Abstract-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 meters of positioning accuracy is the minimum requirement since the vehicle has to keep in a driving lane that usually wider than 3 meters. However, the skyscrapers in highly-urbanized cities such as Tokyo and Hong Kong, dramatically deteriorate GPS localization performance, leading more than 50 meters 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-GNSS (global navigation satellite system). It provides an excellent opportunity for positioning algorithm developer of GPS sensor. More satellites in the sky implies 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 meter lateral positioning error in middle urban canyons.
Combined multi-global navigation satellite system (GNSS) signals are capable of improving satellite availability for both standalone and differential positioning. Currently, the potential for highaccuracy automobile navigation using GNSS is constrained by severe multipath and poor satellite geometry, especially in "urban canyons" in large cities. With differential GNSS (D-GNSS) positioning, inconvenient system time differences can be removed by reference-station processing, allowing a user's receiver position to be accurately calculated using four or more visible satellite signals. Therefore, in the future, we can filter numerous multi-GNSS measurements based on their quality in order to enhance the positioning performance in urban environments. In this paper, we present several methods that use multi-GNSS to filter signals from satellites containing severe multipath errors. In addition, we select single-frequency code-based D-GNSS because it has significant potential due to its low cost and robustness. The first method uses the measured carrier-to-noise ratio (C/N0). When only reflected signals are received in dense urban areas, the C/N0 will decrease by more than 6 dB-Hz, with the exception of high-elevation satellites. Thus, by comparing the measured and expected C/N0 at various elevation angles, we may be able to detect the presence of severe multipath signals. The second method involves using the error residual from the Receiver Autonomous Integrity Monitoring (RAIM), which is a well-known technique for checking the quality of measurements. Signals having severe multipath effects result in significant deterioration of measurements. In addition to the above methods, we introduce several points that should be noted in order to improve D-GNSS. To evaluate our proposed method, we perform positioning tests using a car in an urban environment. Differential positioning was used for multi-GNSS with GPS, QZSS, BeiDou, and GLONASS. We present an evaluation of each technique that is used to mitigate multipath errors. The results show that our proposed techniques effectively improved the horizontal accuracy. In addition, the accuracy of the horizontal errors was improved by more than H. Tokura et al. 8650% in two different environments.
The global navigation satellite system (GNSS) can potentially provide centimeter-level positioning using real-time kinematic (RTK) positioning. However, in static positioning, such as for surveying, receivers easily receive multipath signals continuously. Our goal was to improve the performance of instantaneous RTK-GNSS in multipath environments. Two conventional satellite selection methods based on the idea of correctly removing multipath signals, which allows more reliable solutions, were evaluated in this study. The first method is based on using signal-to-noise ratio (SNR) observations to mask measurements having degraded quality. In the second method, a mask of sky obstacles is generated using a fisheye view lens camera to detect non-line-of-sight (NLOS) signals. In this study, several static tests were performed to evaluate these conventional methods. The results show that both methods can efficiently improve availability. Furthermore, the performance when using a fisheye view mask was slightly better than that when using the SNR method, in particular for situations where a powerful reflected signal by NLOS was received. Based on these results, an improved SNR-based satellite selection method that uses the SNR fluctuation magnitude for a certain period is proposed. The results show that this method effectively improves the performance as compared with the conventional SNR mask.
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