Global Navigation Satellite System Tracking Research Articles

A robust GNSS sensors in presence of signal blockage for USV application

Unmanned surface vehicle (USV) can navigate autonomously via the global navigation satellite system (GNSS). However, the traditional GNSS scalar tracking loop easily loses lock in low carrier-to-noise ratio (CNR) situations, such as signal occlusion and weak signals. Meanwhile, an increase in the carrier/code phase error leads to an increase in the measurement error of the navigation filter, which decreases the accuracy of the position estimation. To solve this problem, this paper proposes a carrier and code tracking structure based on a forward and backward Kalman filter to dynamically adjust the gain of the vector tracking loop. The carrier and code phase errors calculated by the loop discriminators were linearly transformed into pseudo-range rate and pseudo-range errors after filtering and smoothing, which were used as the measurements of the navigation filter. The signal CNR was used to adaptively adjust the measurement noise covariance matrix of the loop filters. The field tests used a commercial receiver’s navigation solution as the reference. In the stationary test, the proposed structure reduced the localization error by 44.3% compared with the traditional methods. In kinematic experiments, the proposed structure reduced the carrier and code phase errors in a harsh signal environment and improved the positioning accuracy at the source. The test results demonstrate that the proposed GNSS tracking method can provide a possible solution for the development of navigation systems for USV.

Robust factor graph optimisation method for shipborne GNSS/INS integrated navigation system

AbstractRobust Global Navigation Satellite System (GNSS) factors are introduced into a factor graph optimisation based integrated navigation system to address the challenge of occluded GNSS signals during ship navigation, which leads to increased errors in positioning results. To enhance the robustness of the GNSS tracking loop, a vector tracking method is applied to receiver tracking loop. Then, the Mahalanobis distance was employed to assess the pseudorange residual and identify and reject signals that exhibit anomalies. Specifically, the pseudorange residual is computed as the difference between the predicted pseudorange of the GNSS receiver and the measured pseudorange. Using the historical information in the window, robust GNSS factors were constructed for use in the factor graph. The robust factor graph optimisation method for a shipborne GNSS/Inertial Navigation System integrated navigation system was implemented by constructing robust GNSS factors and Inertial Measurement Unit factors. The experimental results confirm that the positioning accuracy of the proposed method is superior to those of the factor graph optimization and extended Kalman filter.

Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking.

This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics.

Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry

AbstractAlthough reflectometry is not the primary application of Global Positioning System (GPS) and similar Global Navigation Satellite Systems (GNSS), fast‐growing GNSS tracking networks has led to the emergence of GNSS interferometric reflectometry technique for monitoring surface changes such as water level. However, scientific‐grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. We present a prototype called Raspberry Pi Reflector (RPR) that includes a low‐cost and low‐maintenance single‐frequency GPS module and a navigation antenna connected to an inexpensive Raspberry Pi microcomputer. A unit has been successfully operating for almost 2 years since March 2020 in Wesel (Germany) next to the Rhine river. Sub‐daily and daily water levels are retrieved using spectral analysis of reflection data. The river level measurements from RPR are compared with a co‐located river gauge. We find an Root‐Mean‐Square Error (RMSE) of 7.6 cm in sub‐daily estimates and 6 cm in daily means of river level. In August 2021, we changed the antenna orientation from upright to sideways facing the river. The RMSE reduced to 3 cm (sub‐daily) and 1.5 cm (daily) with the new orientation. While satellite radar altimetry techniques have been utilized to monitor water levels with global coverage, their measurements are associated with moderate uncertainties and temporal resolution. Therefore, such low‐cost and high‐precision instruments can be paired with satellite data for calibrating, validating and modeling purposes. These instruments are financially (<US$ 150 as of November 2021) and technically accessible worldwide.

An efficient strategy for multi-GNSS real-time clock estimation based on the undifferenced method

Precise satellite clock product is an indispensable prerequisite for the real-time precise positioning service. To meet the requirement of numerous time-critical applications, real-time satellite clock corrections need to be broadcast to users with an update rate of 5 s or higher. With the rapid development of global navigation satellite systems (GNSS) over the past decades, abundant GNSS tracking stations and modern constellations have emerged, and the computation for multi-GNSS real-time clock estimation has become rather time-consuming. In this contribution, an efficient strategy is proposed to achieve high processing efficiency for multi-GNSS real-time clock estimation, wherein undifferenced method based on sequential least square is adopted. In the proposed strategy, parallel data processing and high-performance matrix operations are introduced to accelerate the processing of multi-GNSS clock estimation. The former is based on OpenMP (Open Multi-Processing), while the latter is achieved by the implementation of the Schur complement and the open-source library OpenBLAS. Multi-GNSS observations from 85 globally distributed tracking stations are employed for the generation of real-time precise clock products. The average elapsed time per epoch with the proposed strategy is 0.35, 0.68, and 2.30 s for GPS-only, dual-system, and quad-system solutions, respectively. Compared to the traditional serial strategy, the computation efficiency is significantly improved by 76.0%, 77.3%, and 77.7%, respectively. The accuracy of the estimated clocks is evaluated with respect to IGS final GPS clock products and GFZ final multi-GNSS clock products (GBM0MGXRAP), and multi-GNSS real-time precise point positioning (PPP) experiments are further carried out. All the results indicate that the proposed strategy is efficient, accurate, and can promise high-rate multi-GNSS real-time clock estimation.

A GNSS meta-signal SAGE-based multipath-mitigating loop design

A major challenge suffered by Global Navigation Satellite System (GNSS) receivers is the multipath interference, which results in serious tracking performance degradation and increased positioning errors. Growing evidence suggests that wide-bandwidth signals and multi-antenna technology are set to become two vital factors to improve the GNSS multipath mitigation, leading to the concept of meta-GNSS signal and multi-dimensional processing. To estimate the time delay of line-of-sight (LOS) ray in a multipath environment, we propose a meta-signal Space Alternating Generalized Expectation (SAGE) based locked loop (SAGELL) design, which exploits the high-resolution of the meta-signal together with the characteristics from spatial and temporal domains. The SAGELL design comprises two main setups: the SAGE-based discriminator and combinational loops. Benefiting from the multi-dimensional processing and meta-signal bandwidth, the SAGE-based discriminator first estimates the LOS ray and the multipath rays in single side-band components and achieves the global estimates of the dual side-band components coherently. Aiming to accurately track each ray of the meta-signal, we further close the SAGELL via angle, code phase, and carrier phase locked loops to filter and predict the estimates. The proposed GNSS meta-signal multipath-mitigating loop design allows for GNSS high-precision discrimination and tracking in multipath scenarios, even in the presence of highly correlated rays, as confirmed by the numerical results.

A decision support tool for forwarding operations with sequence-dependent loading

High productivity in forest harvesting requires efficient forwarding. Planning is complicated by multiple choices of routes and their order, the number and types of assortments, the loading sequence, and pile organization at the landing. This paper develops and tests a decision support tool for forwarder routing with sequential co-loading of assortments. Input data are the harvester production file (including Global Navigation Satellite Systems tracking), placement of landing, and machine specifications. The trail network is generated from the harvester production data when devising routes to pick up all log piles, including specific assortments and volumes. Multiple assortments can be loaded along each route, and a certain loading sequence of assortments is preferred and (or) required. Sorting time during co-loading varies, depending on the assortment combinations and bunk loading pattern. Route planning is modeled using a set-partitioning problem, and the solution method is a metaheuristic based on repeated matching. In addition to routes and loading sequences, solutions include the organization of assortment piles at the landing, depending on the total volume of each assortment. The tool produces similar results to those attained by skilled forwarder operators on five clearcuts (3–11 ha) in northern Sweden, when the results are compared with data from actual forwarder production files.

Intelligent driving system at opencast mines during foggy weather

ABSTRACT The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds.

Using GNSS radio occultation data to derive critical frequencies of the ionospheric sporadic E layer in real time

The small-scale electron density irregularities in the ionosphere have a significant impact on the interruptions of Global Navigation Satellite System (GNSS) navigation and the accuracy of GNSS positioning techniques. The sporadic ionospheric E (Es) layer significantly contributes to the transient interruptions of signals (loss of lock) for GNSS tracking loops. These effects on the GNSS radio occultation (RO) signals can be used to derive the global location and intensity of Es layers as a complement to ground-based observations. Here we conduct statistical analyses of the intensity of Es layers, based on the scintillation index S4max from the FORMOSAT-3/COSMIC during the period 2006–2014. In comparison with simultaneous observations from an ionosonde network of five low-to-middle latitude ionosondes, the S4max indices from COSMIC, especially the small values, are linearly related to the critical frequency of Es layers (foEs). An accumulated period of less than 1 h is required to derive the short-term variations in real-time ionospheric Es layers. A total of 30.22%, 69.57% and 98.13% coincident hourly foEs values have a relative difference less than 10%, 30% and 100%. Overall, the GNSS RO measurements have the potential to provide accurate hourly observations of Es layers. Observations with S4max < 0.4 (foEs < 3.6 MHz), accounting for 66% of COSMIC S4 measurements, have not been used fully previously, as they are not easily visible in ground-based ionosonde data.

Diurnal variation of precipitable water vapor over Central and South America

Annual and seasonal diurnal precipitable water vapor (PWV) variations over Central and South America are analyzed for the period 2007–2013. PWV values were obtained from Global Navigation Satellite Systems (GNSS) observations of sixty-nine GNSS tracking stations. Histograms by climate categories show that PWV values for temperate, polar and cold dry climate have a positive skewed distribution and for tropical climates (except for monsoon subtype) show a negative skewed distribution.The diurnal PWV and surface temperatures (T) anomaly datasets are analyzed by using principal components analysis (PCA). The first two modes represent more than 90% of the PWV variability. The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature (T), therefore the temperature and the surface conditions (to yield evaporation) could be the main agents producing this variability; PWV variability in inland stations are mainly represented by this mode. The second mode of PWV variability shows a maximum amplitude at midnight, a possible explanation of this behavior is the effect of the sea/valley breeze. The coastal and valley stations are affected by this mode in most cases. Finally, the “undefined” stations, surrounded by several water bodies, are mainly affected by the second mode with negative eigenvectors. In the seasonal analysis, both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons, while the rest of the year they present a behavior according to their temperature and the surface conditions. As a result, the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.

An Improved Relative GNSS Tracking Method Utilizing Single Frequency Receivers.

The Global Navigation Satellite Systems (GNSS) becomes the primary choice for device localization in outdoor situations. At the same time, many applications do not require precise absolute Earth coordinates, but instead, inferring the geometric configuration information of the constituent nodes in the system by relative positioning. The Real-Time Kinematic (RTK) technique shows its efficiency and accuracy in calculating the relative position. However, when the cycle slips occur, the RTK method may take a long time to obtain a fixed ambiguity value, and the positioning result will be a “float” solution with a low meter accuracy. The novel method presented in this paper is based on the Relative GNSS Tracking Algorithm (Regtrack). It calculates the changes in the relative baseline between two receivers without an ambiguity estimation. The dead reckoning method is used to give out the relative baseline solution while a parallel running Extended Kalman Filter (EKF) method reinitiates the relative baseline when too many validation failures happen. We conducted both static and kinematic tests to assess the performance of the new methodology. The experimental results show that the proposed strategy can give accurate millimeter-scale solutions of relative motion vectors in adjacent two epochs. The relative baseline solution can be sub-decimeter level with or without the base station is holding static. In the meantime, when the initial tracking point and base station coordinates are precisely obtained, the tracking result error can be only 40 cm away from the ground truth after a 25 min drive test in an urban environment. The efficiency test shows that the proposed method can be a real-time method, the time that calculates one epoch of measurement data is no more than 80 ms and is less than 10 ms for best results. The novel method can be used as a more robust and accurate ambiguity free tracking approach for outdoor applications.

A Novel Precise GNSS Tracking Method Without Solving the Ambiguity Problem

The Global Navigation Satellite System (GNSS) precise positioning has drawn increasing attention owing to the growing demand for accurate relative tracking of devices. The carrier phase becomes the most precise measurement available, the solution of carrier phase integer ambiguity is essential for achieving precise GNSS tracking. Methods of searching within the position domain show their advantage over the methods supported ambiguity fixing, e.g., far fewer epochs taken for obtaining the precise solution and proof against to cycle slips. However, the drawbacks of low computation efficiency and also the existence of multi-peak candidates restrict these methods to be utilized in modern GNSS tracking techniques. The novel tracking approach derived in this paper is based on the Segmented Simulated Annealing Modified Ambiguity Function Approach (SSA-MAFA) and the Relative Motion Tracking Method (RMTM). It focuses on reducing the computation time, which is the main drawback of the traditional Ambiguity Function Method (AFM) and giving out the precise relative tracking result efficiently without solving the integer ambiguity fixing problem. We use the SSA-MAFA search method to reduce the computation time, the Kernel Density Estimation (KDE) method to filter out the false peak candidates, and the RMTM method to obtain the precise relative motion vector between two adjacent epochs. Both static and kinematic experiments were carried out to evaluate the performance of the new approach. The static test shows that the RMTM method can give out a millimeter level of accuracy relative motion solution. The kinematic experiment shows that the precise tracking result can be obtained after handling only two epochs of data. Meanwhile, the tracking result of the proposed approach can be a centimeter-level of accuracy, even if the prior position is several meters far from the referenced value.

Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones

Tropical cyclones (TCs) are intense rotating storm systems, characterized by strong winds, heavy humidity and storm surges, and usually cause an abnormal increase in regional water vapor during the period of their occurrence. Global navigation satellite system (GNSS) is well-established and initially mainly for positioning, navigation and timing. However, due to their global coverage and 24/7 operability under all weather conditions, GNSS has been widely used in meteorology such as the retrieval of precipitable water vapor (PWV). Since PWV time series obtained from a regional ground GNSS tracking stations network has high temporal and spatial resolutions, in this research, the temporal and spatial variation trends of regional PWVs during several TCs’ periods in Hong Kong were investigated, and a new method using high-resolution GNSS-PWV time series to estimate a TC’s movement was proposed. Test data were from 15 GNSS tracking stations equipped with meteorological sensors, and three GNSS stations without meteorological sensors but using meteorological data interpolated from ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset, and six TC events occurred in 2017 were selected for cases studies. Moreover, for a better accuracy of GNSS-PWV, which is converted from the zenith wet delay of the GNSS signal multiplying a conversion factor that uses a weighted mean temperature ( $T_{m}$ ), a seasonal multi-factor $T_{m}$ model was established and tested. Results showed that the new $T_{m}$ model outperformed the previous regional $T_{m}$ model used in Hong Kong. In addition, based on the temporal variation trend and spatial distribution of the regional PWV, one can determine if a TC approaches the region. A geometric approach to estimating a TC’s movement was developed and its test results agreed well with the truth. These results suggest the potential of using GNSS signals to monitor the movements of TCs for short-time TC forecasting due to their high temporal resolution.

Optimal GNSS Signal Tracking Loop Design Based on Plant Modeling

Conventional research for signal tracking of the Global Navigation Satellite System (GNSS) uses a loop filter to minimize the effect of measurement noise. Although for a few decades, research into the optimal GNSS tracking loop has been based on similarity between signal tracking and general control loop theory, it has mainly focused on optimal estimator, or shown vulnerability for high dynamic signal tracking. To enhance the performance of the optimal signal tracking loop, this paper proposes new plant modeling for optimal GNSS signal tracking that consists of both optimal estimator and controller. The proposed plant modeling is able to maximize the performance of the optimal signal tracking loop due to the relationships between code and carrier tracking, and between the frequency and phase of the carrier. In addition, the plant clearly defines a relationship between the general control loop and GNSS tracking loop, so that the plant is ready to be applied to various control theories for GNSS signal tracking. To assess the performance of the proposed plant modeling, we implement a linear quadratic Gaussian (LQG) tracking loop based on the new proposed plant and process simulation data. Comparison of the processing results to those of conventional research shows improved performance of the proposed plant modeling.

Geodetic observation crucial to sea level monitoring

The relative rate of rise of the sea levels measured by a tide gauge is made of a sea and a land component. The first is usually restricted to the global short-term effect of melting icecaps and expansion of water mass due to global temperature change. The second is often limited to the regional long-term effects of glacial isostatic adjustment (GIA). Sometimes, the regional subsidence, due to compaction and ground water withdrawal, is considered. Here we show as this assumption of regional subsidence fails to represent the relative sea level patterns of Sandy Hook, NJ, and The Battery, NY, as well as of Venezia Punta Della Salute, Venezia II, Trieste and Trieste II. The subsidence of the tide gauge instrument may only be addressed by the precise monitoring of the tide gauge vs. a Global Navigation Satellite System (GNSS) antenna, even if the GNSS tracking is only recent and not yet very accurate. The relative sea level records are much more complicated than what is thought.

Comparative study of using different ionosphere models in Thailand for single-frequency GNSS users

An ionosphere layer makes inaccurate global navigation satellite systems (GNSS) signal. The ionosphere layer contains free electrons delaying the speed of GNSS signals. A suitable selection of ionosphere model is very useful to eliminate this inaccurate GNSS signal. This article presents a comparative study by applying various ionosphere models in Thailand including: Klobuchar model, Global Ionosphere Maps, Ionosphere Model from Quasi-Zenith Satellite System and local ionosphere model. The local ionosphere model is generated from GNSS observations at with the use of locally available GNSS tracking stations by the Bernese GPS software 5.0. Coordinates derived from the above-mentioned models were compared and statistical tests in two observed scenarios; namely, single-frequency data and ionosphere-free linear combination from dual-frequency data. These experiment results proved that applying the local ionosphere model to the estimation produces the most accurate positioning results. They are similar to the results obtained from an ionosphere-free linear combination, but with significant differences.

Hybrid Geoid Model: Theory and Application in Brazil.

Determination of the ellipsoidal height by Global Navigation Satellite Systems (GNSS) is becoming better known and used for purposes of leveling with the aid of geoid models. However, the disadvantage of this method is the quality of the geoid models, which degrade heights and limit the application of the method. In order to provide better quality in transforming height using GNSS leveling, this research aims to develop a hybridization methodology of gravimetric geoid models EGM08, MAPGEO2015 and GEOIDSP2014 for the State of São Paulo, providing more consistent models with GNSS technology. Radial Basis Function (RBF) neural networks were used to obtain the corrector surface, based on differences between geoid model undulations and the undulations obtained by GNSS tracking in benchmarks. The experiments showed that the most suitable interpolation for correction modeling is the linear RBF. Checkpoints indicate that the geoid hybrid models feature root mean square deviation ± 0.107, ± 0.104 and ± 0.098 m, respectively. The results shows an improvement of 30 to 40% in consistencies compared with the gravimetric geoids, providing users with better quality in transformation of geometric to orthometric heights.

Detection and SISAR Imaging of Aircrafts Using GNSS Forward Scatter Radar: Signal Modeling and Experimental Validation

The use of satellite signals for passive radar detection has become quite attractive in the recent years. This paper focuses on the detection and the shadow inverse forward scatter radar (SISAR) imaging of aircrafts in the passive forward scatter radar (FSR) system using global navigation satellite system (GNSS) satellites as illuminators of opportunity. The signal model derived from GNSS tracking loop is given and its Doppler character is analyzed. Then, by comparing GNSS FSR and ground-based FSR, the SISAR signal model and the imaging method for GNSS FSR are constructed. Experiments using airliners as targets were carried out, in which forward scattering signals of both GPS satellites and BeiDou satellites were captured. The experimental results verified the correctness of the given model as well as the effectiveness of the novel imaging method.

Orbital Filter Aiding of a High Sensitivity GPS Receiver for Lunar Missions

Recent studies have shown that weak Global Navigation Satellite System (GNSS) signals could potentially be used to navigate from the Earth to the Moon. This would increase autonomy, robustness and flexibility of the navigation architectures for future lunar missions. However, the utilization of GNSS signals at very high altitudes close to the Moon can be significantly limited by the very low power levels seen at the receiver’s antenna. This can result in a strongly reduced visibility of the GNSS satellites, which can worsen the already poor relative geometry of the GNSS receiver to the GNSS satellites. Furthermore, during most of a Moon Transfer Orbit (MTO), the very weak GNSS signals are also affected by Doppler shifts and Doppler rates larger than the ones generally experienced on the Earth, due to the much higher relative dynamics between the receiver and the transmitters. As a consequence, commercial GNSS receivers for terrestrial use cannot successfully acquire and track such signals. More advanced architectures and specific implementations are thus required to use GNSS for lunar missions. In this paper we propose the use of an adaptive orbital filter to aid the GNSS acquisition and tracking modules and to strongly increase the achievable navigation accuracy. The paper describes the orbital filter architecture and tests results carried out by processing realistic radio frequency (RF) signals generated by our Spirent GSS 8000 full constellation simulator for a highly elliptical MTO.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.