In underwater navigation systems, Global Navigation Satellite System (GNSS) information cannot be used for navigation. The mainstream method of autonomous underwater vehicles (AUV) underwater navigation system is Doppler Velocity Log (DVL) aided strapdown inertial navigation system (SINS). However, because the DVL is an instrument based on Doppler frequency shift to measure velocity, it is easily affected by the external environment. In a complex underwater environment, DVL output is easily polluted by outliers or even interrupted. In this paper, A new integrated navigation algorithm based on deep learning model is proposed to deal with DVL malfunctions. First, use RKF based on Mahalanobis distance algorithm to eliminate outliers, and then train the Nonlinear AutoRegressive with eXogenous input (NARX) model when DVL is available. When DVL is interrupted, use the NARX model to predict the output of DVL and continue integrated navigation. The proposed NARX-RKF scheme’s effectiveness verification was performed on the data set collected by the SINS/DVL ship-mounted experimental system. For comparison, different methods are also compared in the experiment. Experimental results show that NARX-RKF can effectively predict the output of DVL and is significantly better than other methods.

Kalman filter (KF), as an effective method to solve the attitude estimation problem in the initial alignment process of strapdown inertial navigation system (SINS), has been widely used in engineering practice. Unfortunately, in the complex underwater environment, the measurement is easily contaminated by outliers, and the measurement noise covariance is unknown or time-varying, which will degrade the performance of KF severely. To solve this problem, this paper proposes an improved KF with both robustness and adaptivity (namely VBRAKF), and applies it to the in-motion attitude estimation for Doppler velocity log (DVL) aided SINS. In the VBRAKF, the robustness is achieved by suppressing the outliers based on Mahalanobis distance (MD) of the innovation, and the adaptivity is achieved by estimating the uncertain measurement noise covariance based on variational Bayesian (VB) approximation simultaneously. The SINS/DVL shipboard tests were carried out when the outputs of DVL are contaminated by outliers or Gaussian mixture distribution noise, respectively. The tests results demonstrate the superiority of the proposed method over the traditional methods.

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations.

The dither mechanism in a ring laser gyroscope (RLG) produces a lateral deformation when subjected to external acceleration, which is called the g-sensitive misalignment. This article considers the g-sensitive misalignment mechanism, examines the calibration coupling error and the navigation error resulting from it, and proposes a system-level calibration method based on the g-sensitive misalignment. The feasibility of the proposed calibration method is verified by the calibration simulations and experiments using a turntable. The results of a laboratory navigation test and an in-vehicle navigation experiment show that when the g-sensitive misalignment is considered and compensated in the calibration process, the navigation error can be significantly reduced. This article concludes that the g-sensitive misalignment of the RLG should not be disregarded. Finally, it is shown that using an efficient calibration method considering the g-sensitive misalignment can enhance the navigational performance of a high-precision RLG rotational inertial navigation system.

The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.

In a high-precision ring laser gyro (RLG) inertial navigation system (INS), the g-sensitive misalignment caused by external acceleration represents one of the main error sources except for the conventional inertial measurement unit (IMU) errors (e.g., gyro drift, accelerometer bias, installation error, and scale factor error) and, thus, cannot be ignored. However, in related literature, there have been no effective suppression methods for the g-sensitive misalignment. To solve this shortcoming, this paper proposes an innovative dual-axis rotation modulation navigation scheme to suppress the velocity and position errors caused by the g-sensitive misalignment, as well as conventional IMU errors. First, an error model of the g-sensitive misalignment is constructed. Then, this model is used to analyze the error propagation law of the g-sensitive misalignment in a conventional dual-axis rotation scheme. Further, based on the error propagation characteristics of each error source, the design principles of rotational modulation schemes are defined. Moreover, a 32-position dual-axis rotational modulation scheme is designed to reduce all errors, including the g-sensitive misalignment in RLG INSs. The effectiveness of the proposed rotation scheme is verified by simulations and experiments. The laboratory experimental results show that the navigation accuracy of the proposed method in this paper is improved by 29.8% and 28.3% compared to the conventional 16-position scheme and 64-position scheme, respectively. The in-vehicle experiments show that the proposed method improves the accuracy by 32.7% over the 64-position scheme. The results indicate that the g-sensitive misalignment suppression is necessary in the RLG INS long-endurance autonomous navigation, and a reasonable rotational modulation scheme can effectively improve navigation accuracy.