United Kingdom

A method is presented to synchronize and aid the absolute navigation solutions of the members of a swarm by means of relative inter-swarm measurements. Moreover, the operational benefits thereof are discussed.

Collaborative operation of vehicles, especially of unmanned aerial vehicles in close formation, requires accurate relative and absolute navigation. A novel method for collaborative multi-sensors fusion of relative and absolute navigation information is presented designed to cope with GNSS-denied environment.

Relative inter-swarm measurements and absolute navigation means cannot be fused straight away as they are defined in fundamental different frames. Moreover, the sensor fusion has to fulfill two objectives – firstly, restoring the relative geometry of the swarm, and secondly, determining the absolute positions of each vehicle in an optimal way for the overall swarm.

Key for collaborative navigation is to establish a common knowledge within the swarm which is achieved by exchanging the absolute state estimations, the state uncertainties and the relative measurements. Based on the common knowledge the “Consistent INS Drift Algorithm” computes for each vehicle a new position which is statistically optimal for the overall swarm. Simultaneously, these new positions represent the actual geometry of the swarm by essentially resampling the relative measurements. By fusing these new positions the navigation filters are synchronized and the relative measurements are implicitly used to aid the absolute navigation solutions.

This reveals several operational benefits: the absolute navigation solutions are consistent – the whole swarm drifts consistently as only the Inertial Navigation Systems (INS) may remain as absolute navigation means, and hence, the naming “Consistent INS Drift Algorithm”. Moreover, the drift is statistically slower than the best individual INS. Having at least one vehicle equipped with a drift-free navigation mean such as Terrain Referenced Navigation stops the swarm to drift. Therefore, heterogeneous navigation systems are natively supported enabling true collaboration.

Navigation 2021

Collaborative Navigation - Synchronizing and Aiding the Absolute Navigation of Swarm Members using Relative Inter-swarm Navigation Information

### Welcome to Navigation 2021!

Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

Navigation and timing are critical to the world we all inhabit. We believe that the work of the wider navigation community enables societal benefit including support to national grand challenges and also the United Nations’ Sustainable Development Goals. To achieve this requires collaboration and understanding across science, technology and practice.

Navigation 2021 is about much more than technology innovation, it also showcases science and research from across the wider community, including animal navigation, cognition and human factors.


Our aim was to bring diverse disciplines together to stimulate insights and knowledge sharing towards a more navigable world. We hope we achieved this!

technical paper

Session 1.2.

Position, Navigation and Timing (PNT) is integral to the safety, security and prosperity of the United Kingdom, its Overseas Territories, and wider global interests. PNT is also critical to the UK’s national security supporting command, control, and communications capabilities, and to the cyber enterprise. The loss of PNT services from modalities such as Global Navigation Satellite Systems (GNSS) (like the US Global Positioning System (GPS)) will have a significant impact on our economic wellbeing. This paper describes the development of a framework for the coordination of PNT across the UK Government and for the wider UK. It proposes both a strategic and integrated system-of-system architecture that addresses how to bring the complex area of policy, strategy, skills, legislation, delivery mechanisms and multiple technologies, including research and development, together in a coherent way, integrating existing programmes, as well as proposes how to determine appropriate technology approaches looking to the future. It is the core outcome of an evidence based comprehensive review and PNT Strategy development in the UK with the goal to ensure PNT users have access to resilient and assured PNT services when they need them.

Developing a Strategic Architecture Framework for PNT in the United Kingdon

One of the most efficient way to support the current care system, especially in the areas with high rates of aging population as in Europe, is to develop mass-market tools able to monitor movements and activities of older adults in order to detect possible danger situations and send an alert. In this field, PROLONG (Portable Receiver to improve Older adults’ safety through gait anaLysis using Opportunistic Networking and very high accuracy Galileo-based location) [GSA/GRANT/08/2017/PROLONG] initiative targets the development of GNSS-IoT trackers and algorithms to be used as prevention tools in outdoor environment.



The availability of very precise PNT (Position-Navigation-Timing) solutions is the starting point for performing gait analysis in order to detect possible danger, i.e. unstable walking pattern. Despite the traditional RTK, PPP or PPP-RTK algorithms are able to provide accuracy in the positioning domain from centimeters up to few decimeters depending on the environmental conditions, the complexity in handling the phase ambiguity still represents a limiting factor when instantaneous reliable estimates are required. Therefore, in order to increase the accuracy in the final PNT solution without relying on any ambiguity resolution strategies, the Pos-Var algorithm, developed within Sapienza University of Rome, is foreseen for PROLONG devices. The Pos-Var algorithm, based on the coupling between pseudorange-based positioning and phase variometric velocities, allows accuracy in the positioning domain of different decimeters and ensures few millimeters accuracy in the estimation of velocity and derived displacements, fundamental in the detection of unstable walking patterns. 

The aim of this work is to demonstrate the accuracy achievable using the Pos-Var algorithm and both high-end and low-cost GNSS equipment, compatible with the future PROLONG device, in kinematic scenarios with simulated locomotion patterns. The accuracy is evaluated against a reference solution estimated using a double difference post-processed solution on a very short baseline.

PNT for Health: First Results of the Development of a New Algorithm for PROLONG project

PNT services are indispensable for uncountable professional, governmental and private applications. Without highly precise and highly available location and navigation data and services, autonomous driving, fleet management, efficient supply chain logistics would be inconceivable. More and more smartphone apps are driven by location data. 



However, positioning, location and navigation data may qualify as personal data and can therefore only be processed in compliance with data protection law. The opportunities and risks of processing location based data were already highlighted by the so-called Article 29 Data Protection Working Party before the GDPR entered into force and were subject of a much-noticed opinion. The potentials for misuse cannot be denied, e.g. knowledge of movement profiles can help burglars plan a break-in, or kidnappers can ambush people on a route they know. Data from company cell phones and company cars can give employers an almost seamless movement profile of employees. Furthermore, manipulation of corresponding data can have significant consequences for individuals if, for example, they are mistakenly located near a crime scene and therefore become suspect.



The above problems are known and can be solved, but are often ignored. The presentation will explain to what extent the GDPR and data protection law in general apply to the provision of PNT services and what is required for compliant services and use. The focus will be on practical legal solutions.

Autonomous driving, location based services and data protection

The Earth’s magnetic field is ubiquitous and is still widely used with relatively low-cost sensors as a reference direction. One particular area of application is underground, in directional drilling. As hydrocarbon fields become increasingly “busy” with historic wells, and the reservoir targets become smaller and smaller, the magnetic field estimates used to determine well positions need to be accurate and with well-defined uncertainties. In this poster we describe the magnetic referencing services the British Geological Survey provide in order to promote safe and efficient directional drilling, and the latest developments in defining the associated uncertainties.

Underground navigation – geomagnetic field referencing for directional drilling

Traditionally, GPS/GNSS Simulators have required custom hardware to operate. With recent advances in Signal Processing capacity, driven by PC gaming Graphics Processors (GPU), and the availability of high capability off the shelf Software Defined Radios (SDR) for RF signal generation. It is now possible to rethink Simulators with an Open Software Defined Architecture.

Advanced GNSS Simulation with Software Defined Architecture

Galileo is currently testing its Open Service Navigation Message Authentication (OSNMA). OSNMA is based on authenticating the navigation data with cryptographic features, assuring the origin of the data. However, OSNMA does not explicitly authenticate the GNSS signals, and signal authentication is necessary to protect the signal time of arrival. Otherwise, an adversary can replay the signals and alter the time of arrival, spoofing the position even if the data used to calculate it is authentic.

This work describes a method for protection against signal replay attacks based on Galileo OSNMA and analyses its feasibility in smartphones.

The OSNMA protocol is based on the transmission of message authentication codes and the later disclosure of a cryptographic key used to generate them. Once the key is authenticated, the receiver authenticates the navigation data with the codes and the key, obtaining a data-authenticated position.

As in most cryptographic systems, the authentication codes are unpredictable. This feature makes the Galileo signal unpredictable as well. Our method is based on leveraging this signal unpredictability in combination with assistance information, to protect against signal replays.

The method has been tested in smartphones (Samsung S10 and Xiaomi Mi8) using Android, which provides the GNSS raw measurements and bitstreams. While the Galileo E1-B bitstreams are provided only intermittently, the complete authentication process is performed in less than ten seconds for the first satellite and the total time for authenticating four or more satellites is less than two minutes.

The results show that, while signal antireplay is not the primary purpose of OSNMA, bit unpredictability combined with accurate time synchronization can be exploited to make spoofing more difficult.

Using Navigation Message Authentication in Smartphones to Protect against Replay Attacks

For many animals skylight polarization forms part of a solar compass system, allowing them to hold their course when the sun itself is blocked from view. Honeybees were the first species shown to use this “polarization compass”, and are one of only two species to date demonstrated behaviourally to obtain an unambiguous compass reference by integrating polarization information from different parts of the sky. To achieve this, they are thought to employ an internal map of the skylight polarization pattern.

We present data from recent behavioural experiments aimed at better describing how the honeybee’s polarization compass trades off flexibility against generalisation. Via semi-automated detection and decoding of the “waggle dances” returning foragers use to inform their nest-mates of a food sources’ location, we set out to determine the psychophysical limits for the honeybee’s polarization compass.

Characterising the honeybee’s polarization compass

Navigation is difficult and requires all the senses. The usual scientific reduction methodology does not work for Animal Navigation as the reduction of options to one or a few distorts the overall navigation picture. With quantum mechanics we seem to be be moving towards a universe of consciousness and information. I will discuss why I think that migratory animals plug in this universe to navigate by, as well as using all the information that they can glean from their environment.

Animal Navigation is not magnetic based

Overall measurement error can be reduced by averaging across individual measurements. We simulated a ring attractor network (Touretsky, 2005) to find out whether storing successive measurements in connections to a separate network (modelled as a single neuron), then sending them back as input to the ring attractor could approximate averaging, and whether that depends on the widths of tuning curves. Overall error does decrease as the number of measurements increases, and error decreases faster for networks with wider tuning curves. This happens because the ring attractor network does not truly average the inputs. Instead, its activity bump tends towards the mode of the input, and that is more pronounced for networks with narrow tuning curves that integrate input over a smaller range. The narrower the tuning curve is relative to error, the fewer of the past measurements influence the final state of the activity bump. That favours broader tuning curves, and does not explain the empirical finding of there being a mix of tuning curves (Jung et al., 1994; Giocomo et al., 2014). Other effects, such as metabolic costs, may provide an explanation.

Collaborative Navigation - Synchronizing and Aiding the Absolute Navigation of Swarm Members using Relative Inter-swarm Navigation Information

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