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

Sun, Rui; Zhang, Zixuan; Cheng, Qi; Ochieng, Washington Yotto

doi:10.1007/s10291-021-01213-z

Cited by 48 publications

(16 citation statements)

References 38 publications

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“…Additionally, when the number of visible satellites is relatively small, simply excluding the multipath/NLOS signal will deteriorate the satellite geometric distribution, which reduces the positioning accuracy or even fails to execute the positioning solution. Therefore, in future research, we will further consider reasonable multipath/NLOS processing strategies, such as optimizing the stochastic model of the observation equation [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

et al. 2022

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Due to the massive multipath effects and non-line-of-sight (NLOS) signal receptions, the accuracy and reliability of GNSS positioning solution can be severely degraded in a highly urbanized area, which has a negative impact on the performance of GNSS/INS integrated navigation. Therefore, this paper proposes a multipath/NLOS detection method based on the K-means clustering algorithm for vehicle GNSS/INS integrated positioning. It comprehensively considers different feature parameters derived from GNSS raw observations, such as the satellite-elevation angle, carrier-to-noise ratio, pseudorange residual, and pseudorange rate consistency to effectively classify GNSS signals. In view of the influence of different GNSS signals on positioning results, the K-means clustering algorithm is exploited to divide the observation data into two main categories: direct signals and indirect signals (including multipath and NLOS signals). Then, the multipath/NLOS signal is separated from the observation data. Finally, this paper uses the measured vehicle GNSS/INS observation data, including offline dataset and online dataset, to verify the accuracy of signal classification based on double-differenced pseudorange positioning. A series of experiments conducted in typical urban scenarios demonstrate that the proposed method could ameliorate the positioning accuracy significantly compared with the conventional GNSS/INS integrated navigation. After excluding GNSS outliers, the positioning accuracy of the offline dataset is improved by 16% and 85% in the horizontal and vertical directions, respectively, and the positioning accuracy of the online dataset is improved by 21% and 41% in the two directions. This method does not rely on external geographic information data and other sensors, which has better practicability and environmental adaptability.

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Section: Discussionmentioning

confidence: 99%

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

et al. 2022

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“…Various data collection methods can be incorporated into the proposed approach to obtain more variables [108,109]. In addition, for future studies, it is recommended to utilize other kinds of cross-validation techniques [110,111], machine learning methods [112][113][114][115][116][117][118][119], and optimization algorithms [120][121][122][123][124] to make a better decision about the interference of nonmotorized users and vehicle flow within a city and to develop models with high prediction accuracy in taking preventive measures for decreasing pedestrian accidents in urban environments. Also, various crash modeling and before-after safety evaluation methods can be incorporated into the proposed approaches to examine risk factors on crash rates [125][126][127][128].…”

Section: Discussionmentioning

confidence: 99%

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Habibzadeh

Ameri

Ziari

et al. 2022

Journal of Advanced Transportation

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The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming.

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“…e sensors, controllers, and controlled objects in the control loop are time-synchronized through time-stamping technology to deal with the problem of possible out-of-order data packets [38][39][40][41][42].…”

Section: System Modelmentioning

confidence: 99%

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

Zheng

2022

Computational Intelligence and Neuroscience

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At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity υ of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity ω initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle θ coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.

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

Cited by 48 publications

References 38 publications

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

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