excitation frequency. Experiments verify the effectiveness of the proposed method. The experimental results show that the designed MCRWPT system can achieve an efficiency of 74.6% with an output power of 10 W and has been successfully applied to the uniaxial rotation module inertial navigation system. Finally, the design method of the MCRWPT system is simple, and it has guiding significance for the design of the wireless power transfer system in the RMINS.

In advanced sensor system designs, one of the common methods involves converting target physical quantities into phase/frequency-modulated signals. Then, the in-phase/quadrature-phase (I/Q)-based phase demodulation structure is mainly employed to enhance flexibility and efficiency in obtaining the phase information. However, the presence of I/Q imbalance and additive noise in analog I/Q channels greatly degrade the overall system detection performance. Specifically, we investigated the detailed degradation of signal detection, including the introduction of unwanted interference frequency components, elevated noise floor, and distortion of the target signal. To solve this problem, we proposed a novel controlled-carrier closed-loop structure to realize the phase demodulation. This structure, built upon the zero-error closed-loop structure, further mitigates the imbalance distortion and optimizes the noise performance by modifying the control method. The detailed structure and key issues are provided. Further, the effectiveness and enhanced performance of the proposed structure are verified by simulation and experiments. The experimental results align with the simulations and it shows that the proposed structure can achieve enhanced imbalance-distortion mitigation and noise suppression without the need for complex I/Q estimation and compensation.

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.