GNSS/INS integrated solution has been extensively studied over the past decades. However, its performance relies heavily on environmental conditions and sensor costs. The GNSS positioning can obtain satisfactory performance in the open area. Unfortunately, its accuracy can be severely degraded in a highly urbanized area, due to the notorious multipath effects and none-line-of-sight (NLOS) receptions. As a result, excessive GNSS outliers occur, which causes a huge error in GNSS/INS integration. This paper proposes to apply a fish-eye camera to capture the sky view image to further classify the NLOS and line-of-sight (LOS) measurements. In addition, the raw INS and GNSS measurements are tightly integrated using a state-of-the-art probabilistic factor graph model. Instead of excluding the NLOS receptions, this paper makes use of both the NLOS and LOS measurements by treating them with different weightings. Experiments conducted in typical urban canyons of Hong Kong showed that the proposed method could effectively mitigate the effects of GNSS outliers, and an improved accuracy of GNSS/INS integration was obtained, when compared with the conventional GNSS/INS integration.

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Tightly Coupled GNSS/INS Integration via Factor Graph and Aided by Fish-Eye Camera

Global navigation satellite systems (GNSS) are one of the utterly popular sources for providing globally referenced positioning for autonomous systems. However, the performance of the GNSS positioning is significantly challenged in urban canyons, due to the signal reflection and blockage from buildings. Given the fact that the GNSS measurements are highly environmentally dependent and time-correlated, the conventional filtering-based method for GNSS positioning cannot simultaneously explore the time-correlation among historical measurements. As a result, the filtering-based estimator is sensitive to unexpected outlier measurements. In this paper, we present a factor graph-based formulation for GNSS positioning and real-time kinematic (RTK). The formulated factor graph framework effectively explores the time-correlation of pseudorange, carrier-phase, and doppler measurements, and leads to the non-minimal state estimation of the GNSS receiver. The feasibility of the proposed method is evaluated using datasets collected in challenging urban canyons of Hong Kong and significantly improved positioning accuracy is obtained, compared with the filtering-based estimator.

Towards Robust GNSS Positioning and Real-time Kinematic Using Factor Graph Optimization

This article deals with the non-line-of-sight (NLOS) reception issue in the field of global navigation satellite system (GNSS). The NLOS reception has attracted a significant amount of attention because it is one of the main factors that limit the GNSS position accuracy in urban areas. In this article, we dig into the baseband signal processing level to explore a new solution to the NLOS detection and correction by means of the vector tracking loop (VTL). The NLOS effects on both conventional scalar tracking loops (STLs) and VTL are derived mathematically. Based on this, an NLOS detection algorithm is developed using metrics, such as the equivalent noise bandwidth, the time delay of multicorrelator peaks, as well as code discriminator outputs. Once detected, the NLOS-induced measurement error is corrected before being fed forward into the navigation estimator to improve the position accuracy. Two field tests in urban areas in Hong Kong are conducted to illustrate the effectiveness of the proposed method in real applications. The NLOS correction performance is also assessed using simulated NLOS receptions with controllable time delays and reflection coefficients, which reveals how the proposed algorithm performs in different NLOS scenarios.

/pdf/vector-tracking-loop-based-gnss-nlos-detection-and-3fc0875xv9.pdf

Vector Tracking Loop-Based GNSS NLOS Detection and Correction: Algorithm Design and Performance Analysis

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.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S037346332000003X

A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method

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.

3D Mapping Database Aided GNSS Based Collaborative Positioning Using Factor Graph Optimization

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.

3D Mapping Database-Aided GNSS RTK and Its Assessments in Urban Canyons

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.

Robust GNSS Shadow Matching for Smartphones in Urban Canyons

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

Hoi Fung Ng

Papers

A Computation Effective Range-Based 3D Mapping Aided GNSS with NLOS Correction Method

3D Mapping Database Aided GNSS Based Collaborative Positioning Using Factor Graph Optimization

3D Mapping Database-Aided GNSS RTK and Its Assessments in Urban Canyons

Robust GNSS Shadow Matching for Smartphones in Urban Canyons

GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research