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厂耻尘尘补谤测:听The Global Navigation Satellite Systems (GNSS), e.g. GPS, are main positioning technology for many Location-Based听Service (LBS) applications, such as navigation and emergency services. While existing indoor positioning services, e.g.听those based on Wi-Fi and Bluetooth, do not provide a free, accurate, continuous, reliable and privacy-preserving or听鈥淕PS-like鈥� indoor positioning service, inside the buildings, where people spend most of their time, GNSS signals can be听blocked, attenuated or reflected, making indoor positioning unreliable or impossible.
This research proposes two novel techniques to provide a seamless (indoor/outdoor) GNSS-only positioning service.听The first technique combines measurements over a short period of time, or at close-enough locations, to compute the听user鈥檚 position. It will be used when fewer than four satellites (absolute minimum requirement of GNSS-based听positioning), are available at a particular time and location. The second technique, GNSS fingerprinting, extracts the听spatio-temporal patterns from the signals, e.g. the obscuration/unavailability patterns of the satellites or the signal听attenuation听patterns, from historical GNSS signals stored over time (potentially contributed by public and volunteers),听orbital data, 3D model of city (i.e. obstructions and barriers). In the positioning mode, the newly received signals are听matched with the stored database and the most likely location is found.
These step-changing techniques will enable:听-researchers in many areas, e.g. intelligent mobility and smart city, to apply/extend the project鈥檚 concepts and听techniques,听-wide public participation in research,听-LBS (e.g. navigation, tracking, emergency, security, and special assistance services) to provide continuous and reliable听services, enhancing quality of, and potentially saving, lives.
Workflow/Deliverables:This project proposes two novel techniques to provide seamless (indoor-outdoor), continuous, free-to use, privacy听preserving GNSS-based positioning services, requiring no further infrastructure or mobile device modification. This听can be used by many LBS applications, including life-saving emergency and security services.听GNSS positioning in difficult environments, i.e. urban canyons and indoors, suffers from several types of error,听including multipath, non-line-of-sight (NLOS) signals, signal attenuation and signal blockage. GNSS signals can be听reflected by 听urfaces of objects (NLOS) or blocked by objects e.g. buildings and trees, or attenuated with respect to听distance travelled through an object or medium, e.g. windows, (signal attenuation). The reflected GNSS signals can听interfere with reception of the signals received directly from the satellites听(multipath). Since GNSS positioning is based听on ranging measurements (i.e. time taken for the signal to get to the receiver from the satellite), NLOS, multipath and听signal attenuation can all cause positioning errors. Blockage of GNSS signals may result in a lack of availability of the听minimum of four satellites in-view ((absolute minimum requirement of GNSS-based positioning) and consequently听lead to a failure in the continuity of the positioning service.
This project proposes two techniques, based on the concepts of a virtual spatial diversity antenna and GNSS signal听fingerprinting. The first technique combines the raw GNSS observations (which have been made accessible recently听on mobile devices running Android 7 and higher operating systems) over a short period of time, or at close-enough听locations, to compute the user鈥檚 position. It will be used when fewer than four satellites, are available at a particular听time and location. This enables any currently available mobile devices to calculate position, by having more听observations over a longer period of time or from another location that is close enough (depend on applications and听scenarios) to be considered as one single point. Each epoch (set of observations) adds one more unknown, its clock听offset; therefore in total at least n+3 observations are required, where "n" is number of epochs. As the measurements听do not have to be made at the same time, it can be applied in a collaborative or crowd-sourced scheme, where听spatially close users can share measurements and localise themselves. Crowd-sourced-gathered datasets and听collaborative positioning schemes can improve the quality and availability of input datasets.
Secondly, GNSS fingerprinting, extracts the spatio-temporal patterns from the raw measurements, e.g. the听obscuration patterns of the satellites, and the signal attenuation. These patterns are extracted from historical GNSS听signals stored over time or contributed by the crowd, orbital data, 3D model of city, e.g. obstructions and barriers at a听high level of detail (e.g. LoD4). In the positioning mode, the newly received signals are matched with the stored听database and the most likely location is found.
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Dr Ana Basiri
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