Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.

This paper addresses the collaborative localization problem for unmanned surface vehicle (USV) clusters with random measurement delays. We propose a Cubature Kalman Hybrid Consensus Filter (CKHCF) based on the cubature Kalman filter (CKF) for widely distributed USV clusters lacking global communication capabilities. In this approach, each USV exchanges two pairs of information with all its neighbors and recalculates the received localization data based on distance and relative angle measurements. The recalculated information is then fused with the locally filtered data and updated to obtain localization information based on global measurements. To mitigate the impact of random measurement delays, we employ one-step prediction to compensate for delayed measurements. We present the derivation of the CKHCF algorithm and prove its consistency and boundedness using mathematical induction. Finally, we validate the effectiveness of the proposed algorithm through simulation experiments.

The soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these models is also evaluated. The results indicate a high consistency among the prediction results of the different models. The soft coral order is primarily distributed in the Thousand Islands Basin, Japan Trench, and Thousand Islands Trench. Water depth and silicate content are identified as important environmental factors influencing the distribution of Alcyonacea. The RF, Maxent, and XGBoost models demonstrate high accuracies, with the RF model exhibiting the highest prediction accuracy. However, the Maxent model outperforms the other three models in data processing. Developing a high-resolution, high-accuracy, and high-precision habitat suitability model for soft corals can provide a scientific basis and reference for China’s exploration and research in the deep sea field and aid in the planning of protected areas in the high seas.

With the increasing scale of deep-sea oil exploration and drilling platforms, the assessment, maintenance, and optimization of marine structures have become crucial. Traditional detection and manual measurement methods are inadequate for meeting these demands, but three-dimensional laser scanning technology offers a promising solution. However, the complexity of the marine environment, including waves and wind, often leads to problematic point cloud data characterized by noise points and redundancy. To address this challenge, this paper proposes a method that combines K-Nearest-Neighborhood filtering with a hyperbolic function-based weighted hybrid filtering. The experimental results demonstrate the exceptional performance of the algorithm in processing point cloud data from offshore oil and gas platforms. The method improves noise point filtering efficiency by approximately 11% and decreases the total error by 0.6 percentage points compared to existing technologies. Not only does this method accurately process anomalies in high-density areas—it also removes noise while preserving important details. Furthermore, the research method presented in this paper is particularly suited for processing large point cloud data in complex marine environments. It enhances data accuracy and optimizes the three-dimensional reconstruction of offshore oil and gas platforms, providing reliable dimensional information for land-based prefabrication of these platforms.

In order to solve the problem of fixed ambiguity and decreased accuracy in GNSS displacement monitoring of the offshore floating platforms, an attitude correction algorithm based on the fusion of a multi-antenna GNSS and an accelerometer was proposed using the Kalman filtering method. The algorithm was validated on a physical simulation platform and a real offshore floating platform. The results indicate that this fusion method effectively compensates for the loss of high-frequency displacement information caused by low GNSS sampling rates, improves situations in which the fusion effect deteriorates due to attitude changes, and enhances the accuracy of GNSS and accelerometer fusion monitoring through offshore buoy testing.