On Fault Detection and Exclusion in Snapshot and Recursive Positioning Algorithms for Maritime Applications

Resilient provision of Position, Navigation and Timing (PNT) data can be considered as a key element of the e-Navigation strategy developed by the International Maritime Organization (IMO). An indication of reliability has been identified as a high level user need with respect to PNT data to be supplied by electronic navigation means. The paper concentrates on the Fault Detection and Exclusion (FDE) component of the Integrity Monitoring (IM) for navigation systems based both on pure GNSS (Global Navigation Satellite Systems) as well as on hybrid GNSS/inertial measurements. Here a PNT-data processing Unit will be responsible for both the integration of data provided by all available on-board sensors as well as for the IM functionality. The IM mechanism can be seen as an instantaneous decision criterion for using or not using the system and, therefore, constitutes a key component within a process of provision of reliable navigational data in future navigation systems. The performance of the FDE functionality is demonstrated for a pure GNSS-based snapshot weighted iterative least-square (WLS) solution, a GNSS-based Extended Kalman Filter (EKF) as well as for a classical error-state tightly-coupled EKF for the hybrid GNSS/inertial system. Pure GNSS approaches are evaluated by combining true measurement data collected in port operation scenario with artificially induced measurement faults, while for the hybrid navigation system the measurement data in an open sea scenario with native GNSS measurement faults have been employed. The work confirms the general superiority of the recursive Bayesian scheme with FDE over the snapshot algorithms in terms of fault detection performance even for the case of GNSS-only navigation. Finally, the work demonstrates a clear improvement of the FDE schemes over non-FDE approaches when the FDE functionality is implemented within a hybrid integrated navigation system

Development of Precise Point Positioning Algorithm to Support Advanced Driver Assistant Functions for Inland Vessel Navigation

Bridge passing and passing waterway locks are two of the most challenging phases for inland vessel navigation. In order to be able to automate these critical phases very precise and reliable position, navigation and timing (PNT) information are required. Here, the application of code-based positioning using signals of Global Navigation Satellite Systems (GNSS) is not sufficient anymore and phase-based positioning needs to be applied. Due to the larger coverage area and the reduction of the amount of correction data Precise Point Positioning (PPP) has significant advantages compared to the established Real Time Kinematic (RTK) positioning. PPP is seen as the key enabler for highly automatic driving for both road and inland waterway transport. This paper gives an overview of the current status of the developments of the PPP algorithm, which should finally be applied in advanced driver assistant functions. For the final application State Space Representation (SSR) correction data from SAPOS (Satellitenpositionierungsdienst der deutschen Landesvermessung) will be used, which will be transmitted over VDES (VHF Data Exchange System), the next generation AIS

Entwicklung einer Low-Cost-PNT Unit für maritime Anwendungen, basierend auf MEMS-Inertialsensoren

Although the GPS/GNSS had become the primary source for Position, Navigation and Timing (PNT) information in maritime applications, the ultimate performance of the system can strongly degrade due to space weather events, deliberate interference and overall system failures. Within the presented work the development of an affordable integrated PNT unit for future on-board integrated system is presented. The system serves the task to collect and integrate the data from individual sensors in order to deliver the PNT information with a specified performance according to the requirements of the e-Navigation initiative proposed by the International Maritime Organization (IMO). The paper discusses an ongoing activity of replacing an expensive FOG inertial measurement unit with an affordable MEMS sensor system. Preliminary results of the system performance are presented for both static and dynamic scenarios using an Unscented Kalman filter with unit quaternions for the attitude parametrization

PPP-RTK with Rapid Convergence Based on SSR Corrections and Its Application in Transportation

Real-time Kinematic (RTK) positioning provides centimeter-level positioning accuracy within several seconds, but it has to rely on a nearby base station. Although Precise Point Positioning (PPP) supplies precise positions with one receiver, its convergence time takes several tens of minutes, which makes PPP not well suited for real-time kinematic applications where a rapid convergence is required. PPP-RTK integrates the benefits of PPP and RTK, which actually is PPP augmented by a ground GNSS network. The satellite orbit, clock offsets, signal biases, ionospheric and tropospheric corrections are determined based on this GNSS network, modeled as state space information and transmitted to PPP users. By applying these State Space Representation (SSR) corrections, a real-time kinematic PPP-RTK approach is developed and implemented, which can instantly resolve the ambiguities to integers and realize rapid convergence. In a static scenario, it realized an instant ambiguity resolution and a rapid convergence within 2 s in more than 90% of 120 hourly sessions. The PPP-RTK has been applied and evaluated in a kinematic scenario on the highway. The horizontal positioning errors are almost lower than 0.1 m except for the time of passing through bridges. After passing bridges, the PPP-RTK successfully resolved ambiguities within 2 s in 90.6% of the cases and achieved convergence in horizontal within 5 s in more than 90% of the cases. The PPP-RTK with a precision of 0.1 m and rapid convergence of several seconds benefits the precise navigation of automobile on the highway, which will support the development of autonomous driving in futur

From RTK to PPP‑RTK: towards real‑time kinematic precise point positioning to support autonomous driving of inland waterway vessels

PPP-RTK is Precise Point Positioning (PPP) using corrections from a ground reference network, which enables single receiver users with integer ambiguity resolution thereby improving its performance. However, most of the PPP-RTK studies are investigated and evaluated in a static situation or a post-processing mode because of the complexity of implementation in real-time practical applications. Moreover, although PPP-RTK achieves a faster convergence than PPP, it typically needs 30 s or even longer to derive high-accuracy results. We have implemented a real-time PPP-RTK approach based on undifferenced observations and State-Space Representation corrections with a fast convergence of less than 30 s to support autonomous driving of inland waterway vessels. The PPP-RTK performances and their feasibility to support autonomous driving have been evaluated and validated in a real-time inland waterway navigation. It proves the PPP-RTK approach can realize a precise positioning of less than 10 cm in horizontal with a rapid convergence. The convergence time is within 10 s after a normal bridge passing and less than 30 s after a complicated bridge passing. Moreover, the PPP-RTK approach can be extended to outside of the GNSS station network. Even if the location is 100 km away from the border of the GNSS station network, the PPP-RTK convergence time after a bridge passing is also normally less than 30 s. We have realized the first automated entry into a waterway lock for a vessel supported by PPP-RTK and taken the first step toward autonomous driving of inland vessels based on PPP-RTK

Precise Point Positioning to support an automatic entering of a waterway lock

Inland waterway transport is the transport mode with the lowest CO2 emission per tonne kilometre. However, there is a substantial potential for modal shift from road and rail to inland vessel transport. The increase of the grade of automation or even autonomous inland vessels could be a key enabler for this modal shift. By far the most challenging phase of inland navigation is the passing of waterway locks. Here, typically a ship with the dimension of 11.4 x 100 m has to enter a 12 m wide lock chamber, leaving just a few dm space on each side of the vessel. In order to support the automation of this manoeuvre, very accurate position, heading, turn rate and velocity information is required. Within the project SCIPPPER (2018-2022) such a driver assistant function has been developed. The idea was using the absolute Precise Point Positioning (PPP) instead of Real Time Kinematic (RTK) to achieve the required 10 cm horizontal accuracy. The reason was an expected reduction in the amount of PPP correction data and a significantly enlarged service area. Both facts would enable the correction data transmission over the VHF Data Exchange System (VDES) - the next generation of the Automatic Identification System (AIS). While a stable mobile internet connection is unfortunately not available at all inland waterways, currently AIS Base stations are being operated on all main inland waterways in Germany. By upgrading the AIS base stations to VDES stations, in the future all inland vessels on the main waterways could potentially benefit from the highly accurate positioning service. Besides the high accuracy, the reduction of the convergence time is one of the key challenges for the application of PPP for inland vessels. In order to shorten the PPP convergence time, we were using an SSR (state space representation) correction service from a regional network including not only global corrections like satellite clock, orbit, code and phase biases but also regional ionospheric and tropospheric corrections. These corrections were provided by using the GNSS station network of SAPOS (Satelliten Positionierungsdienst der deutschen Landesvermessung). In cooperation with the Working Committee of the Surveying and Mapping Agencies of the States of the Federal Republic of Germany (AdV) and Geo++, an SSR correction data stream has been prepared and optimised for the inland vessel application. By separating the corrections into high (5s update) and low rate (30s update) corrections, an average data rate of about 0.3 kbits/s was achieved, which is a significant reduction compared to RTK correction (4-5 kbit/s). In the paper the details of our real time PPP positioning solver with ambiguity resolution based on GPS and GALILEO observations will be given. Furthermore, the results of static as well as dynamic measurement campaigns in challenging inland waterway scenarios will be presented. In the final demonstration of the SCIPPPER project an 82 m long vessel automatically entered a waterway lock by using PPP as the main source for global positioning of the vessel. While the paper focuses on the positioning part, also a main overview of the complete driver assistant function will be presented

Real-Time Multi-GNSS Precise Point Positioning with Instant Convergence for Inland Waterway Navigation

Precise Point Positioning (PPP) has been highly recommended to be used in the future for precise navigation, and is very suitable for the inland waterway navigation. PPP has great advantages than RTK except for a relatively long convergence time of several minutes or even more than ten minutes without using atmospheric corrections. With the goal of achieving PPP accuracy at centimeters level in horizontal instantly, and supporting by the State Space Representation (SSR) corrections we developed a real time PPP algorithm by fully utilizing GPS and Galileo observations. A measurement campaign was conducted to validate the PPP performance for inland waterway navigation, especially the PPP convergence time and performance when passing a waterway lock or bridges. Finally, the PPP accuracy could be less than 10 cm in horizontal within several seconds or even at the first epoch when the GNSS satellites are evenly distributed in an open sky area. In addition, it can also achieve a fast reinitialization within several seconds after the vessel passing over a waterway lock or a bridge

Computational Aspects of Sensor Fusion for GNSS Outlier Mitigation in Navigation

As the Global Navigation Satellite Systems (GNSS) are intensively used as main source of Position, Navigation and Timing (PNT) information for maritime and inland water navigation, it becomes increasingly important to ensure the reliability of GNSS-based navigation solutions for challenging environments. Although an intensive work has been done in developing GNSS Receiver Autonomous Integrity Monitoring (RAIM) algorithms, a reliable procedure to mitigate multiple simultaneous outliers is still lacking. The presented work evaluates the performance of several methods for multiple outlier mitigation based on robust estimation framework and compares them to the performance of state-of-the-art methods. The relevant methods include M-estimation, S-estimation, Least Median of Squares LMS-based approaches as well as corresponding modifications for C/N0-based weighting schemes. The snapshot positioning methods are also tested within the quaternion-based Unscented Kalman filter for integrated inertial/GNSS solution. The proposed schemes are evaluated using real measurement data from challenging inland water scenarios with multiple bridges and a waterway lock. The initial results are encouraging and clearly indicate the potential of the discussed methods both for classical snapshot solutions as well for the methods with complementary sensors

On the recursive joint position and attitude determination in multi-antenna GNSS platforms

Global Navigation Satellite Systems’ (GNSS) carrier phase observations are fundamental in the provision of precise navigation for modern applications in intelligent transport systems. Differential precise positioning requires the use of a base station nearby the vehicle location, while attitude determination requires the vehicle to be equipped with a setup of multiple GNSS antennas. In the GNSS context, positioning and attitude determination have been traditionally tackled in a separate manner, thus losing valuable correlated information, and for the latter only in batch form. The main goal of this contribution is to shed some light on the recursive joint estimation of position and attitude in multi-antenna GNSS platforms. We propose a new formulation for the joint positioning and attitude (JPA) determination using quaternion rotations. A Bayesian recursive formulation for JPA is proposed, for which we derive a Kalman filter-like solution. To support the discussion and assess the performance of the new JPA, the proposed methodology is compared to standard approaches with actual data collected from a dynamic scenario under the influence of severe multipath effects

Attitude Determination via GNSS Carrier Phase and Inertial Aiding

Attitude Determination (AD) constitutes an important navigation component for vehicles that require orientation information, such as spacecraft or ships. Global Navigation Satellite Systems (GNSS) enable resolving the orientation of a vehicle in a precise and absolute manner, by employing a setup of multiple GNSS antennas rigidly mounted onboard the tracked vehicle. To achieve high-precision attitude estimation based on GNSS, the use of carrier phase observations becomes indispensable, with the consequent added complexity of resolving the integer ambiguities. The use of inertial aiding has been extensively exploited for AD, since it enables tracking fast rotation variations and bridging short periods of GNSS outage. In this work, the fusion of inertial and GNSS information is exploited within the recursive Bayesian estimation framework, applying an Error State Kalman Filter (ESKF). Unlike common Kalman Filters, ESKF tracks the error or variations in the state estimate, posing meaningful advantages for AD. On the one hand, ESKF represents attitude using a minimal state representation, in form of rotation vector, avoiding attitude constraints and singularity risks on the covariance matrix estimates. On the other hand, second-order products on the derivation of the Jacobian matrices can be neglected, since the error-state operates always close to zero. This work details the procedure of recursively estimating the attitude based on the fusion of GNSS and inertial sensing. The GNSS attitude model is parametrized in terms of quaternion rotation, and the overall three-steps AD procedure (float estimation, ambiguity resolution and solution fixing) is presented. The method performance is assessed on a Monte Carlo simulation, where different noise levels, number of satellites and baseline lengths are tested. The results show that the inertial aiding, along with a constrained attitude model for the float estimation, significantly improve the performance of attitude determination compared to classical unaided baseline tracking