RTK Performance in Highway and Racetrack Experiments

Reusable launch vehicles (RLV) in a vertical take-off vertical landing configuration require accurate measurements of the height above the ground during the final descent just before landing. It is proposed to use real-time kinematic positioning (RTK), which is a differential GNSS navigation method requiring a nearby reference GNSS antenna and receiver, to measure the rover's position with centimetre accuracy. The effects of the engine plume, vibration and multipath are challenging when using this carrier-phase based positioning method on an RLV. Especially the residual differential tropospheric error due to the rover and reference GNSS antennas being at different heights, primarily caused by humidity in the air, may hinder the successful fixing of the carrier phase ambiguities. To gain a better understanding of the RTK performance to be expected on a landing RLV, a skydiver experiment was conducted in April 2022. The skydiver was equipped with a rover GNSS receiver and a helmet with a GNSS antenna on top and an independent integrated navigation system serving as reference. The skydiver reached free fall velocities of up to 385 km/h, which is seen representative of the vertical velocity of an RLV. The paper describes the experiment procedure and the results of the experiment.

Tropospheric delay is a major error source that affects the performance of the Global Navigation Satellite Systems (GNSS) Real Time Kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by GNSS carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new GPS/BDS RTK algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from NWP is assessed through comparisons with the results of GAMIT (GNSS at MIT). The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the NWP and GAMIT zenith tropospheric delay (ZTD) are between −5.50 mm and 5.60 mm, and the RMS values of the NWP ZTD residuals are from 24.02 mm to 32.62 mm. A reduction in the initialization time of over 41% and 58% for medium- and long-range baselines can be achieved with the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) compared to the standard RTK solution, respectively. An improvement of over 30% can be found with the GPS/BDS RTK compared with that of the GPS alone RTK for both standard and the NWP-constrained modes. The positioning precision of NWP-constrained GPS/BDS RTK is better than 3 cm in the horizontal direction and better than 5 cm in the vertical direction, which satisfies the requirement of the precise positioning service.

To evaluate the performance of real time kinematic (RTK) network algorithms without applying actual measurements, a new method called geometric precision evaluation methodology (GPEM) based on covariance analysis was presented. Three types of multiple reference station interpolation algorithms, including partial derivation algorithm (PDA), linear interpolation algorithms (LIA) and least squares condition (LSC) were discussed and analyzed. The geometric dilution of precision (GDOP) was defined to describe the influence of the network geometry on the interpolation precision, and the different GDOP expressions of above-mentioned algorithms were deduced. In order to compare geometric precision characteristics among different multiple reference station network algorithms, a simulation was conducted, and the GDOP contours of these algorithms were enumerated. Finally, to confirm the validation of GPEM, an experiment was conducted using data from Unite State Continuously Operating Reference Stations (US-CORS), and the precision performances were calculated according to the real test data and GPEM, respectively. The results show that GPEM generates very accurate estimation of the performance compared to the real data test.

With the rapid development of V2X (Vehicle to Everything), high-accuracy positioning technology has drawn the attention of operators and manufactures. In consideration of automation levels, operated condition or other factors, different V2X application scenarios are required different position accuracy. In general, the quality of positioning has a significant impact on the accuracy of service triggering. The vehicle can adopt different positioning technologies according to its own application scenarios' requirements. As the essential component of C-V2X equipment, GNSS (Global Navigation Satellite System) and RTK (Real-time Kinematic) are most basic positioning technology. However, this technology is not suitable for whole conditions, such as dense urban areas, tunnels or underground parking. The location service providers always provide the highest value of position accuracy in open environment, but positioning quality of the special environments is unknown. Therefore, this paper proposes a test method to evaluate the performance of GNSS and RTK in actual environments. This method can evaluate multiple vehicle terminals' positioning quality from different location service providers simultaneously. Finally, according to the actual test results, this paper estimates whether key performance indicators of GNSS and RTK can meet the application requirements and analyses the influence of positioning for V2X applications scenarios.

Real-time kinematic (RTK) systems use GPS signals to deliver almost instantaneous positions with centimeter-level accuracy. RTK techniques are used for applications such as surveying, engineering, mining and automation. RTK technology has been commercially available since 1993. A critical factor in RTK system performance is the initialization time and reliability of estimating the carrier phase ambiguities. The time-to-initialize governs how long the user must operate with meter-level accuracy. A system that resolves ambiguities very quickly but incorrectly has serious ramifications. There is a trade-off between initialization time and reliability. Manufacturers seek to minimize initialization time while ensuring that reliability exceeds a tolerable threshold. A system has been developed to automatically evaluate RTK initialization performance. The system consists of a network of continuously logging GPS receivers, configured over a variety of baseline lengths and multipath/obstruction scenarios. Each week data is downloaded and processed over all baselines in a real-time fashion. The same engine that is used in RTK receivers is used to process the data. In some cases, multiple RTK engines are operated for comparison purposes. Statistical summaries of the results are automatically generated and electronically distributed for review. The RTK evaluation system aids algorithm refinement and helps specify system performance for users. Results from approximately 2.5 million trials produced an average initialization time of 46 seconds, and an initialization reliability of 99.9%.

This paper introduces data broadcasting technology and cm-level precision positioning technology, which are contributing to taking one step further into the era of autonomous driving. How ‘broadcasting’ and ‘positioning’ can be the best combination will be explained. Furthermore, the performance of RTK (Real-Time Kinematic) correction data for cm-level accuracy will be verified through ATSC 3.0 test-service in South Korea.

Global Navigation Satellite Systems (GNSS) provide various types of positioning state solutions, such as single point positioning (SPP), precise point positioning (PPP), differential GPS (DGPS) and real time kinematic (RTK) solutions. These solutions are obtained involving different data types, receivers, samples, serving different classes of users. Previous studies on performance characteristics have mainly focused on SPP solutions for safety-of-life navigation applications. This paper defines various useful performance characteristics for carrier phase Ambiguity Resolution (AR) and Position Estimation (PE) solutions in the RTK context. These parameters, including base-rover distance, time-to-first fix (TTFF), AR reliability, RTK accuracy, availability and integrity, etc, effectively represent the performance of a commercial RTK system and can be used to evaluate RTK systems and algorithms, and processing strategies through extensive experimental results. Statistical results from extensive field experiments were obtained using a commercial RTK system, demonstrating convincing overall system performance in different perspectives. Experimental results from three baselines were also analysed using a version of research-oriented RTK software, showing that AR performance improvement of using Wide-lane (WL) and Narrow-lane (NL) signals with respect to the original L1 and L2 signals when the baselines exceed 20 kilometres.

In challenging environments, there often exist problems of false alarms and missed detections in real-time kinematic (RTK) ambiguity resolution, which significantly reduce the reliability and availability of position information. To address these issues, a machine-learning method is proposed to conduct a correctness check on RTK ambiguity fixing, aiming to reduce the occurrences of false alarms and missed detections. The inter-system differential RTK model is adopted. Compared with the traditional RTK model, this model can provide an effective feature, namely the differential inter-system biases (DISB), to improve the accuracy of machine-learning classification. This is because when the RTK ambiguity is correctly fixed, the DISB usually appears as a stable constant. In addition to DISB, features that are strongly related to ambiguity fixing, such as the ratio value, DOP value, and residuals, are also comprehensively utilized. This method is verified by an open-source, large-scale, and diverse GNSS/SINS dataset-SmartPNT-POS. The experimental results show that, compared with the traditional method of relying solely on the empirical ratio value for ambiguity fixing verification, the missed detection probability of this method is reduced by 2%, the false-alarm probability is decreased by 29%, and the positioning accuracy is improved by approximately 7%. Moreover, compared with other features, the DISB feature provides the highest contribution rate in the machine-learning classification model.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

The performance of low-cost RTK (real-time kinematic) GPS receivers has been compared to a state-of-the-art system as well to each other. Both static and dynamic performances have been compared. The dynamic performance has been evaluated using a vehicle with driving robot on the AstaZero proving ground. The assembly of the low-cost RTK GPS receivers is presented, and the test set-ups described. Besides having a lower data output frequency, two of the low-cost receivers have static and dynamic performance not far from that of the state-of-the-art system.

The study of BDS RTK algorithm based on zero-combined observations and ionosphere constraints

Real‐time kinematic (RTK) GPS techniques have been extensively developed for applications including surveying, structural monitoring, and machine automation. Limitations of the existing RTK techniques that hinder their applications for geodynamics purposes are twofold: (1) the achievable RTK accuracy is on the level of a few centimeters and the uncertainty of vertical component is 1.5–2 times worse than those of horizontal components and (2) the RTK position uncertainty grows in proportional to the base‐to‐rover distances. The key limiting factor behind the problems is the significant effect of residual tropospheric errors on the positioning solutions, especially on the highly correlated height component. This paper develops the geometry‐specified troposphere decorrelation strategy to achieve the subcentimeter kinematic positioning accuracy in all three components. The key is to set up a relative zenith tropospheric delay (RZTD) parameter to absorb the residual tropospheric effects and to solve the established model as an ill‐posed problem using the regularization method. In order to compute a reasonable regularization parameter to obtain an optimal regularized solution, the covariance matrix of positional parameters estimated without the RZTD parameter, which is characterized by observation geometry, is used to replace the quadratic matrix of their “true” values. As a result, the regularization parameter is adaptively computed with variation of observation geometry. The experiment results show that new method can efficiently alleviate the model's ill condition and stabilize the solution from a single data epoch. Compared to the results from the conventional least squares method, the new method can improve the long‐range RTK solution precision from several centimeters to the subcentimeter in all components. More significantly, the precision of the height component is even higher. Several geosciences applications that require subcentimeter real‐time solutions can largely benefit from the proposed approach, such as monitoring of earthquakes and large dams in real‐time, high‐precision GPS leveling and refinement of the vertical datum. In addition, the high‐resolution RZTD solutions can contribute to effective recovery of tropospheric slant path delays in order to establish a 4‐D troposphere tomography.

This study has developed an improved real-time kinematic (RTK) algorithm by adding the double-differenced (DD) observations between L1 and L2 frequencies. It also introduces new biases between the frequencies to recover the integer of the DD ambiguities. The validations show that the new bias between the frequencies is stable and can be estimated as a constant in the same reference period. Compared with the standard RTK method, the new RTK model increased the observation number. The availability and reliability is thus greatly improved, especially in environmental conditions unsuitable for observation, and in which the observed satellite number is small.

China has recently established the primary constellation of BeiDou-3 global navigation satellite system (BDS-3). It is necessary to conduct a comprehensive assessment about its performance. The signal quality, ambiguity resolution efficiency and real-time kinematic (RTK) performance are assessed based on the datasets collected with two Trimble Alloy receivers that can track all open signals of BDS-3. Then, the precise point positioning (PPP) using combined BDS-2 and BDS-3 measurements is compared with the PPP using BDS-2 only, where the precise satellite orbits and clocks are determined using 116 globally distributed monitor stations. The results show that the signal quality of BDS-3 is generally better than that of BDS-2. Also, the ambiguity resolution efficiency of RTK is improved by incorporating the BDS-3 measurements with success rate improving from 88.5 to 91.4%. Regarding PPP, the convergence time is shortened from about an hour to less than half an hour, while the positioning accuracy is also improved significantly.

Pursuing Precise Vehicle Movement Trajectory in Urban Residential Area Using Multi-GNSS RTK Tracking