Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace.

Ultra-wideband (UWB) is considered as a promising technology in short-distance indoor wireless positioning due to its accurate time resolution and good penetration through objects. Since the functional model of UWB positioning is nonlinear, the optimal solution is generally estimated by the way of continuous iteration. As an iterative descent method of high efficiency, the Gauss–Newton method is widely used to estimate the position. The nonlinear distance equations are linearized, and the solution can be found iteratively. Therefore, the nonlinear least-squares solution is generally biased even if the observations are normally distributed. In outdoor satellite positioning, the ranging distances are long enough so that the bias caused by nonlinearity is very small. However, in UWB positioning, the relative ranging error is large, and the positioning system is prone to become ill-posed, hence the bias due to nonlinearity is not negligible. In this study, both the statistical factor and geometric factor for bias in the nonlinear least-squares estimator of UWB positioning are discussed. In order to assess whether the linearized model is sufficiently approximate for the positioning estimation, a hypothesis test criterion based on Mahalanobis distance is proposed. The simulation and measurement experiments are performed to analyze the factors affecting the bias in UWB positioning. Experimental results are given to demonstrate that the linearization is valid and the bias in UWB positioning estimation can be neglected for the relatively high measurement precision. Moreover, for a positioning configuration, when the anchors are evenly distributed, the amount of nonlinearity is orthogonal to the ranging space of the design matrix, the UWB positioning estimation tends to be unbiased. Meanwhile, the hypothesis test based on Mahalanobis distance is carried out to determine the validity of the linearized model. When the bias is large for UWB positioning, the bias estimate can be used to correct the estimator to guarantee the unbiasedness for UWB positioning. Furthermore, the correction of parameter estimator bias is more effective in the case of relatively low measurement precision or ill-conditioned configuration.

Global surface pressure, terrestrial water storage models, and seabed pressure grids provide valuable support for studying the mechanisms of the nonlinear motion behind GNSS stations. These data allow for the precise identification and analysis of displacement effects caused by environmental loads. This study analyzes GNSS coordinate time series data from 186 ITRF reference stations worldwide over a 10-year period, thoroughly examining the magnitude, spatial distribution, and impact of hydrological, atmospheric, and non-tidal oceanic loading on nonlinear motion. The results indicate that the atmospheric loading effects had a magnitude of approximately ±5 mm in the up (U) direction and ±1 mm in the east (E) and north (N) directions. Moreover, the impact of atmospheric loading on station displacements was more pronounced in high-latitude regions compared with mid- and low-latitude regions. Secondly, the hydrological loading showed a magnitude of approximately ±5 mm in the U direction and ±0.8 mm in the E and N directions, with inland areas causing larger displacements than coastal regions. Furthermore, the non-tidal oceanic loading induced displacements with magnitudes of approximately ±0.5 mm in the E and N directions and ±2 mm in the U direction, significantly affecting stations in the nearshore areas more than inland stations. Subsequently, this study analyzes the corrective effects of environmental loads on the coordinate time series. The average correlation coefficients between the E, N, and U directions and the coordinate time series were 0.35, 0.31, and 0.52, respectively. After removing the displacements caused by environmental loads, the root mean square (RMS) values of the coordinate time series decreased by 85.5% in the E direction, 77.4% in the N direction, and 89.8% in the U direction, with average reductions of 6.2%, 4.4%, and 16.7%, respectively. Lastly, it also comprehensively assesses the consistency between environmental loads and coordinate time series from the perspectives of the optimal noise model, velocity and uncertainty, and amplitude and phase. This study demonstrates that the geographic location of a station is closely related to the impact of environmental loads, with a significantly greater effect in the vertical direction than that in the horizontal direction. By correcting for environmental loads, the accuracy of the coordinate time series can be significantly enhanced.