Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-tonoise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment. K E Y

With ever-more demanding requirements for the accurate manufacture of large components, dimensional measuring techniques are becoming progressively more sophisticated. This review describes some of the more recently developed techniques and the state-of-the-art in the more well-known large-scale dimensional metrology methods. In some cases, the techniques are described in detail, or, where relevant specialist review papers exist, these are cited as further reading. The traceability of the measurement data collected is discussed with reference to new international standards that are emerging. In some cases, hybrid measurement techniques are finding specialized applications and these are referred to where appropriate.

Global navigation satellite system (GNSS) positioning is widely used in land vehicle and pedestrian navigation systems. Nevertheless, in urban canyons GNSS remains inaccurate due to building blockages and reflections, especially in the cross-street direction. Shadow matching is a new technique, recently proposed for improving the cross-street positioning accuracy using a 3D model of the nearby buildings. This paper presents a number of advances in the shadow-matching algorithm. First, a positioning algorithm has been developed, interpolating between the top-scoring candidate positions. Furthermore, a new scoring scheme has been developed that accounts for signal diffraction and reflection. Finally, the efficiency of the process used to generate the grid of building boundaries used for predicting satellite visibility has been improved. Real-world GNSS data has been collected at 22 different locations in central London to provide the first comprehensive and statistical performance analysis of shadow matching.

Positioning using the Global Positioning System (GPS) is unreliable in dense urban areas with tall buildings and/or narrow streets, known as 'urban canyons'. This is because the buildings block, reflect or diffract the signals from many of the satellites. This paper investigates the use of 3-Dimensional (3-D) building models to predict satellite visibility. To predict Global Navigation Satellite System (GNSS) performance using 3-D building models, a simulation has been developed. A few optimized methods to improve the efficiency of the simulation for real-time purposes were implemented. Diffraction effects of satellite signals were considered to improve accuracy. The simulation is validated using real-world GPS and GLObal NAvigation Satellite System (GLONASS) observations. The performance of current and future GNSS in urban canyons is then assessed by simulation using an architectural city model of London with decimetre-level accuracy. GNSS availability, integrity and precision is evaluated over pedestrian and vehicle routes within city canyons using different combinations of GNSS constellations. The results show that using GPS and GLONASS together cannot guarantee 24-hour reliable positioning in urban canyons. However, with the addition of Galileo and Compass, currently under construction, reliable GNSS performance can be obtained at most, but not all, of the locations in the test scenarios. The modelling also demonstrates that GNSS availability is poorer for pedestrians than for vehicles and verifies that cross-street positioning errors are typically larger than along-street due to the geometrical constraints imposed by the buildings. For many applications, this modelling technique could also be used to predict the best route through a city at a given time, or the best time to perform GNSS positioning at a given location.

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Sidereal filtering is a technique used to reduce errors caused by multipath in the positioning of static receivers via the Global Positioning System (GPS). It relies upon the receiver and its surrounding environment remaining static from one day to the next and takes advantage of the approximately sidereal repeat time of the GPS constellation geometry. The repeating multipath error can thus be identified, usually in the position domain, and largely removed from the following day. We describe an observation-domain sidereal filter algorithm that operates on undifferenced ionospheric-free GPS carrier phase measurements to reduce errors caused by multipath. It is applied in the context of high-rate (1 Hz) precise point positioning of a static receiver. An observation-domain sidereal filter (ODSF) is able to account for the slightly different repeat times of each GPS satellite, unlike a position-domain sidereal filter (PDSF), and can hence be more effective at reducing high-frequency multipath error. Using 8-h long datasets of GPS measurements from two different receivers with different antenna types and contrasting environments, the ODSF algorithm is shown overall to yield a position time series 5-10 % more stable, in terms of Allan deviation, than a PDSF over nearly all time intervals below about 200 s in length. This may be particularly useful for earthquake and tsunami early warning systems where the accurate measurement of small displacements of the ground over the period of just a few minutes is crucial. However, the sidereal filters are also applied to a third dataset during which two short episodes of particularly high-frequency multipath error were identified. These two periods are analyzed in detail and illustrate the limitations of using sidereal filters with important implications for other methods of correcting for multipath at the observation level.

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