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.

This paper addresses the challenge of mitigating low-frequency flow noise signals in autonomous underwater vehicles through the optimization of a placoid-scale skin. Drawing inspiration from the bio-inspired surface features of cylindrical shell structures, an enhanced design of placoid-scale skin is developed using 3D printing technology. This improved structure effectively reduced boundary layer vortices and wake intensity, thereby contributing to the suppression of low-frequency flow noise signals. Experimental results demonstrate that the notable reduction in low-frequency flow noise within the frequency range of 0–500 Hz, with average noise reduction of approximately 5 dB observed at 150 Hz. This reduction is validated by a combination of numerical simulations and experimental testing, confirming the efficacy of the optimized placoid-scale skin in attenuating the low-frequency flow noise associated with uniformly advancing turbulent boundary layers underwater.

The problem of dynamic performance in accelerometer is a topic that has not reached a consensus in terms of definition, standards, experiment and evaluation. In view of the problem that traditional methods of evaluating the dynamic performance of accelerometer involve single indicators and that time and frequency indicators are not related, thus being ineffective in evaluating accelerometer, a method based on multiple indices in the time and frequency domain for evaluating the dynamic performance of accelerometer is proposed. Firstly, the accelerometer system structure and mathematical model are introduced, and the dynamic response test and analysis of the accelerometer are performed. Performance indices are defined separately in the time and frequency domains. Secondly, the accelerometer’s deterministic and stochastic performance is analyzed based on the defined indices. Finally, a method for evaluating the dynamic performance of the accelerometer is proposed, and a test platform is set up to validate the proposed method of evaluating the dynamic performance of the accelerometer. Simulated and experimental results demonstrate that the proposed method, taking into account the time and frequency domain performance and the accuracy of the sensors and their application systems, can effectively evaluate the dynamic performance of the accelerometer.

It is very important for non-stationary signal processing, signal measurement, and signal enhancement to improve signal-to-noise ratio (SNR) and suppress noise. For complex non-stationary signals measurement, there is still a lack of effective methods to improve the signal-to-noise ratio without damaging original signal. Therefore, it is vital to develop signal processing method to improve SNR of non-stationary signal in many actual industrial measurement. This paper proposes a new method to enhance the SNR for non-stationary signal processing. First, a self-adapting mathematic model is presented to describe the short-time impulse with board spectrum, judge the non-stationary part of the signal, and determine the length of time window based on itself. Then, for the non-stationary signals in this short time window, the improved self-adaptive modulation is used to remove the non-stationary components and retain the stationary components. Third, the stationary component of the short-time window and the normal part of the original signal are connected via cubic spline interpolation algorithm. Finally, this paper analyzes the range of SNR improvement and measurement accuracy of the new method. Based on the results of experiment, the proposed method can effectively improve the signal-to-noise ratio of non-stationary signals and suppress noise, which is of great significance for measurement.

Large dynamic range (DR) is one of the primary requirements in fiber-optic acoustic sensing systems, wherein acoustic signals are converted into phase-modulated (PM) signals for detection. In the signal transduction stage, research has been conducted to increase the phase sensitivity. This allows for the conversion of acoustic signals into PM signals with higher phase modulation indices (PMIs), therefore improving the detection of weak acoustic signals. However, higher PMIs induced by strong acoustic signals correspondingly broaden the signal bandwidth. A phase demodulation structure that provides both high-bandwidth capability and low-noise performance is needed to improve both the upper and lower limits of DR. To solve the contradiction between phase demodulation noise and bandwidth, we proposed a novel phase demodulation structure featuring an enhanced phase acquisition method and an enhanced feedback control algorithm. The enhanced phase acquisition method combines high-precision analog-to-digital converter (ADC) sampling data with high-frequency comparator data, enhancing the capability to capture dynamic phase variations. The enhanced feedback control algorithm incorporates a predictive control method using high-order signal models and Kalman Filter (KF) iteration to optimize phase tracking performance. With the proposed demodulation structure, phase variation in the residual signal is reduced and the residual signal bandwidth is compressed before sampling. This combined approach effectively balances noise and bandwidth. The performance of the proposed structure is validated through simulations and experiments, achieving a DR of 170.1 dB@1 kHz with a phase resolution of 4×10 ^-6 rad/ √ Hz in practical tests. Based on our estimation, the optimal DR is approximately 211.5 dB@1 kHz.