Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from urban GNSS users. However, the algorithms developed for the aerospace domain cannot be introduced directly to the GNSS land applications. This is because a high data redundancy exists in the aviation domain and the hypothesis that only one failure occurs at a time is made, which is not the case for the urban users. The main objective of this paper is to provide an overview of the past and current literature discussing the GNSS integrity for urban transport applications so as to point out possible challenges faced by GNSS receivers in such scenario. Key differences between integrity monitoring scheme in aviation domain and urban transport field are addressed. And this paper also points out several open research issues in this field.

/pdf/gnss-position-integrity-in-urban-environments-a-review-of-1ved0w5512.pdf

GNSS Position Integrity in Urban Environments: A Review of Literature

A new technique for detecting GNSS multipath interference by comparing signal-to-noise (SNR) measurements on three frequencies is presented. Depending on the phase lag of the reflected signal with respect to the direct signal, multipath interference can be either constructive or destructive, with a commensurate effect on the measured SNR. However, as the phase lag is frequency dependent, the SNR is perturbed differently on each frequency. Thus, by differencing SNR measurements on different frequencies and comparing the result with that obtained in a low-multipath environment, multipath can be detected. Using three frequencies makes the process more robust. A three-frequency SNR-based multipath detector has been developed and calibrated using measurements from GPS Block IIF satellites in a low-multipath environment. The new detector has been tested in a range of urban environments and its multipath detection capability verified by showing that the MP observables oscillate when the new detection statistic is above a threshold value determined using data collected in a low-multipath environment. The new detector is also sensitive to diffraction.

/pdf/gnss-multipath-detection-using-three-frequency-signal-to-4bpropu1ef.pdf

GNSS multipath detection using three-frequency signal-to-noise measurements

Multipath interference and non-line-of-sight (NLOS) reception are major error sources when using global navigation satellite systems in urban environments. A promising approach to minimize the effect of multipath interference and aid NLOS detection is vector tracking. Therefore, the objective of this research was to assess vector tracking in a dense urban environment to determine its effect on multipath interference and NLOS reception. Here, a vector delay lock loop is implemented using an adaptive extended Kalman filter. This replaces the individual code-tracking loops and navigation filter, but retains conventional carrier frequency tracking. The positioning and tracking performance of the conventional and vector-tracking implementations with and without a strobe correlator are compared using intermediate frequency signals recorded in the Koto-Ku district of urban canyon Tokyo city environment. Both static and dynamic tests were performed. It is shown that vector tracking reduces the root-mean-square positioning error by about 30 % compared with an equivalent conventional receiver in urban environments and is capable of detecting long-delay NLOS reception for a GPS receiver without any external aiding .

/pdf/multipath-mitigation-and-nlos-detection-using-vector-1i3cu9zozu.pdf

Multipath mitigation and NLOS detection using vector tracking in urban environments

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., Betaille, 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.

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

eliable positioning in urban canyon, especially in dense urban areas, is difficult to achieve in a cost-effective manner using standalone Global Navigation Satellite System (GNSS), due to multipath problems and Non-Line-of-Sight (NLOS) signals. In this regard, several researches have focused towards identifying and rejecting NLOS measurements. However, very few researches have used NLOS signals, although generating final position using only one of the signals. In this regard, this research utilizes all the available signals, including all NLOS signals, from all the satellites, in order to estimate position, using an algorithm based on concept of constructive use of NLOS signals. The NLOS signals are used constructively by incorporating the information related to nearby reflectors, with the help of a 3D city model. The feasibility and performance of the algorithm was done using a real data collected in Downtown Calgary. In a way, this research attempts towards building a low cost GNSS based technology for improved navigation performance, in scenarios where fewer satellites are available and rejecting measurements due to blunders might cost a crucial stake of availability.

A Novel GNSS Positioning Technique for Improved Accuracy in Urban Canyon Scenarios Using 3D City Model

Non-line-of-sight (NLOS) reception and multipath interference are major causes of poor GNSS positioning accuracy in dense urban environments. They are commonly grouped together. However, both the mechanisms by which they cause position errors and many of the techniques for mitigating those errors are quite different [1]. For example, correlation-based multipath mitigation has no effect on the errors caused by NLOS reception. University College London (UCL) has investigated the performance of a number of multipath and/or NLOS mitigation techniques in dense urban areas, including C/N0-based solution weighting [2], advanced consistency checking [3], dual-polarization NLOS detection [4] and vector tracking [5]. In this paper, we present a new multipath detection technique based on comparing the measured C/N0 on multiple frequencies and also new dual-polarization results. Meanwhile, other researchers have demonstrated NLOS detection using a panoramic camera [6, 7] or 3D city model [8, 9] and detection of NLOS and multipath using an antenna array [10]. All of these techniques bring some improvement in positioning performance in urban environments, but none of them eliminate the effects of both NLOS reception and multipath interference completely. As the different techniques are largely complementary, best performance is obtained by using several of them in combination, a portfolio approach. This paper comprises three parts. The first presents a feasibility study on a new multipath detection technique using multi-frequency C/N0 measurements. Constructive multipath interference results in an increase in the measured C/N0, whereas destructive multipath interference results in a decrease. As the phase of a reflected signal with respect to its directly received counterpart depends on the wavelength, the multipath interference may be constructive on one frequency and destructive on another. Thus, by comparing the difference in measured C/N0 between two frequencies with what would normally be expected for that signal at that elevation angle, strong multipath interference may be detected. However, the converse is not true because, depending on the path delay, the phase of the multipath interference may also be consistent across the two frequencies. Consistency across three frequencies in the presence of multipath interference is much less likely than consistency across two. Therefore, by comparing C/N0 measured across three (or more) frequencies, the chance of detection is improved substantially, noting that reliability is less critical as part of a portfolio approach to multipath detection than for a stand-alone technique. Experimental results are presented demonstrating the potential of this approach using GPS and GLONASS data collected in Central London. The second part of the paper presents the results of the first multi-constellation test of the dual-polarization NLOS detection technique pioneered at UCL [4]. This separately correlates the right hand circularly polarized (RHCP) and left hand circularly polarized (LHCP) outputs of a dual-polarization antenna and differences the resulting C/N0 measurements, producing a result that is positive for directly received signals and negative for most NLOS signals. Data was collected at six different sites in Central London and NLOS reception of both GPS and GLONASS signals was detected. Position solutions with the NLOS signals removed are compared with the corresponding all-satellite solutions. The final part of the paper addresses the portfolio approach to NLOS and multipath mitigation. Each technique is assessed qualitatively for its ease of implementation and its efficiency at detecting or directly mitigating both NLOS reception and multipath mitigation. A compatibility matrix is then presented showing which techniques may be combined without conflict. Suitable portfolios are then proposed both for professional-grade and for consumer-grade user equipment. References [1] Groves, P. D., Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Second Edition, Artech House, 2013. [2] Jiang, Z., P. Groves, W. Y. Ochieng, S. Feng, C. D. Milner, and P. G. Mattos, “Multi-Constellation GNSS Multipath Mitigation Using Consistency Checking,” Proc. ION GNSS 2011. [3] Jiang, Z., and P. Groves, “GNSS NLOS and Multipath Error Mitigation using Advanced Multi-Constellation Consistency Checking with Height Aiding,” Proc. ION GNSS 2012. [4] Jiang, Z., and P. D. Groves, “NLOS GPS Signal Detection Using A Dual-Polarisation Antenna,” GPS Solutions, 2012, DOI: 10.1007/s10291-012-0305-5. [5] Hsu, L.-T., P. D. Groves, and S.-S. Jan, “Assessment of the Multipath Mitigation Effect of Vector Tracking in an Urban Environment,” Proc ION Pacific PNT, 2013. [6] Marais, J., M. Berbineau, and M. Heddebaut, “Land Mobile GNSS Availability and Multipath Evaluation Tool,” IEEE Transactions on Vehicular Technology, Vol. 54, No. 5, 2005, pp. 1697-1704. [7] Meguro, J., et al., “GPS Multipath Mitigation for Urban Area Using Omnidirectional Infrared Camera,” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 1, 2009, pp. 22-30. [8] Obst, M., S. Bauer, and G. Wanielik, “Urban Multipath Detection and mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization,” Proc. IEEE/ION PLANS 2012. [9] Peyraud, S., et al., “About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm,” Sensors, Vol. 13, 2013, pp. 829-847. [10] Keshvadi, M. H., A. Broumandan, and G. Lachapelle, “Analysis of GNSS Beamforming and Angle of Arrival Estimation in Multipath Environments," Proc ION ITM, San Diego, CA, January 2011, pp. 427-435.

P. Strode

Papers

A Portfolio Approach to NLOS and Multipath Mitigation in Dense Urban Areas