An atomic interference gravimeter (AIG) is of great value in underwater aided navigation, but one of the constraints on its accuracy is vibration noise. For this reason, technology must be developed for its vibration isolation. Up to now, three methods have mainly been employed to suppress the vibration noise of an AIG, including passive vibration isolation, active vibration isolation and vibration compensation. This paper presents a study on how vibration noise affects the measurement of an AIG, a review of the research findings regarding the reduction of its vibration, and the prospective development of vibration isolation technology for an AIG. Along with the development of small and movable AIGs, vibration isolation technology will be better adapted to the challenging environment and be strongly resistant to disturbance in the future.
This paper proposes an improved method to accurately and expediently detect water columns at the shells’ impact point. The suggested method combines a lightweight depthwise convolutional neural network (MobileNet v3) with the You Only Look Once X (YOLO X) algorithm, namely, YOLO X-m (MobileNet v3) that aims to simplify the network’s structure. Specifically, we used a weighted average pooling network and a spatial pyramid pooling network comprising multiple convolutional layers to retain as many features as possible. Moreover, we improve the activation and loss functions to reduce network calculations and afford better precision as well as fast and accurate water column detection. The experimental results reveal that YOLO X-m (MobileNet v3) ensures a good detection performance and adaptability to various light intensities, distances, and multiple water columns. Compared with the original YOLO X-m model, the improved network model achieves a 75.76% frames per second improvement and a 71.11% capacity reduction, while its AP50decreases by only 1.29%. The proposed method is challenged against the single shot multibox detector and various YOLO variants, revealing its appealing accuracy, real-time detection performance, and suitability for practical applications and projects.
The information on the earth’s gravity provides significant strategic support for the national economy, defense, and security. Atomic gravimeter (AG) realizes highly precise measurements of gravitational acceleration by virtue of atomic interference. Vibration noise is a strong contributor to the limitation on the measurement sensitivity and accuracy of an AG. Vibration compensation enhances the environmental adaptability of an AG since it can facilitate the measurement of gravity when an isolation platform is unavailable. A dynamic compensation filter was devised for the correction of the data output from a seismometer, which expanded the bandwidth of the seismometer and lowered the distortion of vibration signals. Additionally, transfer function estimation was introduced to better reflect the actual vibration of the Raman mirror. Based on the simplified transfer function model, this method can modify the interference fringes of the AG in real-time. The experimental results exposed that the proposed optimization method could make the cosine fitting phase uncertainty of interference fringes attenuate by up to 85.91%, and reach the uncertainty of about 76.37 μGal in a complicated vibration environment. The measurement accuracy was effectively improved by the proposed method. After all, it was verified that the proposed method was effective and adaptable in a complicated environment.
Atomic gravimeter has been more frequently applied under complex and dynamic environments, but its measurement accuracy is seriously hampered by vibration-induced noise. In this case, vibration compensation provides a way to enhance the accuracy of gravity measurements by correcting the phase noise that resulted from the vibration of a Raman reflector, and improving the fitting of an interference fringe. An accurate estimation of the transfer function of vibration between the Raman reflector and the sensor plays a significant role in optimizing the effect of vibration compensation. For this reason, a vibration compensation approach was explored based on EO (equilibrium optimizer) for estimating the transfer function simplified model of a Raman reflector, and it was used to correct the interference fringe of an atomic gravimeter. The test results revealed that this approach greatly restored the actual vibration of the Raman reflector in a complex vibration environment. With a vibration compensation algorithm, it achieved the correction and fitting of the original interference fringe. In general, it dramatically reduced the RMSE (root mean square error) at the time of fitting and significantly improved the residual error in the gravity measurement. Compared with other conventional algorithms, such as GA (genetic algorithm) and PSO (particle swarm optimization), this approach realized a faster convergence and better optimization, so as to ensure more accurate gravity measurements. The study of this vibration compensation approach could provide a reference for the application of an atomic gravimeter in a wider and more complex environment.
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