Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled ...integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an “EKF‐like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.
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.
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.
Sensors play important roles for autonomous driving. Localization is definitely a key one. Undoubtedly, global positioning system (GPS) sensor will provide absolute localization for almost all the ...future land vehicles. In terms of driverless car, 1.5 m of positioning accuracy is the minimum requirement, since the vehicle has to keep in a driving lane that usually wider than 3 m. However, the skyscrapers in highly-urbanized cities, such as Tokyo and Hong Kong, dramatically deteriorate GPS localization performance, leading more than 50 m of error. GPS signals are reflected at modern glassy buildings, which caused the notorious multipath effect. Fortunately, the number of navigation satellite is rapidly increasing in a global scale, since the rise of multi-global navigation satellite system. It provides an excellent opportunity for positioning algorithm developer of GPS sensor. More satellites in the sky imply more measurements to be received. Novelty, this paper proposes to take advantage of the fact that clean measurements (refers to line-of-sight measurement) are consistent and multipath measurements are inconsistent. Based on this consistency check, the faulty measurements can be detected and excluded to obtain better localization accuracy. Experimental results indicate that the proposed method can achieve less than 1-m lateral positioning error in middle urban canyons.
Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard ...inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes.
This article proposes an improved global navigation satellite system (GNSS) positioning method that explores the time correlation between consecutive epochs of the code and carrier-phase ...measurements, which significantly increases the robustness against outlier measurements. Instead of relying on the time difference carrier phase which only considers two neighboring epochs using an extended Kalman filter estimator, this article proposed to employ the carrier-phase measurements inside a window, the so-called window carrier phase (WCP), to constrain the states inside a factor graph. A left null space matrix is employed to eliminate the shared unknown ambiguity variables and, therefore, correlate the associated states inside the WCP. Then, the pseudorange, Doppler, and the constructed WCP measurements are integrated simultaneously using factor graph optimization to estimate the state of the GNSS receiver. We evaluated the performance of the proposed method in two typical urban canyons in Hong Kong, achieving the mean positioning errors of 1.76 and 2.96 m, respectively, using the automobile-level GNSS receiver. Meanwhile, the effectiveness of the proposed method is further evaluated using a low-cost smartphone-level GNSS receiver, and similar improvement is also obtained when compared with several existing GNSS positioning methods.
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.
Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be ...significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.
The GNSS performance could be significantly degraded by the interferences in an urban canyon, such as the blockage of the direct signal and the measurement error due to reflected signals. Such ...interferences can hardly be predicted by statistical or physical models, making urban GNSS positioning unable to achieve satisfactory accuracy. The deep learning networks, specializing in extracting abstract representations from data, may learn the representation about the GNSS measurement quality from existing measurements, which can be employed to predict the interferences in an urban area. In this study, we proposed a deep learning network architecture combining the conventional fully connected neural networks (FCNNs) and the long short-term memory (LSTM) networks, to predict the GNSS satellite visibility and pseudorange error based on GNSS measurement-level data. The performance of the proposed deep learning networks is evaluated by real experimental data in an urban area. It can predict the satellite visibility with 80.1% accuracy and predict the pseudorange errors with an average difference of 4.9 meters to the labeled errors. Experiments are conducted to investigate what representations have been learned from data by the proposed deep learning networks. Analysis results show that the LSTM layer within the proposed networks may contain representations about the environment, which affects the prediction behavior and can associate with the real environment information.
We present a novel method to detect the GNSS NLOS and correct the NLOS pseudorange measurements based on on‐board sensing. This paper demonstrates the use of LiDAR scanner and a list of building ...heights to describe the perceived environment. To estimate the geometry and pose of the top edges of buildings (TEBs) relative to the GNSS receiver, a surface segmentation method is employed to detect the TEBs of surrounding buildings using 3D LiDAR point clouds. The top edges of the building are extracted and extended using the building height list in Skyplot to identify the NLOS‐affected ones. Innovatively, the NLOS delay in pseudorange is corrected based on the detected TEBs. Weighted least squares (WLS) is used to cooperate the corrected NLOS and other pseudorange measurements. Vehicle experiments are conducted in two different urban canyons to verify the effectiveness of the proposed method in improving GNSS single point positioning (SPP) accuracy.
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