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Multi-Epoch 3D-Mapping-Aided Positioning using Bayesian Filtering Techniques
Abstract
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 building models provide the perception of the actual environment as a software-based aided positioning approach for the low-cost, namely 3D mapping aided (3DMA) GNSS (Groves, 2016). Existing studies by (Zhong & Groves, 2022) and (Ng et al., 2021) show a superior positioning performance of 3DMA GNSS in urban canyons. Doppler measurements are often used to integrate with the position solution to provide smoother positioning results. ...
... Both approaches validate the signal transmission path and calculate the reflection delay with the predicted reflecting point. The likelihood-based ranging (Zhong & Groves, 2022) statistically uses a skew-normal distribution to model the NLOS delay measurements. Then it remaps the errors to the LOS one with the normal distribution. ...
... Extending the single epoch positioning approach to temporal connected can increase the robustness of the positioning performance. (Zhong & Groves, 2022) adopts a grid filter to distribute positioning candidates evenly to improve the smoothness of the solution. An alternative way is using the FGO to connect the temporal domain as a batch optimization to increase the overall robustness and smoothness. ...
This paper discusses ubiquitous smartphone pedestrian positioning challenges in urban canyons and GNSS-denied areas such as indoor spaces. Existing sensor-based techniques, including GNSS, INS, and VIO, have limitations that affect positioning accuracy and reliability. A machine learning-based approach is suggested to employ Support Vector Machine (SVM) to classify indoor/outdoor (IO) detection using GNSS measurement data. The proposed system integrates local estimates on VIO and 3D mapping aided (3DMA) GNSS measurements using Factor Graph Optimization (FGO) with an IO detection switch to estimate precise pose and eliminate global drift. The effectiveness of the system is evaluated through real-world experiments that produce notable outcomes.
... Two multi-epoch 3DMA GNSS algorithms have been described in detail in [32]. Since 3DMA GNSS particle filtering and grid filtering have similar performance, only the results of particle filtering are demonstrated in this paper. ...
... In contrast to the particle filtering algorithm described in [32], the algorithm used in this paper introduces one of the outlier detection techniques mentioned in sections III.2 and III.3 during the initialisation and 3DMA GNSS scoring stages, while all other parts remain the same. ...
... A more detailed description of the above algorithms can be found in [6,32]. Single-epoch 3DMA GNSS has been demonstrated in Canary Wharf [6], and a multi-epoch version incorporating either particle or grid filtering has been demonstrated in the City of London and Canary Wharf [32]. ...
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.
... The study proposed measuring the position integrity as a set-based approach to bound the remaining systematic uncertainty. The statistical approach, also known as likelihood-based ranging [34], uses a skew-normal distribution to model the NLOS delay measurements and then remap the errors to the LOS with the normal distribution. ...
... The complementary nature of the two approaches inspired researchers to integrate them. The latest study shows that an integrated solution of 3DMA GNSS can provide positioning accuracy of around 10 m or less in urban canyons with both single-frequency [34] and multi-frequency [35] measurements. ...
... Researchers also use particle filters to effectively distribute and sample the candidates [26,38]. Moreover, a grid filter was adopted to distribute positioning candidates evenly [34]. The filtering techniques demonstrate excellent results in improving the smoothness of the positioning solution. ...
Smart health applications have received significant attention in recent years. Novel applications hold significant promise to overcome many of the inconveniences faced by persons with disabilities throughout daily living. For people with blindness and low vision (BLV), environmental perception is compromised, creating myriad difficulties. Precise localization is still a gap in the field and is critical to safe navigation. Conventional GNSS positioning cannot provide satisfactory performance in urban canyons. 3D mapping-aided (3DMA) GNSS may serve as an urban GNSS solution, since the availability of 3D city models has widely increased. As a result, this study developed a real-time 3DMA GNSS-positioning system based on state-of-the-art 3DMA GNSS algorithms. Shadow matching was integrated with likelihood-based ranging 3DMA GNSS, generating positioning hypothesis candidates. To increase robustness, the 3DMA GNSS solution was then optimized with Doppler measurements using factor graph optimization (FGO) in a loosely-coupled fashion. This study also evaluated positioning performance using an advanced wearable system’s recorded data in New York City. The real-time forward-processed FGO can provide a root-mean-square error (RMSE) of about 21 m. The RMSE drops to 16 m when the data is post-processed with FGO in a combined direction. Overall results show that the proposed loosely-coupled 3DMA FGO algorithm can provide a better and more robust positioning performance for the multi-sensor integration approach used by this wearable for persons with BLV.
... For the estimation of these motion quantities, the use of EKF approaches and their derivatives has become established, since higher-dimensional state vectors can be implemented in a computationally efficient manner and these sensors satisfy the strict requirements of parametric estimation methods. This has also been applied in previous works addressing multi-epoch 3DMA filtering in (Zhong and Groves, 2022b). ...
... A visualization of the calculation step for a candidate position within a probability grid is shown as an example in fig. 7. Due to the finite nature of the discrete state space representation, a system propagation for global positioning problems needs to also be accounted, as moving objects are able to leave the initially defined state space. A procedure for this task is detailed in (Zhong and Groves, 2022b) and therefore is not further addressed in this paper. ...
... The NLOS correction implemented through the ranging-based 3DMA GNSS may be statistical or geometrical. Likelihood-based ranging 3DMA GNSS uses a statistical approach to remap the NLOS pseudorange error to LOS measurements (Zhong & Groves, 2022). In contrast, ray-tracing GNSS (Hsu et al., 2016;Miura et al., 2015) and skymask 3DMA GNSS attempt to identify the reflection point and estimate the reflection delay. ...
... A particle filter has been used to effectively distribute and sample the candidates (Suzuki, 2016;Yozevitch & Moshe, 2015). Moreover, a grid filter has been used to evenly distribute positioning candidates (Zhong & Groves, 2022). These studies have demonstrated that the filtering technique can provide a better and smoother positioning performance. ...
... Shadow matching can improve the positioning accuracy in the cross-street direction [16], [17]. On the other hand, the likelihood-based ranging method can be applied for the along-street direction using the pseudorange measurement with statistical NLOS corrections [18]. Moreover, the 3DMA GNSS ray tracing can simulate the NLOS errors based on signal propagation geometries [19] and apply corrections to improve the positioning accuracy [20], [21], [22]. ...
Accurate positioning from the global navigation satellite system (GNSS) is critical for various civil applications, like location-based services and intelligent transportation systems. GNSS Doppler frequency can provide reliable velocity estimation to improve positioning performance. Unfortunately, the quality of Doppler frequency measurements can be significantly degraded in urban canyons. This is due to the non-line-of-sight (NLOS) receptions altering the incoming signal direction and its dynamic characteristic. Thus, correcting the NLOS error on Doppler frequency is essential for the velocity as well as position estimation in urban canyons. The 3D mapping aided (3DMA) GNSS is a promising technique for NLOS error correction. Its effectiveness on pseudorange measurements has been well proven. However, its feasibility on Doppler frequency correction has not been investigated yet. Therefore, this paper first verifies the feasibility of ray-tracing in modelling Doppler frequency. Then, an urban Doppler frequency assessment is conducted. Finally, the effectiveness of ray-tracing in correcting velocity estimation accuracy is evaluated. The assessment and evaluation assessment are conducted via experiments in both open-sky and urban areas. Results demonstrate ray-tracing has an excellent capability in modelling the NLOS Doppler frequency, which reduces the corresponding measurement error by 62.8% in average and the root-mean-square of velocity estimation error by 51.92%.
... Poor GNSS positioning in urban canyons: GNSS positioning is significantly degraded in urban canyons because of signal reflection and blockage, leading to the notorious multipath and non-line-of-sight (NLOS) phenomenon (Groves et al., 2013;Hsu, 2018). Numerous existing methods have been investigated to mitigate the impacts of multipath and NLOS reception, such as three-dimensional (3D) mapping-aided (3DMA) GNSS (Zhong & Groves, 2022), camera-aided GNSS NLOS detection (Kato et al., 2016;Meguro et al., 2009;Wen, Bai, et al., 2019), and 3D lidar-aided GNSS NLOS detection or correction (Wen, Zhang, et al., 2019). Unfortunately, the GNSS positioning achieved thus far is still far from sufficient for fully autonomous systems requiring decimeter-level accuracy and stringent integrity requirements. ...
... Advances in 3D mapping, building modeling, ray tracing, and machine learning paved the way for a promising class of multipath and NLOS mitigation techniques (Sokhandan et al., 2017;Lau and Cross, 2007;Ziedan, 2017Ziedan, , 2021Miura et al., 2013;Bradbury et al., 2007;Hsu et al., 2016). A popular sub-class of those techniques are based on shadow matching and 3D mapping Zhong and Groves, 2022;Strandjord et al., 2020;Yozevitch and Ben-Moshe, 2015;Groves et al., 2020). This sub-class encompasses a wide variety of approaches with varying implementation complexities. ...
Multipath and non-line-of-sight (NLOS) signals are a major concern for the application of GNSS positioning in challenging environments. The positioning accuracy degradation, due to these reflected signals, hinders the dependence on GNSS systems. This has led to either integrating GNSS systems with other systems or dropping GNSS systems completely in favor of other systems.
This paper proposes a novel system model for multipath delay estimation; the system is called MDE. The MDE system is integrated with a modified version of an Optimized Position Estimation (OPE) algorithm; the new algorithm is called OPE-MDE. In conventional navigation algorithms, given code delays, a position is estimated. The proposed MDE system does the opposite. Given a position, code delays are estimated. Since the actual position is not known, the MDE system can serve as a validation algorithm to generate a probability that a candidate position is correct. The idea of the proposed MDE system is to construct a model that relates the errors in the individual pseudorange measurements, of each available satellite, to the errors in the estimated receiver coordinates.
The proposed OPE-MDE algorithm keeps track of the most likely paths over several steps. Each path has an associated weight. After several steps, the path with the optimal weight is taken as the estimated path, and the positions that constitute the estimated path are taken as the estimated positions. The OPE-MDE algorithm utilizes the MDE system to compute a probability that a candidate position is correct, where this probability is a part of the path weight. The proposed OPE-MDE algorithm is tested using three scenarios. One of the scenarios has high dynamics properties. The OPE-MDE algorithm shows a 34% positioning accuracy enhancement over a previous optimized position estimation algorithm.
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.
Location-Based Services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS relies on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything (V2X) communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This article elaborates the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas.
The objective of this paper is to enhance the accuracy of urban positioning using all the available LOS, multipath, and NLOS signals. Three algorithms are presented to achieve this objective. The first algorithm is an accelerated ray tracing technique that first eliminates the 3D surfaces that are invisible with respect to a position, and then analyzes the visible surfaces to predict the existence and path lengths of reflected signals. The ray tracing algorithm is applied on the possible range of positions. The second algorithm is a Markov Chain Monte Carlo (MCMC) based algorithm that applies both the Gibbs sampler and the Metropolis-Hastings technique to analyze the received correlated signals to estimate the delays of reflected signals for all the received signals. The third algorithm is a Van Rossum based technique that measures the discrepancy between the estimated delays and the predicted ones at a range of possible positions, where the position that generates the minimum discrepancy is taken as the estimated position. Experimental tests are conducted at two different areas, with different characteristics. The first area is located at the campus of Zagazig University, Egypt, and the second area is located at the campus of Wuhan University, China. The results indicate the ability of the algorithms to successfully utilize reflected signals to enhance urban positioning accuracy.
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.
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.
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.
Positioning in the urban environment using GNSS is hampered by poor satellite availability due to signal obstruction created by both man-made and natural features of the urban environment. In addition, range measurement to satellites for positioning and for navigation is severely degraded by the multipath effect. The arrival and continuous enhancement of computerised geometric city models makes it possible to tackle these problems through modelling. In this paper description is given of a method for determining the local multipath environment, defined by the surfaces within a city model that will cause disruptive signal reflections to be presented to a receiver. An example simulation is performed, and graphical and numerical results are produced.
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.
Capturing a wave of innovation and creativity in the field, this greatly expanded edition of
Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems combines a comprehensive
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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.
This chapter describes the characteristics of reflected and diffracted signals and how they produce non‐line‐of‐sight (NLOS) and multipath errors. It also describes how multipath errors can be reduced using advanced receiver design and signal processing techniques, including antenna design considerations, correlation signal processing, and adaptive antenna array processing. The chapter covers carrier smoothing of code measurements, which is a technique for mitigating both noise and multipath. It describes real‐time navigation‐processor‐based NLOS and multipath mitigation techniques, including C/N0‐based detection and weighting, outlier detection, and aiding from other sensors. The chapter then describes multipath mitigation techniques for post‐processed high‐precision positioning that work by analyzing time series of global navigation satellite system (GNSS) measurement data. Finally, it describes three‐dimensional‐mapping‐aided GNSS.
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.
Global navigation satellite system (GNSS) positioning in dense urban areas remains a challenge due to the signal reflection by buildings, namely multipath and non-line-of-sight (NLOS) reception. These effects degrade the performance of low-cost GNSS receivers such as in those smartphones. An effective three-dimensional (3D) mapping aided GNSS positioning method is proposed to correct the NLOS error. Instead of applying ray-tracing simulation, the signal reflection points are detected based on a skyplot with the surrounding building boundaries. The measurements of the direct and reflected signals can thus be simulated and further used to determine the user's position based on the measurement likelihood between real measurements. Verified with real experiments, the proposed algorithm is able to reduce the computational load greatly while maintaining a positioning accuracy within 10 metres of error in dense urban environments, compared with the conventional method of ray-tracing based NLOS corrected positioning.
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.
In this study, we propose a novel global navigation satellite system (GNSS) positioning technique that can be used in urban canyon environments where GNSS positioning is almost useless. Multipath signals, which are reflected or diffracted by objects such as buildings, are recognized as the most important causes of major positioning errors in urban environments. This problem has been investigated for many years. Various practical and popular signal correlator techniques can also help to mitigate multipath errors. However, if an antenna cannot receive a direct signal (line of sight signal), these techniques do not produce satisfactory results because they assume that the antenna mainly receives direct and multipath signals. Thus, we propose a novel GNSS positioning technique that can be used in multipath environments, which is based on a multipath error simulation using a 3D surface model of a building. To calculate a user's position based on multipath simulation, it is necessary to predetermine their position accurately because the multipath effect is highly dependent on the surrounding obstructions. Thus, a particle filter, which hypothesizes a number of user positions, is used to solve this problem, thereby allowing the multipath simulation to estimate the position. The proposed technique attempts to estimate a user's position by comparing the distance between the particle position and the point positioning solution using pseudoranges to correct the multipath error, which is estimated from the multipath simulation. The multipath error in the observed pseudorange depends on a signal correlator design, which is implemented using GNSS receivers. The consumer's GNSS receivers cannot be used to estimate multipath errors because the correlator is a black box. Therefore, we use a GNSS software receiver to implement the proposed techniques. A positioning test was performed in a real-world urban canyon environment, which confirmed the effectiveness of the proposed technique. The proposed technique is effective and it provides increased positioning accuracy in urban canyon environments that suffer from large reflection and diffraction multipath errors in GNSS signals.
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.
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.
Digital maps with 3D data proved to make it possible the determination of Non-Line-Of-Sight (NLOS) satellites in real time, whilst moving, and obtain significant benefit in terms of navigation accuracy. However, such data are difficult to handle with Geographical Information System (GIS) embedded software in real time. The idea developed in this article consists is proposing a method, light in terms of information contents and computation throughput, for taking into account the knowledge of the 3D environment of a vehicle in a city, where multipath phenomena can cause severe errors in positioning solution. This method makes use of a digital map where homogeneous sections of streets have been identified, and classified among different types of urban trenches. This classification is so called: "Urban Trench Model". Not only NLOS satellites can be detected, but also, if needed, the corresponding measurements can be corrected and further used in the positioning solver. The paper presents in details the method and its results on several real test sites, with a demonstration of the gain obtained on the final position accuracy. The benefit of the Urban Trench Model, i.e. the reduction of positioning errors as compared to conventional solver considering all satellites, gets up to an amount between 30% and as much as 70% e.g. in Paris.
Accurate and reliable positioning is an important prerequisite for numerous vehicular applications. Localization techniques based on satellite navigation systems are nowadays standard and deployed in most commercial vehicles. When such a standalone positioning is used in challenging environments like dense urban areas, the localization performance often dramatically degrades due to blocked and reflected satellites signals. In this paper, a general and lightweight probabilistic positioning algorithm with integrated multipath detection through 3D environmental building models is presented. It will be shown that the proposed system outperforms—in terms of accuracy and integrity—existing methods without introducing additional hardware sensors. Furthermore, a benefit analysis of the suggested D model for tightly and loosely coupled GPS/INS sensor integration schemas is provided. Finally, the algorithm will be evaluated with real-world data collected during an urban measurement campaign.
Pseudo-range (PR) errors due to NLOS-Multipath (non-line-of-sight-multipath) are studied in an urban canyon model. In order to determine the different reflected and diffracted rays which compose the NLOS-multipath, a dedicated ray tracing algorithm is applied. Two different methods are used in order to compute the PR error. The first one uses the error due to the maximum power ray and the second one uses an early minus late (E-L) receiver model. Simulations in different urban canyon configurations are carried out in order to obtain PR error distributions and associated probabilities due to NLOS-multipath rays above a given power threshold.
The Global Positioning System (GPS) is a satellite-based navigation and time transfer system developed by the U.S. Department of Defense. It serves marine, airborne, and terrestrial users, both military and civilian. Specifically, GPS includes the Standard Positioning Service (SPS) which provides civilian users with 100 meter accuracy, and it serves military users with the Precise Positioning Service (PPS) which provides 20-m accuracy. Both of these services are available worldwide with no requirement for a local reference station. In contrast, differential operation of GPS provides 2- to 10-m accuracy to users within 1000 km of a fixed GPS reference receiver. Finally, carrier phase comparisons can be used to provide centimeter accuracy to users within 10 km and potentially within 100 km of a reference receiver. This advanced tutorial will describe the GPS signals, the various measurements made by the GPS receivers, and estimate the achievable accuracies. It will not dwell on those aspects of GPS which are well known to those skilled in the radio communications art, such as spread-spectrum or code division multiple access. Rather, it will focus on topics which are more unique to radio navigation or GPS. These include code-carrier divergence, codeless tracking, carrier aiding, and narrow correlator spacing.
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