United Kingdom

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.

Navigation 2021

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

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!

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"

The positioning service using Japan's quasi-zenith satellite system "MICHIBIKI" is currently operated with a system of four aircraft, and it has been decided that there will be a system of seven aircraft by the end of FY2023. Since there is always one or more "MICHIBIKI" satellites in the direction of the zenith of Japan, reception is possible even in mountainous areas and urban areas with many high-rise buildings. Therefore, high-precision positioning is possible by receiving the reinforcement signal and the like, and it is expected to be used for automatic driving of vehicles and unmanned aerial vehicles. In Japan, the mobile phone communication service LTE (Long Term Evolution) is provided at frequencies close to L1 used in GNSS.

In this study, we investigated the effect of mobile phone radio waves on the CLAS (cm-Level Augmentation Service) provided by "MICHIBIKI". As a result of fixed-point positioning by CLAS, it was confirmed that the time zone when the mobile phone communication increases significantly and the time zone when the positioning accuracy deteriorates coincide while the ionosphere is calm at night. The error was 3 cm on average and more than 15 cm instantaneously. Therefore, when applying CLAS to the automatic driving of automobiles or the automatic delivery of drones in the vicinity of mobile base stations, it may be necessary to pay attention to the deterioration of accuracy.

"Impact of mobile phone radio waves on the cm-Level Augmentation Service provided by the Quasi-Zenith Satellite System in Japan"

Ships nowadays greatly rely on Global Navigation Satellite Systems (GNSSs) in order to determine their position.
Since GNSS outages or jamming events do occur, there are efforts to reduce the dependency on GNSS for maritime Navigation.
One such effort is called R-Mode (Ranging Mode), and focuses on complementing maritime communication systems by
a ranging component to determine the position on the vessel. One of the systems to be extended by R-Mode is the VHF Data Exchange System (VDES).
The VDES communication system is currently in standardization and offers 100 kHz of bandwidth in the maritime VHF band. It utilizes single carrier modulation with π/4-QPSK.
The proposed R-Mode extension works by sending a precisely timed known data sequence, so that time of arrival estimation allows determination of the range.
Using software defined radios (SDR), we implemented a test setup for VDES R-Mode with three base stations and one receiver.
Using this setup, we performed the first VDES R-Mode positioning trials on the Lake Ammer in Germany using different ranging sequences.
By determining the time of the arrival as well as the Doppler shift of the received signals we tracked the boat’s position with an Unscented Kalman Filter.
The positioning accuracy performance ranged to up to 10 meter under favourable conditions.
Crucial was the consideration of the Doppler measurements to enhance tracking performance considerably.

On the Positioning Performance of VDES R-Mode

Session 1.3.

Monitoring GNSS and detecting threats to it are crucial to the better understanding of the scale and risk posed to GNSS end users by PNT disruption, and therefore to being able to counter the threat. Under the GNSS Event Notification Service (GENS) project, CGI has developed scalable and effective tools that fuse multiple data sources to enable live situation awareness of GNSS spectrum quality as well as long term trend analysis. In this process, CGI has integrated its PNT Incident Event Monitoring (PNTIEM) system with existing UK-developed sensors from Ordnance Survey’s (OS) OS NET network of GNSS receivers and GMV NSL’s DETECTOR and Strike 3 interface technologies.

In this talk, CGI will present a summary of the project achievements, how it has brought together its Navigation and Spectrum domain expertise and its scaled agile and SecDevOps delivery experience to create the foundations of a national GNSS event monitoring and incident management capability for the UK. The talk will include key points fed into the project technical direction through the stakeholder engagement, as well as presenting an overview of the system, its innovative aspects such as the data fusion approach, and some of the surprising headline themes that emerged from the observed results. The talk will conclude by covering the recommended next steps for follow-on work.

Status Update on the UK NAVISP GENS project (GNSS Event Notification Service

Adaptive notch filters (ANFs) have shown good interference mitigation capabilities against chirp interferences. However, the chirp dynamics and jump discontinuities in frequency limit ideal ANF tuning for all chirp types. An ANF removes a spectrally sparse interference through a notch filter, and an adaptive algorithm determines the center frequency of the chirp as it changes over time. One adaptive algorithm approach uses a conventional frequency-locked loop (FLL) with fixed settings to follow the chirp frequency. A critical tuning parameter of the FLL is the loop-bandwidth, which is a trade-off between the agility and noise suppression of the FLL. On the one hand, large bandwidth adapts quickly and is suitable for addressing the chirp’s jump-discontinuities but results in noisy estimates. On the other hand, a small bandwidth responds slow but results in more precise tracking.

This paper applies adaptive loop techniques commonly deployed in adaptive scalar tracking loops (ASTLs) to alter the loop-bandwidth of the FLL. As a result, the adaptive FLL-ANF (AFLL-ANF) responds much better against jump discontinuities by temporarily increasing the loop-bandwidth before settling back to a smaller loop-bandwidth once it is on the chirp signal. In recent publications, the loop-bandwidth control algorithm (LBCA) has shown promising results for adapting tracking global navigation satellite system (GNSS) receiver tracking loops, and in this paper, it is applied to a new application for FLL-based notch filters. Initial results indicate improved tracking errors compared to fixed-tuned FLL-ANFs and superior alpha-mean values.

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