With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

The Global Navigation Satellite System (GNSS)/Inertial Measurement Unit (IMU) Loosely Coupled (LC) integration framework has been widely adopted due to its simple structure, but it relies on complete GNSS position and velocity solutions, and the rapid accumulation of IMU errors can easily lead to navigation failure when fewer than four satellites are visible. In this paper, GNSS Doppler observations are fused with IMU attitude information within an LC framework. An inter-satellite differential Doppler model is introduced, and the velocity obtained from the differential Doppler solution is transformed into the navigation frame using the IMU-derived attitude, enabling three-dimensional velocity estimation in the navigation frame even when only two satellites are available. Analysis of real vehicle data collected by the GREAT team at Wuhan University shows that the Signal-to-Noise Ratio (SNR) and the geometric relationship between the Satellite Difference Vector (SDV) and the Receiver Motion Direction (RMD) are the dominant factors affecting velocity accuracy. A multi-factor threshold screening strategy further indicates that when $\mathrm{S}\mathrm{NR}>$ 40 and $\mid \mathrm{SDV}·\mathrm{RMD}\mid >0.2$, the Root Mean Square (RMS) of the velocity error is approximately 0.3 m/s and the data retention rate exceeds 44%, achieving a good balance between accuracy and availability. The results indicate that, while maintaining a simple system structure, the proposed Doppler–IMU fusion method can significantly enhance velocity robustness and positioning continuity within an LC architecture under weak GNSS conditions (when more than two satellites are visible but standalone GNSS positioning is still unavailable), and is suitable for constructing low-cost, highly reliable integrated navigation systems.

Under regional environmental conditions such as open-pit mines and construction sites, there are usually fixed GNSS measurement points. Around these fixed stations, there are also mobile GNSS measurement modules. These mobile measurement modules offer advantages such as low power consumption, low cost, and large data volume. However, due to their low accuracy, these modules can only provide approximate positions as monitoring data, such as for vehicle management in open-pit mines. To extract more information from the existing large volume of low-accuracy data, it is necessary to process these low-accuracy data. Under conditions of the same time and space in a small area, factors affecting measurement accuracy can be comprehensively considered. By analyzing the temporal GNSS data within the same spatiotemporal small region and understanding the variation patterns of measurement errors, a general equation for measurement error variation can be formulated. Using filtering methods, the data quality can be improved. Through the analysis of the experimental data in this study, it was found that the variation patterns of measurement data obtained by devices of the same accuracy during the same time period are generally consistent. After applying filtering methods, the measurement accuracy of each station improved by up to approximately 95.9%, with a minimum improvement of approximately 84.4%. Under the condition of a 95% confidence level, the reliability increased by up to approximately 73.2%, with a minimum improvement of approximately 58.2%. These experimental results fully demonstrate that under regional spatiotemporal conditions, the temporal data obtained by GNSS measurement devices with similar accuracy exhibit similar error distribution patterns. Applying the same filtering method can significantly improve the accuracy and reliability of measurement data.

Node-state uncertainty in dynamic spatial networks is commonly characterized by posterior covariance. However, under non-Gaussian outliers, unmodeled systematic biases, and geometric degeneracy, a small posterior covariance does not necessarily correspond to high physical reliability and may lead to overconfident assessments of anomalous nodes. To address the inability of a single uncertainty indicator to adequately support node-state quality evaluation and anomaly-related decision making, this paper proposes a measure-theoretic framework for formalizing node-state quality in dynamic spatial networks. First, the conventional node-state is extended to a generalized state tuple comprising the posterior state estimate, covariance structure, observation evidence set, and environmental metadata, and the posterior reliability that the node belongs to the physically valid state domain is defined as the theoretical quality. Second, a four-dimensional evidence vector consisting of residual consistency, posterior uncertainty, geometric constraint balance, and environmental stability is constructed. A computable quality measure is then obtained through direction-consistent normalization and Soft Logical AND product coupling, satisfying boundedness, direction-consistent monotonicity, and the marginal veto property. On this basis, the inheritance of component-wise convergence by the aggregated quality measure is analyzed and an adaptive observation stopping criterion combining the quality level with marginal variation is established. Controllable quality sequences are constructed according to statistically characterized degradation patterns, including heavy-tailed errors and abnormal degradation modes observed in UrbanNav and M2DGR datasets, for numerical verification. The results show that, while maintaining a comparable mean score for normal nodes, the proposed method reduces the mean scores of outlier-contaminated, geometrically degraded, and overconfident-biased nodes from 0.6151, 0.6371, and 0.6569 under the linear model to 0.4910, 0.4303, and 0.4885, respectively. The corresponding false acceptance rates due to overestimation decrease from 10.14%, 23.46%, and 58.04% to 1.22%, 1.94%, and 1.96%, respectively. Under the current parameter settings, 73.74% of the normal nodes trigger adaptive stopping, whereas none of the three anomalous node types incorrectly triggers acceptance-based stopping. These results indicate that the proposed framework limits the compensation of locally failed evidence by high-quality components and provides a unified quantitative basis for node-state quality evaluation, anomalous-node screening, and observation-process management in dynamic networks.