Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons

Sun, Rui; Fu, Linxia; Wang, Guanyu; Cheng, Qi; Hsu, Li‐Ta; Ochieng, Washington Yotto

doi:10.1002/navi.408

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…Other methods use measurements such as the code-minus-carrier (CMC) and Doppler shift [33], [34]. Furthermore, emerging studies are using machine-learning approaches to develop signal reception condition classifiers using GNSS measurements as features of machine learning [35]- [37].…”

Section: Introductionmentioning

confidence: 99%

The Academic Traditions of Yonsei University Law School – Emphasis on Education, Respect for Humans, and Practice of Reforms –

Kim¹

2023

YLA

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In urban areas, dense buildings frequently block and reflect global positioning system (GPS) signals, resulting in the reception of a few visible satellites with many multipath signals. This is a significant problem that results in unreliable positioning in urban areas. If a signal reception condition from a certain satellite can be detected, the positioning performance can be improved by excluding or de-weighting the multipath contaminated satellite signal. Thus, we developed a machinelearning-based method of classifying GPS signal reception conditions using a dual-polarized antenna. We employed a decision tree algorithm for classification using three features, one of which can be obtained only from a dual-polarized antenna. A machinelearning model was trained using GPS signals collected from various locations. When the features extracted from the GPS raw signal are input, the generated machine-learning model outputs one of the three signal reception conditions: non-line-of-sight (NLOS) only, line-of-sight (LOS) only, or LOS+NLOS. Multiple testing datasets were used to analyze the classification accuracy, which was then compared with an existing method using dual single-polarized antennas. Consequently, when the testing dataset was collected at different locations from the training dataset, a classification accuracy of 64.47% was obtained, which was slightly higher than the accuracy of the existing method using dual single-polarized antennas. Therefore, the dual-polarized antenna solution is more beneficial than the dual single-polarized antenna solution because it has a more compact form factor and its performance is similar to that of the other solution.

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Section: Introductionmentioning

confidence: 99%

The Academic Traditions of Yonsei University Law School – Emphasis on Education, Respect for Humans, and Practice of Reforms –

Kim¹

2023

YLA

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“…Additionally, there are hardware-based detection methods that use a special antenna [50], a sky-pointing camera [51], and a method that uses a three-dimensional (3D) model of a city and ray-tracing techniques [52]. Furthermore, GNSS multipath signal detection techniques using machine learning approaches have been emerging recently [53]- [55].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

Kim,

Byun,

Park

2022

Preprint

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In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.

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“…Among antenna design-based methods, choke-ring and dual-polarization antennas are commonly used to mitigate the effects of multipath and NLOS signals (Sun et al 2021a;Li and Schwieger 2018). The cost and bulky size of the antennas limit the applications for ITS in urban environments, however.…”

Section: Introductionmentioning

confidence: 99%

Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas

et al. 2021

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The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/ IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. The urban experimental results show that the proposed algorithm provides a 3D accuracy of 9.21 m, with an improvement of 55% and 15%, respectively, over the traditional fixed covariance extended Kalman filter (EKF)-based fusion and EKF-based fusion with pseudorange error correction.

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Using dual‐polarization GPS antenna with optimized adaptive neuro‐fuzzy inference system to improve single point positioning accuracy in urban canyons

Cited by 19 publications

References 32 publications

The Academic Traditions of Yonsei University Law School – Emphasis on Education, Respect for Humans, and Practice of Reforms –

The Academic Traditions of Yonsei University Law School – Emphasis on Education, Respect for Humans, and Practice of Reforms –

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas

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