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Outlier Detection for 3D-Mapping-Aided GNSS Positioning
Abstract
This paper takes 3D-mapping-aided (3DMA) GNSS as an example and investigates the outlier detection for pattern matching based positioning. Three different test statistics, two in the measurement domain and one in the position domain, are presented. Two 3D city maps with different levels of detail were used, one of which contained two obvious errors, to demonstrate the performance of 3DMA GNSS positioning in the presence of errors in the mapping data. The experiments tested were conducted alongside busy roads in the London Borough of Camden, where a total of 8 sets of 2-minute static pedestrian navigation data were collected with a u-blox EVK M8T GNSS receiver. The results confirm that both 3D mapping errors and temporary environmental changes (such as passing vehicles) can have a significant negative impact on the performance of 3DMA GNSS positioning. After applying outlier detection, single-epoch 3DMA GNSS algorithm reduces the horizontal RMS position error by approximately 15% compared to that without outlier detection. The filtering algorithm attenuates the effects of temporary environmental changes, providing an improvement of about 15% over single-epoch positioning, while the outlier algorithm further reduces the RMS error to a comparable level to that of using high-accuracy maps, about 4.7m.
... Since the raw point clouds collected by lidar are only locally referenced for ego-motion estimations (Zhang et al. 2021b;Li et al. 2022b), high-definition (HD) maps, which supply geospatial information of the road environments, are often jointly used to transform lidar measurements to align with their GNSS counterparts for fusion . In addition to the traditional integration of GNSS and lidar aided by HD maps at the solution level (i.e., loose coupling), which mainly aims for multi-path and outlier mitigation such as Wen et al. (2019Wen et al. ( , 2020 and Zhong and Groves (2022), lidar has also been exploited for integer ambiguity resolution in RTK by improving the precision of the float solutions (Qian et al. 2020;Li et al. 2021b;Zhang et al. 2022b). Most recently, the fusion of PPP and lidar have been explored for continuous vehicle positioning by decreasing the convergence time of PPP. ...
Global navigation satellite system (GNSS) and light detection and ranging (lidar) are well known to be complementary for vehicle positioning in urban canyons, where GNSS observations are prone to signal blockage and multi-path. As one of the most common carrier-phase-based precise positioning techniques, precise point positioning (PPP) enables single-receiver positioning as it utilizes state-space representation corrections for satellite orbits and clocks and does not require a nearby reference station. Yet PPP suffers from a long positioning convergence time. In this contribution, we propose to reduce the PPP convergence using an observation-level integration of GNSS and lidar. Lidar measurements, in the form of 3D keypoints, are generated by registering online scans to a pre-built high-definition map through deep learning and are then combined with dual-frequency PPP (DF-PPP) observations in an extended Kalman filter implementing the constant-velocity model that captures the vehicle dynamics. We realize real-time PPP (RT-PPP) in this integration using the IGS real-time service products for vehicle positioning. Comprehensive analyses are provided to evaluate different combinations of measurements and PPP corrections in both static and simulated kinematic modes using data captured by multiple receivers. Experimental results show that the integration achieves cm-level accuracy and instantaneous convergence by using redundant measurements. Accordingly, for classical PPP accuracy of 10 cm and convergence within minutes, respectively, lidar input is only required once every 10 s.
This paper presents a novel algorithm to enhance the accuracy of urban positioning in mobile applications, without requiring high processing. A multipath model is derived to express the multipath effects on the auto-correlation function (ACF). The multipath model is a function in the code delay error, the multipath code delays, the multipath complex amplitudes, and the composite complex amplitude, which consists of the complex amplitudes of the Line-of-Sight (LOS) signal and all the existing multipath signals. 3D buildings model and an accelerated ray tracing algorithm are used to get predictions for the code delay error and the multipath code delays at a range of possible positions. The predicted parameters are used to estimate the multipath complex amplitudes and the composite complex amplitude using measured correlations. The predicted and the estimated parameters are plugged into the multipath model to get a predicted ACF, for each possible position. The idea is that if the predictions are correct, then the estimations will be correct, which will lead to a predicted ACF that closely match the measured ACF. Therefore, a consistency analysis is performed to measure the consistency between each predicted ACF and the measured ACF. The possible position with the highest consistency is taken as the estimated position. The proposed algorithm is called Correlation Prediction and Consistency Analysis (CPCA). The CPCA algorithm is verified using real GPS data collected in Hong Kong. The results show enhancement in urban position estimation compared to a conventional position estimation method and a vector tracking-based method.
Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.
Commercial GNSS devices tend to perform poorly in urban canyon environments. The dense and tall buildings block the signals from many of the satellites. In this work, we present a particle filter algorithm for a Shadow Matching framework to face this problem. Given a 3D city map and given the satellites' signal properties, the algorithm calculates in real-time invalid regions inside the Region Of Interest (ROI). This approach reduces the ROI to a fraction of its original size. We present a general framework for a Shadow Matching positioning algorithm based on a modified particle filter. Using simulation experiments we have shown that the suggested method can improve the accuracy of existing GNSS devices in urban regions. Moreover, the proposed algorithm can be efficiently extended to 3D positioning in high sampling rate, inherently applicable for UAVs and Drones.
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.
The current low-cost global navigation satellite systems (GNSS) receiver cannot calculate satisfactory positioning results for pedestrian applications in urban areas with dense buildings due to multipath and non-line-of-sight effects. We develop a rectified positioning method using a basic three-dimensional city building model and ray-tracing simulation to mitigate the signal reflection effects. This proposed method is achieved by implementing a particle filter to distribute possible position candidates. The likelihood of each candidate is evaluated based on the similarity between the pseudorange measurement and simulated pseudorange of the candidate. Finally, the expectation of all the candidates is the rectified positioning of the proposed map method. The proposed method will serve as one sensor of an integrated system in the future. For this purpose, we successfully define a positioning accuracy based on the distribution of the candidates and their pseudorange similarity. The real data are recorded at an urban canyon environment in the Chiyoda district of Tokyo using a commercial grade u-blox GNSS receiver. Both static and dynamic tests were performed. With the aid of GLONASS and QZSS, it is shown that the proposed method can achieve a 4.4-m 1σ positioning error in the tested urban canyon area.
In urban areas, GNSS localization quality is often degraded due to signal blockage and multi-path reflections. When several GNSS signals are blocked by buildings, the remaining unblocked GNSS satellites are typically in a poor geometry for localization (nearly collinear along the street direction). Multi-path reflections result in pseudo range measurements that can be significantly longer than the line of sight path (true range) resulting in biased geolocation estimates. If a 3D map of the environment is available, one can address these problems by evaluating the likelihood of GNSS signal strength and location measurements given the map. We present two approaches based on this observation. The first is appropriate for cases when network connectivity may be unavailable or undesired and uses a particle filter framework that simultaneously improve both localization and the 3D map. This approach is shown via experiments to improve the map of a section of a university campus while simultaneously improving receiver localization. The second approach which may be more suitable for smartphone applications assumes that network connectivity is available and thus a software service running in the cloud performs the mapping and localization calculations. Early experiments demonstrate the potential of this approach to significantly improve geo-localization accuracy in urban areas.
Non-line-of-sight (NLOS) reception and multipath interference are major causes of poor GNSS positioning accuracy in urban environments. This paper describes three pieces of work to mitigate their effects: a new multipath detection technique using multi-frequency carrier-power-to-noise-density ratio (C/N0) measurements; the first multi-constellation test of the dual-polarization NLOS detection technique; and a proposal for a portfolio approach to multipath and NLOS mitigation.
Constructive multipath interference results in an increase in C/N0, whereas destructive 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 is normally expected for that signal at that elevation angle, strong multipath interference may be detected. A new multipath detection technique based on this principle is demonstrated using data collected in an urban environment.
The dual-polarization NLOS detection technique separately correlates the right hand circularly polarized (RHCP) and left hand circularly polarized (LHCP) outputs of a dual-polarization antenna and differences the C/N0 measurements. The result is positive for directly received signals and negative for most NLOS signals. Here, this technique is demonstrated on GLONASS signals for the first time and the effect of removing an NLOS signal from the position solution is assessed.
Finally, a qualitative assessment and comparison of 18 different classes of technique for mitigating and detecting NLOS reception and multipath interference is presented, considering ease of implementation and performance. It is concluded that, for most applications, no one technique is completely effective. A portfolio approach is therefore proposed in which multiple techniques are combined. Suitable portfolios are then proposed for professional-grade and for consumer-grade user equipment.
Multiple global navigation satellite system (GNSS) constellations can dramatically improve the signal availability in dense urban environments. However, accuracy remains a challenge because buildings block, reflect and diffract the signals. This paper investigates three different techniques for mitigating the impact of non-line-of-sight (NLOS) reception and multipath interference on position accuracy without using additional hardware, testing them using data collected at multiple sites in central London. Aiding the position solution using a terrain height database was found to have the biggest impact, improving the horizontal accuracy by 35% and the vertical accuracy by a factor of 4. An 8% improvement in horizontal accuracy was also obtained from weighting the GNSS measurements in the position solution according to the carrier-power-to-noise-density ratio (C/N0). Consistency checking using a conventional sequential elimination technique was found to degrade horizontal positioning performance by 60% because it often eliminated the wrong measurements in cases when multiple signals were affected by NLOS reception or strong multipath interference. A new consistency checking method that compares subsets of measurements performed better, but was still equally likely to improve or degrade the accuracy. This was partly because removing a poor measurement can result in adverse signal geometry, degrading the position accuracy. Based on this, several ways of improving the reliability of consistency checking are proposed.
Reliable GPS positioning in city environment is a key issue: actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.
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 GPS 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.
With the existing GPS, the replenishment of GLONASS and the launching of Galileo there will be three satellite navigation systems in the future, with a total of more than 80 satellites. So it can be expected that the performance of the global navigation satellite system (GNSS) will be greatly improved, especially in urban environments. This paper studies the potential benefits of GPS/GLONASS/Galileo integration in an urban canyon – Hong Kong. The navigation performances of four choices (GPS alone, GPS+GLONASS, GPS+Galileo and GPS+GLONASS+Galileo) are evaluated in terms of availability, coverage, and continuity based on simulation. The results show that there are significant improvements in availability, coverage and continuity, by using GPS+GLONASS+Galileo compared with the other choices. But the performance is still not good enough for most navigation applications in urban environments.
This contribution addresses position accuracy and availability of satellite based radio navigation for Location Based Services (LBS). Standard standalone GPS positioning with a simple handheld receiver offers 5-10 meter accuracy. With corrective information received from a geostationary satellite, the European Geostationary Navigation Overlay Service (EGNOS) brings the accuracy down to the 1 meter level. Global Differential GPS, with a dual-frequency user receiver, reaches decimeter level accuracy, but only after a considerable initialization period. The highest accuracy, which is at the cm-level, is obtained using differential carrier phase positioning with high-end equipment and a local reference station. Position availability has been analyzed for the historic town center of Delft in the Netherlands. The availability of standalone GPS is less than 50% (considered over a full day) for half of the chosen trajectory. In the heart of the town-center, with very narrow streets and alleys, the availability of EGNOS through the geo-stationary satellites is poor. The availability is in the order of only 10-20% per satellite. The inclusion of Galileo, by a largely increased resource in space, is shown to be particularly beneficial to the position-availability. Whereas an availability of 95% or larger is achieved for only 12% of the trajectory with GPS only, this increases to 75% of the trajectory with Galileo in this challenging urban environment and offers thereby great potential for Location Based Services.
This paper addresses the problem of calculating the ac- curate position of a GNSS device operating in an urban canyon, where lines of sight (LOS) with navigation satel- lites are too few for accurate trilateration calculation. We introduce a post-processing refinement algorithm, which makes use of a 3D map of the city buildings as well as captured signals from all traceable navigation satellites. This includes weak signals originating from satellites with no line of sight (NLOS) with the device. We also address the dual problem - computing a 3D map of the city buildings when the position of the device is given. This is achieved by storing LOS/NLOS rays to all navigation satellites sampled at multiple locations within a region of interest (ROI). These rays are then used to compute the 3D shapes of buildings in the ROI. A series of field experiments confirm that both algo- rithms are applicative. The position refinement algo- rithm significantly improves the device's accuracy and the mapping algorithm allows few users to map a com- plex urban region simply by walking through it.
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.
3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard.
GNSS is being widely used in different applications in navigation. However, GNSS positioning is greatly challenged by notorious multipath effects and non-line-of-sight (NLOS) receptions. The signal blockage and reflection by buildings cause these effects. In other words, the more urbanized the city is, the more challenge on the GNSS positioning. The conventional multipath mitigation approaches, such as the sophisticated design of GNSS receiver correlator, can efficiently mitigate the most of multipath effects. However, it has less capability against NLOS reception, potentially leading to several tens of positioning errors. Therefore, the 3D mapping aided (3DMA) GNSS positioning is introduced to exclude or even use the NLOS signal. Shadow matching is to make use of the similarity between building geometry and satellite visibility to improve the positioning performance. This paper introduces a machine learning intelligent classifier with features to distinguish LOS and NLOS. With the NLOS reception classification, the positioning accuracy of shadow matching can be increased. In addition, this paper develops several indicators to label the unreliable solution of shadow matching. These indicators are to examine the complexity of the surrounding environment, which is the key factor relating to the proposed shadow matching performance. Several designed experiments were done in Hong Kong to evaluate the proposed method. With the intelligent classifier, the average positioning accuracy is about 15m and 6m on 2D and the across-street direction, respectively. Simultaneously, the reliability evaluation rules can exclude unreliable epoch and improve the positioning results, especially on smartphone data.
Different revolutionary applications, like the unmanned autonomous systems, require a highly precise positioning with centimetre-level accuracies. And real-time kinematic (RTK) GNSS positioning stands a chance to these potential applications. RTK positioning can provide a centimetre-level positioning in open-sky or suburban environments. However, the performance is limited in a deep urban canyon with severe multipath and cycle slip effects. The measurements with noise will introduce error and results in bad solution quality. Therefore, removal on the bad measurements and remain those good-condition signals become essential for performing RTK in the urban environment. Based on this idea, this article proposes using 3D building model with position hypothesis to select the healthy satellites for RTK positioning. We believed that using the 3D building model can better exclude the unhealthy or nonline-of-sight satellite compared to using a fixed high elevation angle mask. Therefore, this article will compare the position results between 3D mapping-aided GNSS RTK and the GNSS RTK with a fixed elevation angle mask. Geodetic- and commercial-grade receivers are employed to perform experiments in the urban environment of Hong Kong. The experiment results with geodetic-grade receiver showing that 3DMA GNSS RTK can provide a positioning accuracy with 10 cm averagely.
Conventional GNSS positioning in dense urban areas exhibits errors of tens of meters due to non-line-of-sight (NLOS) reception and multipath interference. Inertially-aided extended coherent integration within the GNSS receiver, as in the case of supercorrelation or S-GPS/GNSS, mitigates these effects by making the receiver more sensitive to directly received signals than reflected signals whenever the receiver is moving. This reduces multipath interference and makes NLOS signals easier to detect using signal-to-noise measurements. 3D-mapping-aided (3DMA) GNSS uses predictions of which signals are NLOS to enhance the positioning algorithms. 3DMA GNSS ranging algorithms can be combined with shadow matching, which uses the signal-tonoise measurements. Both supercorrelation and 3DMA GNSS can improve positioning accuracy in dense urban areas by more than a factor of two. As supercorrelation takes place at the receiver signal processing stage while 3DMA GNSS operates at the positioning algorithm stage, the two techniques are potentially complementary. This paper therefore investigates the benefits of combining them. GNSS signals were recorded using a Racelogic Labsat 3 GNSS front end on a trials van in the Canary Wharf area of London and subsequently processed to generate conventional and supercorrelated ranging and signal-to-noise measurements from the GPS and Galileo satellites. These are then input to several different positioning algorithms, including conventional positioning, shadow matching and likelihood-based 3DMA ranging and a combination of shadow matching and likelihood-based 3DMA ranging. Single-epoch positioning results using code-only pseudo-ranges clearly demonstrate the benefit of supercorrelation with position errors using S-GNSS measurements 40% smaller than those using conventional least-squares positioning techniques. 3DMA GNSS improves the position accuracy by about 25% in the denser environments, but does not bring any benefits in the more open areas. When supercorrelation is combined with filtered carrier-smoothed pseudo-ranges using outlier detection to reject code components that inconsistent with the carrier-based component, the least-squares position errors are reduced by a factor of 5.7 in the densest of environments and a factor of 3.5 elsewhere. With these filtered measurements, 3DMA GNSS techniques have little impact on the positioning accuracy. Thus, 3DMA GNSS techniques are likely to be more useful for snapshot positioning techniques and potentially for the initialization of filtered solutions than for continuous positioning.
Multipath interference and non-line-of-sight (NLOS) reception are major error sources when using global navigation satellite system-based cooperative positioning (CP) in urban environments. In this paper, we develop a multipath and NLOS mitigation algorithm that significantly improves the relative positioning accuracy in urban environments, compared with the absolute positioning and conventional tight CP technique, respectively. In this proposed method, each receiver fuses the Global Positioning System (GPS) pseudorange and Doppler shift measurements, and the linear cooperative navigation observation model is formulated by considering the multipath and NLOS biases as additive sparse errors. We investigate a sparse estimation algorithm to solve the navigation problem and estimate the multipath and NLOS biases, with the weighting matrix designed as function of satellite carrier-to-noise density ratio and the regularization parameter selected by Bayesian information criterion. Based on a least absolute shrinkage and selection operator problem, an ℓ1 regularization is introduced to enforce sparsity in the biases estimation model, which can be solved by the reweighted-ℓ1 algorithm. The GPS measurements can be corrected by subtracting the estimated multipath and NLOS biases, and the relative positioning performance of proposed method is verified by analytical and experimental results.
The recent development in vehicle-to-everything (V2X) communication opens a new opportunity to improve the positioning performance of the road users. We explore the benefit of connecting the raw data of the global navigation satellite system (GNSS) from the agents. In urban areas, GNSS positioning is highly degraded due to signal blockage and reflection. 3D building model can play a major role in mitigating the GNSS multipath and non-line-of-sight (NLOS) effects. To combine the benefits of 3D models and V2X, we propose a novel 3D mapping aided (3DMA) GNSS-based collaborative positioning method that makes use of the available surrounding GNSS receivers' measurements. By complementarily integrating the ray-tracing based 3DMA GNSS and the double difference technique, the random errors (such as multipath and NLOS) are mitigated while eliminating the systematic errors (such as atmospheric delay and satellite clock/orbit biases) between road user. To improve the accuracy and robustness of the collaborative algorithm, factor graph optimization (FGO) is employed to optimize the positioning solutions among agents. Multiple low-cost GNSS receivers are used to collect both static and dynamic data in Hong Kong and to evaluate the proposed algorithm by post-processing. We reduce the GNSS positioning error from over 30 meters to less than 10 meters for road users in a deep urban canyon.
Performing precise positioning is still challenging for autonomous driving. Global navigation satellite system (GNSS) performance can be significantly degraded due to the non-line-of-sight (NLOS) reception. Recently, the studies of 3D building model aided (3DMA) GNSS positioning show promising positioning improvements in urban canyons. In this study, the benefits of 3DMA GNSS are further extended to the GNSS/inertial navigation system (INS) integration system. Based on the shadow matching solution and scoring information of candidate positions, two methods are proposed to better classify the line-of-sight (LOS) and NLOS satellite measurements. Aided by the satellite visibility information, the NLOS-induced pseudorange measurement error can be mitigated before fusing GNSS with the INS in the loosely-coupled or tightly-coupled integration system. Both the proposed satellite visibility estimation methods achieve over 80% LOS/NLOS classification accuracy for most of the scenarios in the urban area, which are at least 10% improvement over the carrier-to-noise ratio (C/N
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)-based method. By further extending the satellite visibility estimation to exclude NLOS measurements and adjust the measurement noise covariance, the proposed 3DMA GNSS/INS tightly-coupled integrated positioning achieves nearly a factor of 3 improvements comparing to the conventional GNSS/INS integration method during the vehicular experiment in the urban canyon.
All travel behavior of people in urban areas relies on knowing their position. Obtaining position has become increasingly easier thanks to the vast popularity of ‘smart’ mobile devices. The main and most accurate positioning technique used in these devices is global navigation satellite systems (GNSS). However, the poor performance of GNSS user equipment in urban canyons is a well-known problem and it is particularly inaccurate in the cross-street direction. The accuracy in this direction greatly affects many applications, including vehicle lane identification and high-accuracy pedestrian navigation. Shadow matching is a new technique that helps solve this problem by integrating GNSS constellation geometries and information derived from 3D models of buildings. This study brings the shadow matching principle from a simple mathematical model, through experimental proof of concept, system design and demonstration, algorithm redesign, comprehensive experimental tests, real-time demonstration and feasibility assessment, to a workable positioning solution. In this thesis, GNSS performance in urban canyons is numerically evaluated using 3D models. Then, a generic two-phase 6-step shadow matching system is proposed, implemented and tested against both geodetic and smartphone-grade GNSS receivers. A Bayesian technique-based shadow matching is proposed to account for NLOS and diffracted signal reception. A particle filter is designed to enable multi-epoch kinematic positioning. Finally, shadow matching is adapted and implemented as a mobile application (app), with feasibility assessment conducted. Results from the investigation confirm that conventional ranging-based GNSS is not adequate for reliable urban positioning. The designed shadow matching positioning system is demonstrated complementary to conventional GNSS in improving urban positioning accuracy. Each of the three generations of shadow matching algorithm is demonstrated to provide better positioning performance, supported by comprehensive experiments. In summary, shadow matching has been demonstrated to significantly improve urban positioning accuracy; it shows great potential to revolutionize urban positioning from street level to lane level, and possibly meter level.
High accuracy seamless positioning is required to support a vast number of applications in varying operational environments. Over the last few years, the global positioning system (GPS) has become the de facto technology for positioning applications. However, its performance is limited in indoor and dense urban environments due to multipath as well as signal attenuation and blockage. A number of techniques integrating GPS with other positioning technologies have been developed to address the limitations of standalone GPS in these difficult environments. While most of the developed techniques cover the outages of GPS in such environments, they do not provide acceptable performance, in terms of positioning accuracy, especially for some mission-critical (e.g. safety) applications. This paper proposes a tightly coupled (i.e. in the measurement domain) GPS/WiFi integration method which, in addition to addressing GPS outages, improves the overall positioning accuracy to the meter-level, thus satisfying the requirements of a number of location based services and intelligent transport systems applications. The performance of the proposed GPS/WiFi integration method is assessed for a number of scenarios in a simulation environment for an identified dense urban area in London, UK.
Positioning using the global navigation satellite system (GNSS) 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 a novel solution to this problem - Shadow Matching. It is to use 3D building models to improve cross-street 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. A preliminary shadow-matching algorithm has been developed to predict whether GNSS satellites in urban canyons are visible or shadowed and then compare this with the receiver-measured signal availability. This is repeated at a number of candidate positions in order to find the best match between predictions and observations. The shadow-matching algorithm has been tested using real-world GPS and GLONASS observations in a London urban canyon. In the tests, shadow matching correctly determined which side of the street the user was in more than 97% of the time, significantly improving on the performance of a conventional GNSS solution.
The poor performance of global navigation satellite system (GNSS) user equipment in urban canyons is a well-known problem, particularly in the cross-street direction. However, the accuracy in the cross-street direction is of great importance in Intelligent Transportation Systems (ITS) and land navigation systems for lane identification, in location-based advertisement (LBA) for targeting suitable consumers and many other location-based services (LBS).
To tackle this problem, a new approach, shadow matching, has been proposed, assisted by knowledge derived from 3D models of buildings. In this work, a new smartphone-based positioning system is designed. The system is then implemented in an application (app, or software) on the Android operating system. With a number of optimizations developed, for the first time, a demonstration is performed on a smartphone using real-time GPS and GLONASS data stream. The computational efficiency of the system is thus verified, showing its potential for larger scale deployment. An experiment was conducted at four different locations, providing a statistical performance analysis of the new system. Analysis was conducted to evaluate the performance of the system. The experimental results show that the proposed system outperforms the conventional GNSS positioning solution, reducing the cross-street positioning error by 69.2 % on average.
It should be noted that the system does not require any additional hardware or real-time rendering of 3D scenes. It is therefore power-efficient and cost-effective. The system is also expandable to work with Beidou (Compass) and Galileo in the future, with potentially improved performance.
The inherent problem of the Global Positioning System (GPS), which is signal obstruction, remains the major obstacle that inhibits it from functioning as a "reliable stand-alone" positioning system. Therefore, it is becoming a common practice to couple the GPS system with an external positioning system whenever the GPS receiver is expected to operate in regions of dense canopy, such as urban areas. Commercial automobile navigation systems currently employ a GPS receiver coupled with a dead reckoning (DR) system and a map-matching algorithm. Most DR systems, which compensate for GPS inaccuracies and frequent GPS signal obstructions, employ an odometer and a directional sensor. In this paper, a power matching approach is proposed for GPS coverage extension in urban area. The algorithm, based on a statistical measure, correlates the received power from different GPS satellite vehicles (SVs), which leads to a specific signature, i.e., to a topographical database with periodic time-varying estimates of the received powers of SVs. An experimental approach is presented to examine the feasibility of applying the proposed positioning system.
Multipath mitigation and NLOS detection using vector tracking in urban environments
- L T Hsu
- S S Jan
- P D Groves
- N Kubo
L. T. Hsu, S. S. Jan, P. D. Groves, and N. Kubo, "Multipath mitigation and NLOS detection using vector
tracking in urban environments," GPS Solutions, vol. 19, no. 2, pp. 249-262, jun 2015. [Online]. Available:
https://link.springer.com/article/10.1007/s10291-014-0384-6
Correcting GNSS multipath errors using a 3D surface model and particle filter
- T Suzuki
- N Kubo
T. Suzuki and N. Kubo, "Correcting GNSS multipath errors using a 3D surface model and particle filter," 26th International
Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2013, vol. 2, pp. 1583-1595, 2013.
The effect of acquisition error and level of detail on the accuracy of spatial analyses
- F Biljecki
- G B Heuvelink
- H Ledoux
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