The operation of underwater vehicles in deep waters is a very challenging task. The use of AUVs (Autonomous Underwater Vehicles) is the preferred option for underwater exploration activities. They can be autonomously navigated and controlled in real time underwater, which is only possible with precise spatio-temporal information. Navigation and positioning systems based on LBL (Long-Baseline) or USBL (Ultra-Short-Baseline) systems have their own characteristics, so the choice of system is based on the specific application scenario. However, comparative experiments on AUV navigation and positioning under both systems are rarely conducted, especially in the deep sea. This study describes navigation and positioning experiments on AUVs in deep-sea scenarios and compares the accuracy of the USBL and LBL/SINS (Strap-Down Inertial Navigation System)/DVL (Doppler Velocity Log) modes. In practice, the accuracy of the USBL positioning mode is higher when the AUV is within a 60° observation range below the ship; when the AUV is far away from the ship, the positioning accuracy decreases with increasing range and observation angle, i.e., the positioning error reaches 80 m at 4000 m depth. The navigational accuracy inside and outside the datum array is high when using the LBL/SINS/DVL mode; if the AUV is far from the datum array when climbing to the surface, the LBL cannot provide accurate position calibration while the DVL fails, resulting in large deviations in the SINS results. In summary, the use of multi-sensor combination navigation schemes is beneficial, and accurate position information acquisition should be based on the demand and cost, while other factors should also be comprehensively considered; this paper proposes the use of the LBL/SINS/DVL system scheme.

Aiming at the problem of nonlinear observation model mismatch and insufficient anti-interference ability of SINS/GNSS integrated navigation system in complex dynamic environment, this paper proposes an adaptive robust filtering algorithm with improved fading factor. Aiming at the problem that the traditional Kalman filter is easy to diverge in severe heave motion and abnormal observation, a multi-source information fusion framework integrating satellite positioning geometric accuracy factor (PDOP), solution quality factor (Q value), effective satellite observation number (Satnum), and residual vector is constructed. The dynamic weight adjustment mechanism is designed to realize the real-time optimization of the fading factor. Through the collaborative optimization of robust estimation theory and adaptive filtering, a dual robust mechanism is constructed by combining the sequential update strategy. In the measurement update stage, the observation weight is dynamically adjusted according to the innovation covariance, and the fading memory factor is introduced in the time update stage to suppress the error accumulation of the model. The experimental results show that compared with EKF, Sage-Husa adaptive filtering and robust filtering algorithms, the three-dimensional positioning accuracy is improved by 47.12%, 35.26%, and 9.58%, respectively, in the vehicle strong maneuvering scene. In the scene of ship-borne heave motion, the corresponding increase is 19.44%, 10.47%, and 8.28%. The research results provide an effective anti-interference solution for navigation systems in high dynamic and complex environments.

To address the insufficient reliability of manned submersible navigation systems in complex deep-sea environments, this paper proposes an adaptive fusion navigation method based on multi-dimensional confidence assessment. This study proposes a method establishing a four-dimensional evaluation framework for the USBL (Ultra-Short Baseline) positioning system. The framework encompasses signal quality, geometric precision, environmental attenuation, and data stability. It enables the quantitative, real-time assessment of system reliability. Consequently, it facilitates an adaptive weight adjustment mechanism. Experimental results demonstrate that under harsh conditions featuring jump point anomalies and data loss, the proposed algorithm achieves an average position error of 1.15 m. This represents a 53.1% improvement over conventional methods, with the enhancement reaching 58.9% in scenarios specifically affected by jump points. The proposed method study effectively enhances the navigation reliability of manned submersibles in complex underwater acoustic environments, thereby demonstrating significant engineering application value.

The stochastic model in Precise Point Positioning (PPP) defines the statistical properties of observations and the dynamic behavior of parameters. An inaccurate stochastic model can degrade positioning accuracy, ambiguity resolution, and other aspects of performance. However, due to the influence of multiple factors, the stochastic model in PPP cannot be precisely predetermined, necessitating the development of an Adaptive Stochastic Model (ASM) based on Variance Component Estimation (VCE). While the benefits of ASMs for PPP float solutions are well documented, their contributions to other performance aspects remain insufficiently explored. This paper presents a comprehensive assessment of an ASM’s impact on PPP. First, the implementation of an ASM using VCE is described in detail. Then, experimental results demonstrate that the ASM effectively captures observational conditions through the estimated variance component factors. It enhances both PPP float and fixed solutions when the predefined stochastic model is inadequate, improves cycle-slip detection by tightening the stochastic model (reducing the missed detection rate from 19% to 8%), and accelerates both direct reconvergence and re-initialization after data gaps, with reconvergence times improved by 18% and 55%, respectively.