The vulnerability of civil receivers of the Global Satellite Navigation System (GNSS) to spoofing jamming has raised significant concerns in recent times. Traditional multi-antenna spoofing detection methods are limited in application scenarios and come with high hardware costs. To address this issue, this paper proposes a novel GNSS spoofing detection method utilizing three low-cost collinear antennas. By leveraging the collinearity information of the antennas, this method effectively constrains the observation equation, leading to improved estimation accuracy of the pointing vector. Furthermore, by employing a binary statistical detection model based on the sum of squares (SSE) between the observed value and the estimated value of the pointing vector, real-time spoofing signal detection is enabled. Simulation results confirm the efficacy of the proposed statistical model, with the error of the skewness coefficient not exceeding 0.026. Experimental results further demonstrate that the collinear antenna-based method reduces the standard deviation of the angle deviation of the pointing vector by over 55.62% in the presence of spoofing signals. Moreover, the experiments indicate that with a 1 m baseline, this method achieves 100% spoofing detection.

As a significant space–time infrastructure, the Global Navigation Satellite System (GNSS) provides high-precision positioning, navigation, and timing (PNT) information to users all over the world. However, GNSS real-time kinematic (RTK) mobile receiver signal attenuation is obvious in complex environments such as under trees, urban canyons, and indoors, among others, and it is incapable of meeting the demand of multi-level mass users for indoor and outdoor seamless positioning applications. The goal of this study was to address the limitations and vulnerabilities of the GNSS RTK positioning above-mentioned. First, we propose a GNSS RTK/UWB/DBA fusion positioning model and provide detailed algorithm steps for various types of observations. The performance of the GNSS RTK/UWB/DBA fusion positioning under various occlusion environments is then thoroughly evaluated using static and dynamic cart experiments. The experiment results show that as the elevation mask angle increases, the number of available GNSS satellites decreases and the ambiguity resolution success rate decreases; in comparison to GNSS RTK, the proposed GNSS RTK/UWB/DBA fusion positioning model can significantly improve the spatial geometry distribution of observations, reduce the position dilution of precision (PDOP) value, and improve the ambiguity resolution success rate. At an elevation mask angle of 50 degrees, GNSS RTK/UWB/DBA combination positioning can improve the ambiguity resolution success rate by 20% to 60%, and a positioning error less than 5 cm by 20% to 50%. It also indicates that the GNSS RTK/UWB/DBA fusion positioning model has higher positioning accuracy and can effectively improve the availability and reliability of GNSS RTK in a local harsh environment.

The ultra-wideband (UWB) base station (BS) deployment pattern seriously affects mobile tag positioning accuracy, but the traditional classical deployment methods, such as rectangular and diamond deployment, cannot take into account the influence of non-line-of-sight (NLOS) occlusion, which leads to a blind area in positioning. In this paper, we propose a new UWB BS deployment optimization method that takes into account the influence of NLOS occlusion, determines the BS deployment range and occlusion by indoor map information, uses the locatable points coverage rate in the whole indoor positioning area as the fitness function, and proposes an improved particle swarm optimization algorithm based on the Levy flight strategy (LPSO) to solve the optimization problem. The simulation experiment results show that the locatable space coverage rate of rectangular and diamond deployment models gradually decreases and the blind positioning area gradually increases with the increase in NLOS occlusion. The locatable space coverage rate of the LPSO-optimized deployment is better than that of the standard PSO-optimized deployment model, while it is 19.0% and 22.6% better than the rectangular deployment and 3.0% and 6.5% better than the diamond deployment when the NLOS values are 3 and 5 for complex occlusion environments, respectively. The experimental results of the underground garage demonstrate that the optimal 13 BS layout scheme, obtained through LPSO, outperforms the 7 BS layout scheme by 34.9% while reducing the horizontal dilution of precision (HDOP) values by 81.7%. Therefore, the proposed UWB BS layout optimization scheme exhibits superior adaptability to large and complex indoor environments, effectively enhances signal coverage and positioning accuracy, and holds significant practical value.

In Global Navigation Satellite System (GNSS)-denied environments, navigation using signals of opportunity (SOP) from Low Earth Orbit (LEO) satellites is considered a feasible alternative. Compared with single-constellation systems, multiple-constellation LEO systems offer improved satellite visibility and geometric diversity, which enhances positioning continuity and accuracy. To allocate weights among heterogeneous observations, prior studies have employed the Helmert variance component estimation (HVCE) method, which iteratively determines relative weight ratios of different observation types through posterior variance estimation. HVCE enables error modeling and weight adjustment without prior noise information but is highly sensitive to outliers, making it vulnerable to their impact. This study proposes a Robust HVCE-based dual-constellation weighted positioning method. The approach integrates prior weighting based on satellite elevation, observation screening based on characteristic slopes, HVCE, and IGG-III robust estimation to achieve dynamic weight adjustment and suppress outliers. Experimental results over a 33.9 km baseline demonstrate that the proposed method attains Two-Dimensional (2D) and Three-Dimensional (3D) positioning accuracies of 12.824 m and 23.230 m, corresponding to improvements of 29% and 16% over conventional HVCE weighting, respectively. It also outperforms single-constellation positioning and equal-weighted fusion, confirming the effectiveness of the proposed approach.

Orbit determination in Earth–Moon space is challenged by dynamic-model mismatch and unstable GNSS observation conditions, especially under weak and intermittent signals. To address this issue, this paper proposes a GNSS single-point positioning (SPP)-aided orbit determination method based on adaptive sliding-window filtering. A tightly coupled framework is constructed by integrating orbital dynamics propagation with SPP pseudo-range observations, allowing propagation errors to be corrected in real time through measurement updates. To enhance adaptability under time-varying observation conditions, a dynamic sliding-window strategy is introduced, in which the observation-noise covariance is adjusted according to carrier-to-noise ratio ($C/N_0$) variations. Simulations for three representative Earth–Moon trajectories, including a near-rectilinear halo orbit (NRHO), a distant retrograde orbit (DRO), and a Halo orbit, show that the proposed method significantly outperforms the conventional tightly coupled solution. The three-dimensional RMS position error is reduced from 6.65 m to 1.27 m for NRHO, from 6.57 m to 1.27 m for DRO, and from 5.91 m to 1.44 m for Halo, corresponding to improvements of 80.9%, 80.4%, and 75.4%, respectively. Under a simulated 200-epoch GNSS interruption in the Halo case, the method also improves outage robustness and post-recovery performance, reducing the three-dimensional RMS error by 23.2% in the interruption-centered interval and by 26.1% over the full arc.

Reliable spatial georeferencing of mobile mapping platforms is a fundamental prerequisite for high-fidelity urban remote sensing products such as 3D point clouds and digital twins. However, in deep urban canyons, severe signal occlusion and multipath effects reduce visible GNSS satellites, causing ambiguity resolution (AR) failure and degraded observation geometry for UGV-borne systems. Conventional Vehicle-to-Vehicle (V2V) cooperation offers limited improvement due to symmetric ground-level occlusion. To overcome this, we propose an asymmetric GNSS/UWB fusion method that introduces Unmanned Aerial Vehicles (UAVs) as high-altitude dynamic spatial anchors to reconstruct the 3D observation geometry. Two contributions are presented: (i) an asymmetric heterogeneous stochastic model coupling carrier-to-noise ratio (C/N0) and elevation angle to handle the quality disparity between air and ground sensor links, preventing multipath contamination of high-fidelity UAV observations; and (ii) a dynamic baseline constrained least-squares algorithm integrating Ultra-Wideband (UWB) ranging to stabilize GNSS positioning under high-dynamic relative motion. Validated through high-fidelity simulations and field experiments, the method achieves a 98.2% AR success rate and sub-decimeter 3D accuracy under extreme occlusion (≤3 visible satellites), while urban-canyon tests demonstrate 100% positioning availability across all evaluated epochs and reduce the 95th-percentile 3D error from 7.25 m to 0.19 m under the tested single-UAV/single-UGV configuration. The framework supports smart city modeling, 3D reconstruction, and infrastructure monitoring.