Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments

Autonomuous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the case in urban environments. Thus, the prediction of integrity for specific routes or trajectories is of interest. The carrier-to-noise density ratio (C/N0) reported by the GNSS receiver offers important insights into the signal quality, the carrier phase availability and subsequently the RTK solution integrity. The ultimate goal of this research is to investigate the predictability of the GNSS signal strength. Using a ray-tracing algorithm together with known satellite positions and 3D building models, not only the satellite visibility but also the GNSS signal propagation conditions at waypoints along an intended route are computed. Including antenna gain, free-space propagation as well as reflection and diffraction at surfaces and vegetation, the predicted C/N0 is compared to that recorded by an Septentrio Altus receiver during an experiment in an urban environment in Hannover. Although the actual gain pattern of the receiving antenna was unknown, good agreements were found with small offsets between measured and predicted C/N0

Intelligent GNSS Positioning using 3D Mapping and Context Detection for Better Accuracy in Dense Urban Environments

Conventional GNSS positioning in dense urban areas can exhibit errors of tens of meters due to blockage and reflection of signals by the surrounding buildings. Here, we present a full implementation of the intelligent urban positioning (IUP) 3D-mapping-aided (3DMA) GNSS concept. This combines conventional ranging-based GNSS positioning enhanced by 3D mapping with the GNSS shadow-matching technique. Shadow matching determines position by comparing the measured signal availability with that predicted over a grid of candidate positions using 3D mapping. Thus, IUP uses both pseudo-range and signal-to-noise measurements to determine position. All algorithms incorporate terrain-height aiding and use measurements from a single epoch in time. Two different 3DMA ranging algorithms are presented, one based on least-squares estimation and the other based on computing the likelihoods of a grid of candidate position hypotheses. The likelihood-based ranging algorithm uses the same candidate position hypotheses as shadow matching and makes different assumptions about which signals are direct line-of-sight (LOS) and non-line-of-sight (NLOS) at each candidate position. Two different methods for integrating likelihood-based 3DMA ranging with shadow matching are also compared. In the position-domain approach, separate ranging and shadow-matching position solutions are computed, then averaged using direction-dependent weighting. In the hypothesis-domain approach, the candidate position scores from the ranging and shadow matching algorithms are combined prior to extracting a joint position solution. Test data was recorded using a u-blox EVK M8T consumer-grade GNSS receiver and a HTC Nexus 9 tablet at 28 locations across two districts of London. The City of London is a traditional dense urban environment, while Canary Wharf is a modern environment. The Nexus 9 tablet data was recorded using the Android Nougat GNSS receiver interface and is representative of future smartphones. Best results were obtained using the likelihood-based 3DMA ranging algorithm and hypothesis-based integration with shadow matching. With the u-blox receiver, the single-epoch RMS horizontal (i.e., 2D) error across all sites was 4.0 m, compared to 28.2 m for conventional positioning, a factor of 7.1 improvement. Using the Nexus tablet, the intelligent urban positioning RMS error was 7.0 m, compared to 32.7 m for conventional GNSS positioning, a factor of 4.7 improvement. An analysis of processing and data requirements shows that intelligent urban positioning is practical to implement in real-time on a mobile device or a server. Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. Specifically, 3DMA GNSS should be selected when the user is in a dense urban environment, not indoors or in an open environment. Algorithms for detecting indoor and outdoor context using GNSS measurements and a hidden Markov model are described and demonstrated

Potential of Consumer-Grade Cameras and Photogrammetric Guidelines for Subsurface Utility Mapping

The poor documentation of subsurface utility data is a common problem in many cities, exposing field engineers to risks of utility strike. This paper investigates the use of consumer-grade cameras to improve operational efficiency on construction sites and explores different imaging networks to optimize photogrammetric processing for low-cost subsurface utility surveys. Results from the first part of the study demonstrated the potential of consumer-grade cameras as a photogrammetric utility data acquisition tool. However, statistical insights from the photogrammetric calibration show that caution needs to be taken about the camera types particularly for lens calibration. Results from the second part of the study were recommended as easy-to-understand guidelines for image acquisition at trenches and supported the planning of photogrammetric measurements in the field

Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques

In dense urban areas, conventional GNSS does not perform satisfactorily, sometimes resulting in errors of tens of metres. This is due to the blocking, reflection and diffraction of GNSS satellite signals by obstructions such as buildings and moving vehicles. The 3D mapping data of buildings can be used to predict which GNSS signals are line-of-sight (LOS) and which are non-line-of-sight (NLOS). These data have been shown to greatly improve GNSS performance in urban environments. Location-based services typically use single-epoch positioning, while all pedestrian and vehicle navigation applications use filtered solutions. Filtering can reduce the impact of noise-like errors on the position solution. Kalman filtering-based solutions have been adopted as one of the standard algorithms for GNSS navigation in many different products, and particle filtering has been demonstrated by several research groups. This paper mainly investigates the performance of different filtering algorithms combined with 3D-mapping-aided (3DMA) techniques. In addition to the Kalman filter and particle filter, the grid filter is also considered. In contrast to a particle filter, the hypotheses of a grid filter are uniformly distributed (forming a grid), but with different likelihoods, which better fits the physics of the problem. At the same time, this allows the current UCL’s single-epoch 3DMA GNSS positioning algorithm to be easily extended to multi-epoch situations. This paper then compares the performance of these continuous positioning algorithms in urban environments. The datasets used for testing include pedestrian and vehicle navigation data, covering two main application scenarios that often appear in cities. Pedestrian navigation data is static, and was collected in the City of London using a u-blox EVK M8T GNSS receiver. The vehicle navigation data consists of GPS and Galileo measurements, collected in Canary Wharf by a trials van with a Racelogic Labsat 3 GNSS front end. Subsequently, these data are fed into several single- and multi-epoch filtering algorithms, including single-epoch conventional GNSS, single-epoch 3DMA GNSS, conventional extended Kalman Filter (EKF), conventional particle filter (PF), 3DMA GNSS particle filter (PF), and 3DMA GNSS grid filter (GF). The results show that filtering has a greater impact on the results of mobile positioning with significant movement compared to static positioning. In vehicle tests, the conventional multi-epoch GNSS algorithms improve positioning accuracy by more than 40% compared to single-epoch GNSS, whereas in static positioning they deliver a limited improvement. 3DMA GNSS significantly improves positioning accuracy in the denser environments, but provides little benefit in more open areas. The 3DMA GNSS techniques and the filtering algorithms benefit each other. The former provides the latter with a better position solution at the measurement update step, while the latter in turn repays the former with a better initial position and a smaller search area. In vehicle tests at Canary Wharf, the 3DMA GNSS filtering reduces the overall solution error by approximately 50% and 40% compared to the single-epoch 3DMA GNSS and filtered conventional GNSS, respectively. Thus, multi-epoch 3DMA GNSS filtering should bring maximum benefit to mobile positioning in dense environments. The results from both datasets also confirm that the performance of 3DMA GNSS particle filtering and grid filtering are similar in terms of positional accuracy. In terms of efficiency, 3DMA GNSS grid filtering uses fewer particles to achieve the same coverage of the search area as particle filtering

Characterisation of GNSS carrier phase data on a moving zero-baseline in urban and aerial navigation

We present analyses of Global Navigation Satellite System (GNSS) carrier phase observations in multiple kinematic scenarios for different receiver types. Multi-GNSS observations are recorded on high sensitivity and geodetic-grade receivers operating on a moving zero-baseline by conducting terrestrial urban and aerial flight experiments. The captured data is post-processed; carrier phase residuals are computed using the double difference (DD) concept. The estimated noise levels of carrier phases are analysed with respect to different parameters. We find DD noise levels for L1 carrier phase observations in the range of 1.4–2 mm (GPS, Global Positioning System), 2.8–4.6 mm (GLONASS, Global Navigation Satellite System), and 1.5–1.7 mm (Galileo) for geodetic receiver pairs. The noise level for high sensitivity receivers is at least higher by a factor of 2. For satellites elevating above 30◦, the dominant noise process is white phase noise. For the flight experiment, the elevation dependency of the noise is well described by the exponential model, while for the terrestrial urban experiment, multipath and diffraction effects overlay; hence no elevation dependency is found. For both experiments, a carrier-to-noise density ratio (C/N0) dependency for carrier phase DDs of GPS and Galileo is clearly visible with geodetic-grade receivers. In addition, C/N0 dependency is also visible for carrier phase DDs of GLONASS with geodetic-grade receivers for the terrestrial urban experiment. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques

The performance of different filtering algorithms combined with 3D mapping-aided (3DMA) techniques is investigated in this paper. Several single- and multi-epoch filtering algorithms were implemented and then tested on static pedestrian navigation data collected in the City of London using a u-blox EVK M8T GNSS receiver and vehicle navigation data collected in Canary Wharf, London, by a trial van with a Racelogic Labsat 3 GNSS front-end. The results show that filtering has a greater impact on mobile positioning than static positioning, while 3DMA GNSS brings more significant improvements to positioning accuracy in denser environments than in more open areas. Thus, multi-epoch 3DMA GNSS filtering should bring the maximum benefit to mobile positioning in dense environments. In vehicle tests at Canary Wharf, 3DMA GNSS filtering reduced the RMS horizontal position error by approximately 68% and 57% compared to the single-epoch 3DMA GNSS and filtered conventional GNSS, respectively

GNSS Shadow Matching: The Challenges Ahead

GNSS shadow matching is a new technique that uses 3D mapping to improve positioning accuracy in dense urban areas from tens of meters to within five meters, potentially less. This paper presents the first comprehensive review of shadow matching’s error sources and proposes a program of research and development to take the technology from proof of concept to a robust, reliable and accurate urban positioning product. A summary of the state of the art is also included. Error sources in shadow matching may be divided into six categories: initialization, modelling, propagation, environmental complexity, observation, and algorithm approximations. Performance is also affected by the environmental geometry and it is sometimes necessary to handle solution ambiguity. For each error source, the cause and how it impacts the position solution is explained. Examples are presented, where available, and improvements to the shadow-matching algorithms to mitigate each error are proposed. Methods of accommodating quality control within shadow matching are then proposed, including uncertainty determination, ambiguity detection, and outlier detection. This is followed by a discussion of how shadow matching could be integrated with conventional ranging-based GNSS and other navigation and positioning technologies. This includes a brief review of methods to enhance ranging-based GNSS using 3D mapping. Finally, the practical engineering challenges of shadow matching are assessed, including the system architecture, efficient GNSS signal prediction and the acquisition of 3D mapping data