查看更多>>摘要：? 2022The paper's main goal is to accomplish a high accuracy of yaw/heading by Machine Learning approach when the motion range of vehicle/device calibration is limited. The nonlinear Random Forest (RF) Regression with proper training has a high potential to deal with the magnetometer uncertainty before calibration and during iron distortion cases. The proposed solution solely requires the magnetometer without other sensor's support. A Pan Tilt Unit-C46 (PTU-C46) with high precise positioning was used as a reference heading value to label the corresponding magnetic features in the learning model. The proposed approach helps yaw estimation to be carried out under harsh conditions, which resolve many difficulties in orientation tracking since the magnetometer is susceptible to hard iron and soft iron in the environment. In addition, many mechanical devices work only within the specific range and waste their dynamic motion around two axes or more just for calibration. Thus, the research focuses on the level rotation calibration around Z-axis within the restricted range of motion for practical application. The experiment was carried out using a low-cost platform equipped with Micro-Electro-Mechanical System (MEMS) sensors as gyroscope, accelerometer, and magnetometer. The 9 Degree of Freedom (DoF) Madgwick was implemented into the Microcontroller to compare with the proposed model. The sensor fusion can track the yaw value after the level calibration despite various error conduction. The RF model accomplishes a superior result with more stability and more minor error. Under iron disturbance or calibration absence, the ML model still maintains the good tracking command with maximum Mean Square Error of about 0.3°, while the Madgwick is unsuccessful in heading measurement due to huge error in these circumstances.
查看更多>>摘要：? 2022 Elsevier LtdThe pseudorange bias inconsistencies are notably originating primarily from the individual chip shape distortions in the ranging signals, these distortions cause different shifts of the correlator's tracking point for receivers, as a result giving rise to a different pseudorange bias for each pseudorange observation. The different bias should be properly dealt with in the process of satellite clock offset estimation when receivers of mixed types are used. In this work, using observations from 140 international global navigation satellite system (GNSS) service stations, the pseudorange bias was calculated, and the effect of inconsistent biases on satellite clock offset estimation from different receiver networks was investigated. The inconsistencies between the GPS satellites were ranging from ?6 ns to 4 ns. For GALILEO and BDS, the biases are significant, ranging from ?20 ns to 40 ns and ?60 ns to 40 ns, respectively. The biases of the individual satellites are receiver-related and statistically stable from day to day. The influence of pseudorange bias was strikingly noticeable, and can improve the accuracy of the estimated clock offset by several ps for GPS; and by more than 15 ps for GALILEO and BDS after considering bias inconsistencies. Furthermore, owing to the pseudorange bias correction, the convergence and positioning performance have improved significantly, with an improvement of more than 12 % for the static precise point position (PPP), and more than 10 % for the kinematic PPP.
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