Comparison of Clock Models in View of Clock Composition, Clock Steering and Measurement Fitting

Trainotti, Christian; Schmidt, Tobias D.; Furthner, Johann

doi:10.33012/2019.16756

Cited by 7 publications

(12 citation statements)

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“…This way, the output of the steered clock provides a physical realization of the IEM. This CCA implementation is based on more than ten years of both theoretical and applied experience gathered at the Institute of Communications and Navigation at the German Aerospace Center (DLR) [3].…”

Section: Realization Of the Robust And Precise Timing Facilitymentioning

confidence: 99%

3.3 A novel Robust and Precise Timing Facility for Galileo

Kindl¹,

Agazzi²,

Schilling³

et al. 2022

Proceedings - Ettc2022

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A fundamental component in each Global Navigation Satellite System is the timing facility, which is responsible for the synchronization of all elements within the ground segment as well as for the satellites in space. Different concepts exist to provide a time scale that fulfils all requirements, e.g. master clock principle, weighted clocks or composite clock algorithm. In the European Global Navigation Satellite System Galileo, the so-called Precise Timing Facility (PTF), located in Oberpfaffenhofen, Germany, and Fucino, Italy, has the task to provide the Galileo System Time (GST). The actual published design of the PTF depends on a master clock principle and is therefore sensitive to failures of individual units within the GST generation [1].At DLR Galileo Competence Center we propose an alternative design for such a timing facility, which is called Robust Precise Timing Facility (RPTF). The focus of the concept is on mitigating technical vulnerabilities and increasing the tolerance to the failure of any of the components of the existing PTF.In this paper we present the concept, current status and future plans for such a RPTF to generate robust system timescales for the next generation of the European satellite navigation systems. The aim is to build, test and characterize the RPTF under the aspect of a 24/7 operational service. The key element is the combination of all the individual atomic clocks in the facility via the composite clock approach to generate a weighted average, the so-called Implicit Ensemble Mean (IEM), and to provide redundancy and test opportunities in case of hardware and software failures.

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Section: Realization Of the Robust And Precise Timing Facilitymentioning

confidence: 99%

3.3 A novel Robust and Precise Timing Facility for Galileo

Kindl¹,

Agazzi²,

Schilling³

et al. 2022

Proceedings - Ettc2022

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“…Figure 3 shows exemplary OADEVs for different oscillators in order of increasing stability: TCXO, microelectromechanical systems (MEMS), oven-controlled crystal oscillator (OCXO), and Rubidium. The TCXO has been characterized with our swarm radio system, and the OCXO and Rubidium clock have been characterized in [6]. OADEV curves show three distinct regions over τ which are of interest for us: A decreasing OADEV for small averaging intervals to a minimum or flat region, and an increasing OADEV for larger τ.…”

Section: The Role Of Clocksmentioning

confidence: 99%

“…From this section we can conclude that the stability of the used clock is key for the design of a wireless communication system, and we refer the interested reader to [6] on the state-space representation and simulation of clocks.…”

Section: The Role Of Clocksmentioning

confidence: 99%

“…Each agent tracks the dynamics of its clock and the clocks of its neighboring agents with Kalman filters (KFs) to compensate fractional frequency offsets. The KF employs the transition model parameters of the clocks [6], like the TCXOs embedded in SDRs, obtained from RF calibration. One SDR is connected to an external Rubidium clock as a stable frequency reference.…”

Section: Paving the Way To A Real-world Swarm Mission A Innovatimentioning

confidence: 99%

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The Role of Time in a Robotic Swarm: A Joint View on Communications, Localization, and Sensing

et al. 2021

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Autonomous robotic swarms are envisioned for a variety of sensing applications in space exploration, search-and-rescue and disaster management. An important capability of a swarm is sensing spatio-temporal processes such as radio wave propagation or seismic activities. The spatio-temporal properties of these processes dictate the required sensing position and time accuracy, as well as update rate. A dedicated wireless communication system needs to be jointly designed for swarm information exchange, self-localization and sensing. In this article, we emphasize the role of time in a robotic swarm. We introduce the system ingredients and dive into realistic clock models. Clock models and channel access schemes decisively influence the swarm self-localization and synchronization accuracy, and consequently the swarm sensing performance. Finally, we discuss practical implementation aspects, introduce our developed swarm radio system, and give an outlook on a moon-analogue exploration mission. Siwei Zhang received the B.Sc. in electrical engineering from the Zhejiang University, China, in 2009, the M.Sc. in communication engineering from the Technical University of Munich, Germany, in 2011, and the Dr.-Ing. (Ph.D.) in electrical engineering from the University of Kiel, Germany, in 2020. In 2012, he joined the Institute of Communications and Navigation, German Aerospace Center (DLR), Germany, as a Research Staff Member. His research interests include radio navigation, cooperative positioning and swarm navigation. Robert Pöhlmann received B.Sc. and M.Sc. in electrical engineering and information technology from the Technical University of Munich (TUM), Germany, in 2014 and 2016, respectively. In 2013, he joined the Institute of Communications and Navigation, German Aerospace Center (DLR), Germany, as a Student Trainee, where he became a Research Staff Member in 2016. His current research interests are in the area of statistical signal processing for multi-antenna systems and cooperative localization. Armin Dammann received the Dipl.Ing. (M.Sc.) and Dr.Ing. (Ph.D.

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“…Time plays an important role in swarm navigation as discussed in [8]. The imperfection of clocks [9] has to be considered particularly when propagation time based ranging schemes like time of flight (ToF) are applied. In [10], simultaneous localization and synchronization has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Pose Estimation in a Robotic Swarm: Framework, Simulation and Experimental Results

Zhang

Cokona

Pöhlmann

et al. 2022

2022 30th European Signal Processing Conference (EUSIPCO)

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Swarm robotics has gained an increasing attention in applications like extraterrestrial exploration and disaster management, due to the ability of simultaneously observing at different locations and avoiding a single point of failure. In order to operate autonomously, robots in a swarm need to know their precise poses, including their positions, velocities and orientations. When external navigation infrastructures like the global navigation satellite systems (GNSS) are not ubiquitously accessible, the swarm of robots need to rely on internal measurements to estimate their poses. In this paper, we propose a cooperative 3D pose estimation framework, based on the insights of sensor characteristics that we gained from outdoor swarm navigation experiments. A decentralized particle filter (DPF) operates on each robot to estimate its pose via fusing radiobased ranging, inertial sensor data, control commands and the pose estimates of its neighbors. This framework is integrated in the swarm navigation ecosystem developed at the German Aerospace Center (DLR), and is unified for both simulations and experiments.

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Comparison of Clock Models in View of Clock Composition, Clock Steering and Measurement Fitting

Cited by 7 publications

References 0 publications

3.3 A novel Robust and Precise Timing Facility for Galileo

3.3 A novel Robust and Precise Timing Facility for Galileo

The Role of Time in a Robotic Swarm: A Joint View on Communications, Localization, and Sensing

Cooperative Pose Estimation in a Robotic Swarm: Framework, Simulation and Experimental Results

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