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.

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.

In this paper, a rapid alignment method for high-precision rotation inertial navigation system (RINS) within group affine is proposed based on the Lie group theory. The proposed method can effectively solve the ultimate contradiction of initial alignment: speed and accuracy. It meets the requirements of fast response and high accuracy of high-precision RINS. We abandon the conventional coarse alignment + fine alignment scheme and realize a linear Kalman filter alignment method with arbitrarily initial misalignment based on the Earth-centered Earth-fixed frame RINS mechanization and the Lie group theory. Subsequently, considering the error characteristics of RINS two-position alignment, new left-invariant error model and observation model satisfying the group affine property are constructed to improve the convergence speed and accuracy of alignment. It is verified that the method can complete the initial alignment of arbitrary large misalignment and reach the ultimate accuracy in a short time, and the land vehicle navigation test shows that the proposed alignment method can complete the initial alignment of arbitrary misalignment in 5 min under the swaying disturbance and achieve better pure inertial navigation accuracy compared with the traditional scheme.

Inertial navigation systems (INS) are able to continuously provide the attitude, velocity, and position of the vehicle with high accuracy, nevertheless, the accelerometer scale factor asymmetry and the temperature-related accelerometer errors can cause significant navigation errors. To solve this problem, in this paper, a novel system-level calibration method is proposed, which achieves simultaneous compensation of accelerometer asymmetric errors and second-order temperature-related errors. First, the error model including accelerometer asymmetry and temperature-related errors is constructed. Then, a 51-dimensional Kalman filter is applied to estimate all error factors of accelerometers including biases, scale factor errors, installation errors, scale factor asymmetry, first-order and second-order temperature-related error coefficients at the same time. The effectiveness of the calibration method is proved by both simulation and experiment. The pure inertial navigation experiment results illustrated that, compared with the traditional 30-dimensional calibration, our method can reduce the root mean squares of radial position error and radial velocity error by 50% and 39%, respectively, which indicates that the proposed method can be used to improve the navigation performance of ground or aerial vehicles.

Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments.

Initial alignment plays a pivotal role in inertial navigation systems, as even small orientation errors introduced at startup can significantly degrade subsequent positioning and attitude estimates. In this context, we propose a novel inverse alignment method for rotational inertial navigation that leverages the group affine property and high-speed computing to accelerate and refine the alignment process. Adopting inverse navigation and Lie group theory, we derive a left-invariant error model in the geocentric geosynchronous coordinate framework and rapidly achieve alignment by integrating forward and inverse Kalman filtering. During 2.5-h in-vehicle tests, our approach reduced both the maximum error and CEP (Circular Error Probable 50%) by 60% compared to standard alignment methods, and it surpassed the performance of conventional group affine alignment by improving accuracy by 7.2% and 20%, respectively. These results highlight the method’s ability to deliver swift, precise alignment across diverse initial misalignment angles, offering significant benefits for modern high-precision inertial navigation applications.