Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit—IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.

Advanced driver-assistance system (ADAS) and high levels of autonomy for vehicular applications require reliable and high precision pose information for their functioning. Pose estimation comprises solving the localization and orientation problems for a rigid body in a three-dimensional space. In outdoor scenarios, the fusion of Global Navigation Satellite Systems (GNSS) and inertial data in high-end receivers constitutes the baseline for ground truth localization solutions, such as Real-Time Kinematic (RTK) or Precise Point Positioning (PPP). These techniques present two main disadvantages, namely the inability to provide absolute orientation information and the lack of observations redundancy in urban scenarios. This paper presents Array PPP-RTK, a recursive three-dimensional pose estimation technique which fuses inertial and multi-antenna GNSS measurements to provide centimeters and sub-degree precision for positioning and attitude estimates, respectively. The core filter is based on adapting the well-known Extended Kalman Filter (EKF), such that it deals with parameters belonging to the SO(3) and GNSS integer ambiguity groups. The Array PPP-RTK observational model is also derived, based on the combination of carrier phase measurements over multiple antennas along with State Space Representation (SSR) GNSS corrections. The performance assessment is based on the real data collected on an inland waterway scenario. The results demonstrate that a high precision solution is available 99.5% of the time, with a horizontal precision of around 6 cm and heading precision of 0.9 degrees. Despite the satellite occlusion after bridge passing, it is shown that Array PPP-RTK recovers high accurate estimates in less than ten seconds.

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 future.