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The use of carrier phase data is the main driver for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). However, carrier phase observations are ambiguous by an unknown number of cycles, and their use in RTK relies on the process of mapping real-valued ambiguities to integer ones, so-called Integer Ambiguity Resolution (IAR). The main goal of IAR is to enhance the position solution by virtue of its correlation with the estimated integer ambiguities. With the deployment of new GNSS constellations and frequencies, a large number of observations is available. While this is generally positive, positioning in medium and long baselines is challenging due to the atmospheric residuals. In this context, the process of solving the complete set of ambiguities, so-called Full Ambiguity Resolution (FAR), is limiting and may lead to a decreased availability of precise positioning. Alternatively, Partial Ambiguity Resolution (PAR) relaxes the condition of estimating the complete vector of ambiguities and, instead, finds a subset of them to maximize the availability. This article reviews the state-of-the-art PAR schemes, addresses the analytical performance of a PAR estimator following a generalization of the Cramér–Rao Bound (CRB) for the RTK problem, and introduces Precision-Driven PAR (PD-PAR). The latter constitutes a new PAR scheme which employs the formal precision of the (potentially fixed) positioning solution as selection criteria for the subset of ambiguities to fix. Numerical simulations are used to showcase the performance of conventional FAR and FAR approaches, and the proposed PD-PAR against the generalized CRB associated with PAR problems. Real-data experimental analysis for a medium baseline complements the synthetic scenario. The results demonstrate that (i) the generalization for the RTK CRB constitutes a valid lower bound to assess the asymptotic behavior of PAR estimators, and (ii) the proposed PD-PAR technique outperforms existing FAR and PAR solutions as a non-recursive estimator for medium and long baselines.

International audience; 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.

Global Navigation Satellite Systems (GNSS) plays a fundamental part on the maritime navigation. Beyond positioning, GNSS is key for the operation of multiple interfaces on the bridge of a ship, compromising the skipper skills to perform traditional navigation. Jamming attacks have been recognized as a major vulnerability for GNSS and their proliferation have raised concerns, given the implication of GNSS into several safety-critical applications. This work provides an overview on the jamming threat and the main countermeasures techniques, especially in the fields of robust signal processing, adaptive antenna arrays and multi sensor fusion. Moreover, the effects of a Personal Privacy Device (PPD) on positioning based on conventional methods using GPS L1 is addressed. The experimentation is conducted on the Baltic Sea, where a civilian maritime jamming testbed was allocated, as result of the cooperation of DLR with the German Federal Network Agency.

Global Navigation Satellite Systems (GNSS) are increasingly used as the main source for Positioning, Navigation and Timing (PNT) information for maritime and inland water applications. With the rising availability of cheap jamming devices such as Personal Privacy Devices (PPD), there is real threat to GNSS based navigation. Therefore, it becomes of upmost importance to estimate the accuracy of the satellite signals, even under disturbance. The presented work examines the influence of jamming on the noise of the signals and if it can be modeled. Laboratory experiments showed that it is possible to accurately estimate the noise in jamming conditions which is even consistent with undisturbed measurements. Hence, the derived model describes the noise of GNSS signals in scenarios with and without jamming. It uses the elevation angles of the satellites and the Signal-toNoise Ratio (SNR) of its signals to estimate the respective noise. The parameters of the model are calculated by fitting them to real measurement data of scenarios with different amount of jamming. The initial results show that this model estimates the noise of the signals better than classic weighting schemes using only the elevation angles or just the SNR. This shows the potential of the model to be used in multisensor fusion scheme as a weighting scheme in an iterative least squares position solver or as a variance estimator in a Kalman filter in demanding environments.

Inland shipping is an important pillar of the European transport system, however due to dense traffic, reduced visibility and increasing ship dimensions it is very challenging. About 20 to 30 collisions per year, mainly caused by carelessness, lead to damages on waterway infrastructures like bridges, damages on the vessel or even injured people. This fact demonstrates the necessity of driving assistance functions for inland vessels. This contribution will give an overview about the concluded project LAESSI which includes the require-ments for accuracy, integrity and time to alert for the different assistance functions as well as the de-scription of the complete overall system setup like shore-based and board equipment. A main topic in this project was the first realization of the new communication concept based on VHF Data Exchange System (VDES) the next generation of the AIS communication which will also present here. A challenge in this communication concept was the limited data capacity for transmitting all necessary phase and code corrections for every observation in every epoch.

Inland shipping is an important pillar of the European transport system; however the challenges of inland navigation due to dense traffic, increasing ship dimension, reduced maneuver space and visibility are multifaceted and increasing. About 20 to 30 collisions per year, partially caused by carelessness, demonstrate the necessity of driving assistance systems for inland waterway applications. The project LAESSI (Guiding and assistance systems to improve safety of navigation on inland waterways) is developing efficient navigation assistance functions for inland waterway transport which aim the reduction of risk of collisions by supporting the skipper in its task. Therefore, nautical information like position, height, velocity and heading has to be determined. One focus in this contribution is a bridge collision warning system, which provides a timely alert to the skipper, whenever the vessel, particularly the wheelhouse or radar mast, will not safely pass the bridge. A feasibility study has identified Global Navigation Satellite System (GNSS) technologies as basis for the reliable height determination for such a bridge collision warning system. This approach requires information about the vertical clearance of the bridge superstructure as well as precise height information in the same height reference system at least 300 m before the vessel will pass the bridge. The high accuracy level of less than 10 cm in the vertical position necessitates the Real Time Kinematic (RTK) as suitable GNSS processing method. RTK is a phase-based differential GNSS technique and uses additional observations from permanent reference stations to mitigate or eliminate effects like tropospheric and ionospheric delays or satellite clocks and orbit errors. As bridge collision warning system is a safely critical application, this means that beside the provision of accurate height information also an integrity monitoring for the complete RTK procedure has to be introduced. The shore-based architecture is designed in such a way that correction data, based on a permanent GNSS reference station network, will be checked for their integrity before they are broadcasted (Pre-Broadcast Monitoring) to the processing computer on the vessel. Furthermore, several station dependent effects like signal interferences, signal losses or multipath effects, caused due to obstacles near the inland waterways, impact the GNSS signals, which hamper or falsify the ambiguity fixing and finally the position results. To prevent the provision of faulty results a further integrity monitoring for the shipborne architecture has to be applied. This shipborne integrity monitoring utilizes on the one hand a number of internal statistical parameters from the RTK algorithm like the conventional used fixed ratio value and on the other hand additional information from other sensors or observations. The contribution will present the derived requirements for accuracy, integrity and time to alert for different inland waterway assistance functions. Furthermore an overview about the system architecture and a communication concept based on VHF Data Exchange System (VDES) communication channels will be given. The main focus, however, is an RTK-algorithm which not only estimates position, velocity and heading, but also provides integrity information. This concept considers in a first realization internal statistical parameters from the RTK adjustment, other observations (e.g. Doppler shift) and physical information (e.g. baseline length of two GNSS antennas). A first measurement campaign was done in July 2016 in Koblenz/Germany on the river Moselle. This test area is characterized by three bridges in a relatively short distance of only 300 m which make the provision of reliable and precise RTK position data rather challenging. The preliminary analyses based on three hours GPS and GLONASS observations showed a high rate (> 85%) of evaluated position and height results, which fulfilled the required cm-level accuracies. The first implementation of the internal integrity check could detect and eliminate faulty results. The results have shown that beside a good height performance also fast provision of the heights after the vessel sailed through the bridges is recognizable. This position data between the bridges allows a provision of information to the skipper to check or adjust the wheelhouse or radar mast height before the next bridge will be passed. In the next implementation the rate of turn and acceleration data from an Inertial Measurement Unit (IMU) will be tightly coupled with the raw GNSS observations. Thereby additional information will be available to evaluate the position, height, velocity and heading results and increase the integrity validation. Furthermore it can be expected that the ambiguities after a complete signal interruption can be fixed faster.

Over the last decade, context has become a key source of information for tracking problems. Context inference allows refining sensor modeling and target dynamics as well as the creation of motion constraints according to the physical and operational conditions of the scenario. This work presents two example applications: indoor and inland waterway navigation where the context information is employed to reduce the uncertainty of the tracking and enhance the navigation solution. For indoor positioning a cascaded Extended Kalman Filter (EKF) and Particle Filter (PF) architecture is proposed. The system uses stance phase detection and the available floor map to construct the measurement models for the KF and motion constraints for the PF respectively. For navigating in inland waterways it is shown how to benefit from context information fusion by inferring the operating condition of the Global Navigation Satellite System (GNSS). In this scenario, context based criteria are derived for the selection of the best position estimation amongst several positioning solvers running in parallel. This work presents the basics for context fusion in tracking applications, illustrating the theory with two application examples. The preliminary results already demonstrate a performance improvement compared to state-of-the-art approaches.

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.

As the Global Navigation Satellite Systems (GNSS) are increasingly used as the main source of Positioning, Navigation and Timing (PNT) information for maritime and inland water applications, it becomes of crucial importance to ensure the reliability and the accuracy of the GNSS-based navigation solution for certain challenging environments. The presented work extends the previously reported GPS L1C positioning algorithms using robust estimation framework for the scenarios with multiple separate GNSS antennas. The performance of a robust method suitable for multiple outlier mitigation is compared to one of the robust methods designed for a single antenna. The presented schemes are evaluated using real measurement data from inland water scenario with multiple bridges and a waterway lock. The initial results are encouraging and indicate the scalability of the previously suggested robust GPS positioning schemes for setups with multiple GNSS antennas as well as the potential of the methods to be used in integrated navigation systems.

Although the GNSS/GPS had become the primary source for Positioning, Navigation and Timing (PNT) information in maritime applications, the ultimate performance of the system can strongly degrade due to space weather events, deliberate interference, shadowing, multipath and overall system failures. Within the presented work the development of an affordable integrated PNT unit for future on-board integrated systems is presented, where the GNSS information is fused both with inertial and Doppler Velocity Log (DVL) measurements. Here redundant and complementary information from different sensors serves to improve the system performance and reduce the position drift when the GNSS signals are not available. The nonlinearity of this advanced fusion problem is addressed by employing Unscented Kalman Filter (UKF) with spherical point arrangement and further detailed analysis is presented in terms of the process and measurement models implemented. The results demonstrate that position drift can be significantly reduced by incorporating DVL measurements in IMU/GNSS system and that the proposed integrated navigation algorithm is feasible and efficient for GNSS outages of prolonged duration, where pure inertial GNSS outage bridging would be either inaccurate or would require too expensive IMUs.

Resilient provision of Position Navigation and Time (PNT) data is a strategic key element of the e-Navigation strategy, developed by the International Maritime Organization (IMO). The improvement and the indication of reliability have been identified as high level user need with respect to PNT data supplied by electronics means. IMO as developed a maritime PNT system concept aiming to improve the resilience and reliability of PNT data provision during berth-to-berth navigation. The maritime PNT System comprises several structural components, where Global Navigation Satellite Systems (GNSS), have become the primary component to produce position, velocity and time information for maritime applications. For a comprehensive onboard provision of PNT data as well as to compensate the vulnerability of GNSS, further onboard sensors are needed. The PNT system is responsible for the fusion of the data provided by all the available onboard sensors and data integrity monitoring functions. A unit composed by several sensors of different classes improves the resilience of the system. DLR has developed a prototype of an onboard PNT unit and several measurement campaigns have been performed. This paper concentrates on integrity monitoring (IM) for navigation systems based on sensor fusion. IM is a mechanism that protects the user from large position and velocity errors in the presence of failures or non-scheduled events in a timely fashion. It can be seen as an instantaneous decision criterion for using or not the system and therefore constitutes a key function for the safety of navigation. The IM includes the detection and exclusion functions, they are responsible for detecting the measurements errors (faults) and exclude them from the PNT data computation algorithm. This work presents a systematic analysis of Fault Detection and Exclusion (FDE) algorithms in representative single and multi-sensor. More specifically, a pure GNSS-based snapshot weighted iterative least-square (WLS) solution is compared to a classical error-state Extended Kalman Filter (EKF) for a combined GNSS/IMU system with Euler angles for attitude parameterization. The outlier detection functionality is implemented for both pseudorange and Doppler shift observations in order to ensure the integrity of the estimated position and velocity data. The work confirms the superiority of the recursive Bayesian scheme over a snapshot algorithm in terms of the outlier detection performance. This can be explained by the recursive structure of the estimator, where the dynamical model of the system provides the additional source of information, which increases the system’s redundancy and hence improves the performance of the FDE schemes.