The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-ofsight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods.

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This paper builds on the machine learning research to propose two new algorithms based on optimizing the Adaptive Neuro Fuzzy Inference System (ANFIS) with a dual‐polarization antenna to predict pseudorange errors by considering multiple variables including the right‐hand circular polarized (RHCP) signal strength, signal strength difference between the left‐hand circular polarized (LHCP) and RHCP outputs, satellites’ elevation angle, and pseudorange residuals. The final antenna position is calculated following the application of the predicted pseudorange errors to correct for the effects of non‐line‐of‐sight (NLOS) and multipath signal reception. The results show that the proposed algorithm results in a 30% improvement in the root mean square error (RMSE) in the 2D (horizontal) component for static applications when the training and testing data are collected at the same location. This corresponds to 13% to 20% when the testing data is from locations away from that of the training dataset.

Global navigation satellite system is indispensable to provide positioning, navigation, and timing information for pedestrians and vehicles in location-based services. However, tree canopies, although considered as valuable city infrastructures in urban areas, adversely degrade the accuracy of global navigation satellite system positioning as they attenuate the satellite signals. This article proposes a bagging tree-based global navigation satellite system pseudorange error prediction algorithm, by considering two variables, including carrier to noise C/ N0 and elevation angle θe to improve the global navigation satellite system positioning accuracy in the foliage area. The positioning accuracy improvement is then obtained by applying the predicted pseudorange error corrections. The experimental results shows that as the stationary character of the geostationary orbit satellites, the improvement of the prediction accuracy of the BeiDou navigation satellite system solution (85.42% in light foliage and 83.99% in heavy foliage) is much higher than that of the global positioning system solution (70.77% in light foliage and 73.61% in heavy foliage). The positioning error values in east, north, and up coordinates are improved by the proposed algorithm, especially a significant decrease in up direction. Moreover, the improvement rate of the three-dimensional root mean square error of positioning accuracy in light foliage area test is 86% for BeiDou navigation satellite system/global positioning system combination solutions, while the corresponding improvement rate is 82% for the heavy foliage area test.

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