Search

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.

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.

With respect to "GNSS in marine appications" the presentation was used to discuss some fundamental aspects concerning the new proposed resilient PNT unit as well as the role of integrity within the maritime community.