R-Mode is a terrestrial navigation system under development for the maritime domain that provides backup in case of a GNSS outage. This paper describes the first test results for real-time positioning on board a ship using medium frequency R-Mode signals. The estimation and positioning algorithms used are described in detail and it is shown how they are integrated into the R-Mode receiver developed by the German Aerospace Center. Moreover, during two daytime experiments with lower and higher dynamic movements of a ship in the Baltic Sea, we were able to achieve a 95% horizontal positioning accuracy of better then 12 m in the center of three R-Mode transmitters. This demonstrates the first time that the medium frequency R-Mode has provided positioning at sea.

Global Navigation Satellite Systems (GNSS) are the primary source of position, navigation and timing (PNT) information in the maritime domain. Nevertheless, there is a pressing need for alternative absolute position information to serve as a backup when GNSS is not trustworthy or usable. One possible alternative navigation system is the terrestrial Ranging Mode, also known as the R-Mode. It reuses medium frequency (MF) radio beacons and base stations of the very high frequency data exchange system (VDES) for the transmission of synchronised signals in the service areas of those stations. A large-scale R-Mode testbed is available in the southern Baltic Sea with eight MF radio beacons transmitting R-Mode signals. These signals suffer the self-interference generated by the reflection of the radio wave from the ionosphere at night. The reflected signal, known as the sky wave in the literature, is a form of multipath, which decreases the accuracy of the system. In our work, we present the analysis of measurements obtained with the research vessel Deneb. The horizontal accuracy achieved in the optimal condition was 15.1 m (95%), whereas under the sky wave, the accuracy decreased to 55.3 m. The results are a starting point for the further development of the MF R-Mode system.

Time delays caused by ground wave propagation are the primary source of systematic error limiting the performance of the medium-frequency R-Mode radionavigation system. To achieve the desired ranging accuracy and compensate these delays, we have conceived a comprehensive correction scheme based on the prediction and application of the Atmospheric and Ground wave Delay Factor (AGDF). The AGDF was computed and mapped in 2D for a number of MF R-Mode transmitters in the Baltic Sea that were embedded into the receiver and evaluated during a large-scale measurement campaign. Our results show that the proposed AGDF approach is valid for the MF R-Mode system and provides accurate corrections of ground wave propagation delays within the performance requirements.

Position, navigation, and timing information are critical to today’s infrastructures; as a result, the possibility of estimating ranges is being explored in more and more radio systems. One way to achieve this is to extend the modulation with time-synchronised aiding carriers and to estimate their phase at the receiver side. In this paper, we present two ways to minimise the negative influence of the modulation on the phase estimation. We show that the classical maximum likelihood estimator is still an efficient estimator for our problem, using a medium-frequency R-Mode signal as an example, and is therefore used in receiver designs. We then describe two possible ways to precondition the signal to increase the accuracy for short observations. As a first approach, we describe how window functions can positively change the signal-to-noise ratio for our estimation. As a second approach, we show the use of a narrowband bandpass filter. Finally, we show that these approaches, applied to real measurements, improve the variance of the estimate by up to two orders of magnitude.

Global Navigation Satellite Systems (GNSS) constitute the cornerstone for outdoor positioning, which is essential information for prospective automated vehicles. The combination of GNSS with terrestrial ranging, for instance in the form of 5G or UWB, will make accurate positioning a reality even in urban canyon scenarios where GNSS is likely to fail. Thus, hybrid GNSS-terrestrial localization in collaborative networks has become a hotspot for the research community. This paper discusses the Cramér-Rao Bound (CRB) as lower bound for location estimates and evaluates two snapshot estimators, one deterministic and the other Bayesian, for distributed and centralized localization in cooperative networks. The performance of the estimators is evaluated with respect to the presented CRB in a simulated network of mobile and anchor agents, and the role played by agent-to-agent and agent-to-anchor ranging is discussed.

Nowadays, the maritime sector strongly relies on global navigation satellite systems (GNSS) for the provision of position, navigation, and timing (PNT) information. The standard functionality of several ships’ bridge instruments depends on such information, which becomes critical to have a safe and reliable navigation. Nevertheless, the possibility of GNSS outages combined with unintentional and intentional interference to GNSS signals, which have been increasing over the last years, can severely threaten the nominal activity of the crew onboard of vessels. For these reasons, alternative position, navigation, and timing (APNT) systems become fundamental in order to provide operational continuity. R(anging)-Mode is a terrestrial alternative system to GNSS for the maritime domain. In one of its possible implementation, it exploits synchronized medium frequency (MF) signals transmitted by maritime radio beacons. In the MF band, the radio waves are attenuated and distorted by the change of electrical properties of the ground along the propagation path. Moreover, these signals are affected by terrain elevation changes and large metallic infrastructures which induce an overall loss in the accuracy of the system. In this paper, we propose a mitigation technique based on the estimation of these additional error sources by using in-field range measurements. The theoretical details are explained in depth and the technique is validated with real data gathered on a measurement campaign. A clear and significant performance improvement is achieved on the range accuracy when the proposed approach is applied.