For the integration of global navigation satellite system (GNSS) and inertial navigation system (INS), real-time and accurate fault detection is essential to enhance the reliability and precision of the system. Among the existing methods, the residual chi-square detection is still widely used due to its good real-time performance and sensibility of fault detection. However, further investigation on the performance of fault detection for different observational conditions and fault models is still required. In this paper, the principle of chi-square detection based on the predicted residual and least-squares residual is analyzed and the equivalence between them is deduced. Then, choosing the chi-square detection based on the predicted residual as the research object, the influence of satellite configuration and fault duration time on the performance of fault detection is analyzed in theory. The influence of satellite configuration is analyzed from the number and geometry of visible satellites. Several numerical simulations are conducted to verify the theoretical analysis. The results show that, for a single-epoch fault, the location of faulty measurement and the geometry have little effect on the performance of fault detection, while the number of visible satellites has greater influence on the fault detection performance than the geometry. For a continuous fault, the fault detection performance will decrease with the increase of fault duration time when the value of the fault is near the minimal detectable bias (MDB), and faults occurring on different satellite’s measurement will result in different detection results.

Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.

A direction of arrival (DOA) estimator for two-dimensional (2D) incoherently distributed (ID) sources is presented under proposed double cross arrays, satisfying both the small interval of parallel linear arrays and the aperture equalization in the elevation and azimuth dimensions. First, by virtue of a first-order Taylor expansion for array manifold vectors of parallel linear arrays, the received signal of arrays can be reconstructed by the products of generalized manifold matrices and extended signal vectors. Then, the rotating invariant relations concerning the nominal elevation and azimuth are derived. According to the rotating invariant relationships, the rotating operators are obtained through the subspace of the covariance matrix of the received vectors. Last, the angle matching approach and angular spreads are explored based on the Capon principle. The proposed method for estimating the DOA of 2D ID sources does not require a spectral search and prior knowledge of the angular power density function. The proposed DOA estimation has a significant advantage in terms of computational cost. Investigating the influence of experimental conditions and angular spreads on estimation, numerical simulations are carried out to validate the effectiveness of the proposed method. The experimental results show that the algorithm proposed in this paper has advantages in terms of estimation accuracy, with a similar number of sensors and the same experimental conditions when compared with existing methods, and that it shows a robustness in cases of model mismatch.

Dense 3D reconstruction and accurate pose measurement of non-cooperative satellites are essential for on-orbit servicing missions. Multi-view stereo (MVS) is the preferred approach for high-precision dense reconstruction from monocular imagery, yet current methods struggle with the speed–accuracy trade-off and reconstruction degradation under complex on-orbit conditions, while keypoint-based pose measurement is further constrained by limited training data and heavy reliance on manual annotations. To address these challenges, we present a monocular vision-based method coupling dense 3D reconstruction with pose estimation. We develop MambaMVS, an end-to-end MVS network featuring a Mamba Cost Volume (MCV) Module for efficient cost volume regularization via the linear complexity of structured state space models, and an improved focused linear attention module for robust cross-view feature matching. The reconstructed point cloud provides 3D keypoint coordinates for pose measurement, where a few-shot scheme employing 3D Gaussian Splatting-based view augmentation and reprojection-driven self-labeling enables training with minimal manual annotations. During online inference, detected 2D keypoints are combined with offline-reconstructed 3D coordinates via the Perspective-n-Point algorithm for fast solving of the 6-DOF pose. Experiments on the Customized Space Target dataset demonstrate state-of-the-art reconstruction quality with 1.5× faster inference than current leading methods. Physical satellite model experiments achieve rotation ME within 0.2° and translation ME below 0.01 m, while robustness is validated across varying illumination, reflective surfaces, motion blur, and occlusion. The proposed reconstruction-guided measurement paradigm offers a practical pathway toward autonomous on-orbit perception and in situ structural assessment.

Far-field vision-based pose measurement is a crucial technology for applications such as high-resolution Earth observation and space security early warning. However, owing to the perspective imaging model of long-range optical systems, conventional vision-based pose measurement methods are highly susceptible to image noise and pose parameter coupling, leading to significant estimation deviations. Consequently, these methods fail to meet the rigorous requirements for the accurate measurement and intelligent perception of object poses in far-field scenarios, particularly when the object distance significantly exceeds the focal length. To address these challenges, this paper presents a Perspective-n-Point (PnP) preprocessing and post-optimization method for far-field pose measurement based on weighted central normalization. First, the Robust PnP (RPnP) algorithm is employed to obtain an initial pose for the far-field object, and an objective function is formulated by minimizing the reprojection error of the image feature points. Second, central normalization is applied to the Jacobian matrix of the pose parameters, and the information matrix is weighted according to the localization uncertainty of the image feature points. Finally, a weighted nonlinear optimization is executed to obtain refined pose parameters. Under the tested conditions, this approach can reduce the sensitivity of the pose parameters to image noise, minimizes the coupling among extrinsic parameters, and reduces the tendency of noise-driven pose-update excursions. The proposed method is evaluated through simulations and scaled physical relative-comparison experiments, supporting its potential for numerically stable vision-based pose measurement of far-field objects in aerospace and related domains. Noise-and-turbulence simulations demonstrate the pose-refinement benefit of CS and improved rotation estimation with a known spatial covariance model.