United Kingdom

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.

Navigation 2021

Characterising the honeybee’s polarization compass

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

### 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!

Session 1.2.

NextNav has developed a TerraPoiNT system, a terrestrial-based positioning, navigation and timing system which can complement navigation systems as well as serve as GNSS-backup. TerraPoiNT is a Time-Of-Arrival (TOA) multilateration system offering 3D positioning and timing through a dedicated terrestrial network of synchronized transmitters using Code Division Multiple Access spread spectrum signals like GPS. TerraPoiNT transmitters can synchronize between themselves without the need for an absolute time source like GNSS, thus proving to be an ideal alternative to the GPS. TerraPoiNT offers vertical positioning through integration with barometric and environmental reference information provided over the data payload. The beacons are designed to detect anomalies in position and timing. This capability enables TerraPoiNT to detect local anomalies in any absolute time system like GPS. TerraPoiNT’s innovative methods to address the near-far reception problem coupled with the short multipath resolution capability enables it to provide a robust performance in any environment. TerraPoiNT signals can penetrate inside the building enabling TerraPoiNT receivers to conquer the frontier of indoor positioning. TerraPoiNT System capabilities were assessed by the U.S. Department of Transportation at LaRC as part of the alternate navigation technology assessment for positioning and navigation under various scenarios including car and drone navigation. TerraPoiNT technology showed exemplary performance in all the tests under different dynamics. The results from the vehicle navigation test provide accuracy within 3m (50%) and 8m (95%). TerraPoiNT receiver utilized fusion of TerraPoiNT signals integrated with inertial sensors in these 2D navigation tests. Drone navigation test results are within 3D performance < 3m (50%) and < 5m (95%). In these drone tests, TerraPoiNT receiver utilized only the TerraPoiNT signals without any inertial sensor fusion. TerraPoiNT system demonstrated accurate, reliable and robust performance in entire trials and it was rated #1 among all the technologies evaluated both in position and navigation trials

TerraPoiNT: An APNT system to augment and enhance urban/Indoor navigation

The European Commission has completed a study under the Horizon 2020 research programme to investigate the opportunity for developing new services based on the Galileo Return Link Message. One of these services is the two-way communication over Return Link. 

The messaging service is a mean to improve the situational awareness of a person in distress and can be beneficial in managing the rescue operation. It offers the RCC operator the possibility of sending instructions or collecting information on the on-going distress situation by contacting the beacon user directly: for instance, giving instructions to facilitate the rescue, obtaining information about the number of people to be rescued, the urgency of the situation, etc...

Several applications for the two-way messaging are set out below, which can be used both in the aeronautical and maritime domain on condition that the person in distress has access to the beacon (survival, portable):



To collect additional information on the on-going distress situation regarding the persons in distress, their environment and their equipment;
To guide the persons in distress to optimize the rescue operation;
To inform the persons in distress on how to behave to maximise their chances of survival; 
To confirm distress in case of manual activation (avoiding a false alert).


The EC has launched a project named SERENITY carried out by a consortium composed of Telespazio France, CNES, Thales Alenia Space France, Orolia, Pildo Labs, in order to develop beacon prototypes and perform a service demonstration. The SERENITY project will push forward the TWC service adoption with the provision of Mission/Service/Beacon requirements, the development of a beacon prototype and the set-up of the service demonstration involving the Beacon prototype and the SAR operational chain (SARR, MEOLUT, MCC …) to be disseminated towards the SAR community including the Cospas-Sarsat programme.

The Galileo SAR Two-Way Communication Service : presentation of the future service and end-to-end demonstration preparation.

Shadow matching can significantly improve GNSS positioning accuracy in urban canyons by matching the observed signal availability from measurements and predicted availability from 3D building models. However, some limitations hinder its application, such as poor along-street accuracy and ambiguous solution from scoring grids. This paper improves shadow matching method by integrating road information with multi-epoch filtering.

First, the proposed method improves signal availability prediction from raw measurements within shadow matching algorithm. A machine learning model has been developed to use signal-to-noise ratio, elevation and ranging based features to classify LOS/NLOS signals. This achieves higher classification accuracy than using signal-to-noise measurement alone.

Second, the road information has been implemented to aid hypothesis positioning from the scoring grids. After calculating the matching scores, all candidate grids are clustered and weighted by scores to identify the best matching street among the search region, so that ambiguous solution from multiple high scoring candidates can be largely avoided. The error covariance in across and along-street direction can then be estimated from the grids on that street, which will be converted into navigation coordinates based on road angle and used for filtering.

Third, shadow matching solution is loosely integrated with the conventional GNSS positioning for multi-epoch filtering. An initial EFK based state estimates can be obtained from the previous filtered solution and satellite measurements at current epoch, which will supply to shadow matching with better position initialization and ranging measurements without clock bias. Then the shadow matching solution and its error covariance are further integrated within the Kalman filter to update position estimates along only across-street direction.

The worldwide urban tests show the proposed method can achieve about 10m positioning accuracy and more than 30% same-street-side improvement than shadow matching. This would benefit various location based applications in dense urban like Uber taxiing.

Integration of Shadow Matching with Road Information for Improved Mobile Urban Positioning

One of the most important functions for the safety of autonomous driving is the self-localisation of a moving platform. The GNSS (Global Navigation Satellite System) sensor is - compared to other sensors as, e.g., video, radar or camera - the only sensor that provides absolute positioning. Under clear sky conditions, GNSS positioning methods yield high level of accuracy and integrity. Poor signal propagation conditions make precise GNSS positioning challenging under urban conditions where multipath degrades the attainable accuracy and integrity. In the last decades, various correction or mitigation approaches for multipath errors have been developed and tested, but for carrier phase observations it is still the most critical error with magnitudes of up to a quarter of the wavelength. Since the carrier phase multipath error is location dependent, this property can be used to construct a correction map. 

In this paper, we report on the necessary steps to construct such a multipath correction map.

The carrier phase error is a function of the multipath characteristics and the extra path delay. Hence, it is dependent on the distance to the reflection surface. Using a 3D building model, the geometry between the building and potentially multipath affected satellite signals is established to obtain one periodic cycle of the carrier phase error.

A pseudo-kinematic experiment in an urban environment is planned and designed based on simulation studies of a ray-tracing algorithm to ensure ideal receiving properties for multipath signals in a realistic scenario. 

Two different approaches of carrier phase residuals will be compared to derive the multipath error from the observations. Firstly, double difference residuals are computed. Secondly, carrier phase residuals extracted from a positioning engine will be analysed.

Finally, the developed functions are evaluated in the context of generating GNSS multipath correction maps for moving platforms.

"Analysis of the Carrier Phase Multipath Error in the Context of Correction Maps for Urban Navigation"

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

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.

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