London Creative and Digital Fusion

date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000The London Creative and Digital Fusion programme of interactive, tailored and in-depth support was designed to support the UK capital’s creative and digital companies to collaborate, innovate and grow. London is a globally recognised hub for technology, design and creative genius. While many cities around the world can claim to be hubs for technology entrepreneurship, London’s distinctive potential lies in the successful fusion of world-leading technology with world-leading design and creativity. As innovation thrives at the edge, where better to innovate than across the boundaries of these two clusters and cultures? This booklet tells the story of Fusion’s innovation journey, its partners and its unique business support. Most importantly of all it tells stories of companies that, having worked with London Fusion, have innovated and grown. We hope that it will inspire others to follow and build on our beginnings.European Regional Development Fund 2007-13

D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Indoor location based services challenges, requirements and usability of current solutions

Indoor Location Based Services (LBS), such as indoor navigation and tracking, still have to deal with both technical and non-technical challenges. For this reason, they have not yet found a prominent position in people’s everyday lives. Reliability and availability of indoor positioning technologies, the availability of up-to-date indoor maps, and privacy concerns associated with location data are some of the biggest challenges to their development. If these challenges were solved, or at least minimized, there would be more penetration into the user market. This paper studies the requirements of LBS applications, through a survey conducted by the authors, identifies the current challenges of indoor LBS, and reviews the available solutions that address the most important challenge, that of providing seamless indoor/outdoor positioning. The paper also looks at the potential of emerging solutions and the technologies that may help to handle this challenge