Shadow Matching: A New GNSS Positioning Technique for Urban Canyons

The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining UPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed

NLOS GPS signal detection using a dual-polarisation antenna

The reception of indirect signals, either in the form of non-line-of-sight (NLOS) reception or multipath interference, is a major cause of GNSS position errors in urban environments. We explore the potential of using dual-polarisation antenna technology for detecting and mitigating the reception of NLOS signals and severe multipath interference. The new technique computes the value of the carrier-power-to-noise-density (C/N0) measurements from left-hand circular polarised outputs subtracted from the right-hand circular polarised C/N0 counterpart. If this quality is negative, NLOS signal reception is assumed. If the C/N0 difference is positive, but falls below a threshold based on its lower bound in an open-sky environment, severe multipath interference is assumed. Results from two experiments are presented. Open-field testing was first performed to characterise the antenna behaviour and determine a suitable multipath detection threshold. The techniques were then tested in a dense urban area. Using the new method, two signals in the urban data were identified as NLOS-only reception during the occupation period at one station, while the majority of the remaining signals present were subject to a mixture of NLOS reception and severe multipath interference. The point positioning results were dramatically improved by excluding the detected NLOS measurements. The new technique is suited to a wide range of static ground applications based on our results

Navigation Using Inertial Sensors

This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection

Urban Positioning on a Smartphone: Real-time Shadow Matching Using GNSS and 3D City Models

The performance of global navigation satellite system (GNSS) user equipment in urban canyons is particularly poor in the cross-street direction. This is because more signals are blocked by buildings in the cross-street direction than along the street [1]. To address this problem, shadow matching has been proposed to improve cross-street positioning from street-level to lane-level (meters-level) accuracy using 3D city models. This is a new positioning method that uses the city model to predict which satellites are visible from different locations and then compares this with the measured satellite visibility to determine position [2]. In previous work, we have demonstrated shadow matching using GPS and GLONASS data recorded using a geodetic GNSS receiver in Central London, achieving a cross-street position accuracy within 5m 89% of the time [3]. This paper describes the first real-time implementation of shadow matching on a smartphone capable of receiving both GPS and GLONASS. The typical processing time for the system to provide a solution was between 1 and 2 seconds. On average, the cross-street position accuracy from shadow matching was a factor of four better than the phone’s conventional GNSS position solution. A number of groups have also used 3D city models to predict and, in some cases, correct non-line-of-sight reception [4-6]. However, to our knowledge, this paper reports the first ever demonstration of any 3D-model-aided GNSS positioning technique in real time, as opposed to using recorded GNSS data. When it comes to real-time positioning on a smartphone, various obstacles exist including lower-grade GNSS receivers, limited availability of computational power, memory, and battery power. To tackle these problems, in this work, an efficient smartphone-based shadow-matching positioning system was designed. The system was then implemented in an app (i.e. application or software) on the Android operating system, the most common operating system for smartphones. The app has been developed in Java using Eclipse, a software development environment (SDE). It was built on Standard Android platform 4.0.3, using the Android Application programming interface (API) to retrieve information from the GNSS chip. The new positioning system does not require any additional hardware or real-time rendering of 3D scenes. Instead, a grid of building boundaries is computed in advance and stored within the phone. This grid could also be downloaded from the network on demand. Shadow matching is therefore both power-efficient and cost-effective. Experimental testing was performed in Central London using a Samsung Galaxy S3 smartphone. This receives both GPS and GLONASS satellites and has an assisted GNSS (AGNSS) capability. A 3D city model of the Aldgate area of central London, supplied by ZMapping Ltd, was used. Four experimental locations with different building topologies were selected on Fenchurch Street, a dense urban area. Using the Android app developed in this work, real-time shadow-matching positioning was performed over 6 minutes at each site with a new position solution computed every 5 seconds using both GPS and GLONASS observations were used for real-time positioning. The measurement data was also recorded at 1-second intervals for later analysis. Various criteria are applied to access the new system and compare it with the conventional GNSS positioning results. The experimental results show that the proposed system outperforms the conventional GNSS positioning solution, reducing the mean absolute deviation of the cross-street positioning error from 14.81 m to 3.33 m, with a 77.5 percentage reduction. The feasibility of deploying the new system on a larger scale is also discussed from three perspectives: the availability of 3D city models and satellite information, data storage and transfer requirements, and demand from applications. This meters-level across-street accuracy in urban areas benefits a variety of applications from Intelligent Transportation Systems (ITS) and land navigation systems for automated lane identification to step-by-step guidance for the visually impaired and for tourists, location-based advertisement (LBA) for targeting suitable consumers and many other location-based services (LBS). The system is also expandable to work with Galileo and Beidou (Compass) in the future, with potentially improved performance. In the future, the shadow-matching system can be implemented on a smartphone, a PND, or other consumer-grade navigation device, as part of an intelligent positioning system [7], along with height-aided conventional GNSS positioning, and potentially other technologies, such as Wi-Fi and inertial sensors to give the best overall positioning performance. / References [1] Wang, L., Groves, P. D. & Ziebart, M. Multi-constellation GNSS Performance Evaluation for Urban Canyons Using Large Virtual Reality City Models. Journal of Navigation, July 2012. [2] Groves, P. D. 2011. Shadow Matching: A New GNSS Positioning Technique for Urban Canyons The Journal of Navigation, 64, pp417-430. [3] Wang, L., Groves, P. D. & Ziebart, M. K. GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring. ION GNSS 2012. [4] Obst, M., Bauer, S. and Wanielik, G. Urban Multipath Detection and mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization. IEEE/ION PLANS 2012. [5] Peyraud, S., Bétaille, D., Renault, S., Ortiz, M., Mougel, F., Meizel, D. and Peyret, F. (2013) About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm. Sensors, Vol. 13, 2013, 829?847. [6] Bourdeau, A., M. Sahmoudi, and J.-Y. Tourneret, “Tight Integration of GNSS and a 3D City Model for Robust Positioning in Urban Canyons,” Proc. ION GNSS 2012. [7] Groves, P. D., Jiang, Z., Wang, L. & Ziebart, M. Intelligent Urban Positioning using Multi-Constellation GNSS with 3D Mapping and NLOS Signal Detection. ION GNSS 2012

The Limits of In-run Calibration of MEMS and the Effect of New Techniques

Inertial sensors can significantly increase the robustness of an integrated navigation system by bridging gaps in the coverage of other positioning technologies, such as GNSS or Wi-Fi positioning [1]. A full set of chip-scale MEMS accelerometers and gyros can now be bought for less than $10, potentially opening up a wide range of new applications. However, these sensors require calibration before they can be used for navigation[2]. Higher quality inertial sensors may be calibrated “in-run” using Kalman filter-based estimation as part of their integration with GNSS or other position-fixing techniques. However, this approach can fail when applied to sensors with larger errors which break the Kalman filter due to the linearity and small-angle approximations within its system model not being valid. Possible solutions include: replacing the Kalman filter with a non-linear estimation algorithm, a pre-calibration procedure and smart array [3]. But these all have costs in terms of user effort, equipment or processing load. This paper makes two key contributions to knowledge. Firstly, it determines the maximum tolerable sensor errors for any in-run calibration technique using a basic Kalman filter by developing clear criteria for filter failure and performing Monte-Carlo simulations for a range of different sensor specifications. Secondly, it assesses the extent to which pre-calibration and smart array techniques enable Kalman filter-based in-run calibration to be applied to lower-quality sensors. Armed with this knowledge of the Kalman filter’s limits, the community can avoid both the unnecessary design complexity and computational power consumption caused by over-engineering the filter and the poor navigation performance that arises from an inadequate filter. By establishing realistic limits, one can determine whether real sensors are suitable for in-run calibration with simple characterization tests, rather than having to perform time-consuming empirical testing

Assessment of the Multipath Mitigation Effect of Vector Tracking in an Urban Environment

Today, smart mobiles play an important role in our daily life. Most of these devices are equipped with a navigation function based on GNSS positioning. However, these devices may not work accurately in urban environments due to severe multipath interference and non-line of sight (NLOS) reception caused by nearby buildings. A promising approach for reducing the effect of multipath interference and NLOS reception is vector tracking (VT). VT is well-known for its robustness against poor signal-to-noise levels. However, its capability against multipath and NLOS has yet to be determined. The new combination of this paper is therefore to evaluate the performance of vector tracking in the presence of multipath and NLOS effects. A vector delay lock loop (VDLL) is used as the vector tracking technique. The noise tuning of the extended Kalman filter (EKF) in vector tracking is a key factor affecting its performance. Therefore, developed an adaptive noise tuning algorithm had been based on the measurement innovation. In order to evaluate vector tracking’s performance, equivalent conventional tracking loops are used as a control. GNSS signals were collected, while walking around in a challenging urban environment subject to multipath interference. The experimental results show that VT generates a more stable code numerical-controlled oscillator (NCO) frequency than CT does. This characteristic could reduce the impact of multipath interference which is reflected in a smaller position error using VT during most of run. To further test capability of VT against signal attenuation, this paper applies a signal cancellation method called direct signal cancellation algorithm to simulate the scenario of signal termination and NLOS reception. According to the simulation, VT provides not only robustness against signal termination but can also detect NLOS reception without any external aiding

GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Prediction Scoring

The poor performance of global navigation satellite systems (GNSS) user equipment in urban canyons is a well-known problem, especially in the cross-street direction. A new approach, shadow matching, has recently be proposed to improve the cross-street accuracy using GNSS, assisted by knowledge derived from 3D models of the buildings close to the user of navigation devices. In this work, four contributions have been made. Firstly, a new scoring scheme, a key element of the algorithm to weight candidate user locations, is proposed. The new scheme takes account of the effects of satellite signal diffraction and reflection by weighting the scores based on diffraction modelling and signal-to-noise ratio (SNR). Furthermore, an algorithm similar to k-nearest neighbours (k-NN) is developed to interpolate the position solution over an extensive grid. The process of generating this grid of building boundaries is also optimized. Finally, instead of just testing at two locations as in the earlier work, realworld GNSS data has been collected at 22 different locations in this work, providing a more comprehensive and statistical performance analysis of the new shadowmatching algorithm. In the experimental verification, the new scoring scheme improves the cross street accuracy with an average bias of 1.61 m, with a 9.4% reduction compared to the original SS22 scoring scheme. Similarly, the cross street RMS is 2.86 m, a reduction of 15.3%. Using the new scoring scheme, the success rate for determining the correct side of a street is 89.3%, 3.6% better than using the previous scoring scheme; the success rate of distinguishing the footpath from a traffic lane is 63.6% of the time, 6.8% better than using the previous scoring scheme

Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

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. This paper aims to determine the behavioural and environmental contexts together, building the foundation of a context-adaptive navigation system. Both behavioural and environmental context detection results are presented. A hierarchical behavioural recognition scheme is proposed, within which the broad classes of human activities and vehicle motions are detected using measurements from accelerometers, gyroscopes, magnetometers and the barometer on a smartphone by decision trees (DT) and Relevance Vector Machines (RVM). The detection results are further improved by behavioural connectivity. Environmental contexts (e.g., indoor and outdoor) are detected from GNSS measurements using a hidden Markov model. The paper also investigates context association in order to further improve the reliability of context determination. Practical test results demonstrate improvements of environment detection in context determination