scholarly journals GNSS DOPPLER VELOCITY BASED ON ADAPTIVE ROBUST KALMAN FILTERING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1005-1012
Author(s):  
H. Liu ◽  
M. Z. Xin ◽  
J. J. Wei ◽  
Y. K. Liang ◽  
F. L. Yang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Velocity Measurement ◽  
Measurement Errors ◽  
Threshold Value ◽  
Velocity Estimation ◽  
Least Square ◽  
Doppler Velocity ◽  
Factors Affecting ◽  
Robust Kalman Filter

Abstract. The main factors affecting the error of Doppler velocity measurement mainly come from the measurement errors of GNSS data, influence of different motion states on GNSS velocity measurement and the noise of different receiver types. To improve the precision of GNSS velocity estimation, an algorithm of adaptive robust Kalman filter based on the PDOP was put forward. PDOP value as well as the number of satellite in each epoch are used as a criterion in the velocity processing. While the PDOP value is greater than the threshold value, which means the observation accuracy is low, then the robust Kalman filter based on IGG – III scheme is introduced. While the PDOP value is between the threshold values, which means the observation precision is normal, adaptive factor could be determined normally, and the single-factor three-stage adaptive model is applied for Kalman filtering. If the above two conditions are not consistent, it indicates that the prediction accuracy of the local epoch satellite is high, and Kalman filtering can be directly used. Through the experiment of shipborne GNSS velocity measurement, it was proved that comparing with conventional least square, the algorithm based on the adaptive robust Kalman filtering can improve the accuracy and stability of the GNSS velocity determination.

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

2018 ◽  
Vol 233 (11) ◽  
pp. 4017-4031
Author(s):  
Kai Xiong ◽  
Chunling Wei ◽  
Haoyu Zhang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Autonomous Navigation ◽  
Adaptive Estimation ◽  
Line Of Sight ◽  
Parallel Model ◽  
Multiple Model ◽  
Filtering Algorithm ◽  
Robust Kalman Filter ◽  
Multiple Model Adaptive Estimation

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

A novel robust Kalman filter with unknown non-stationary heavy-tailed noise

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109511
Author(s):  
Hao Zhu ◽  
Guorui Zhang ◽  
Yongfu Li ◽  
Henry Leung
Keyword(s):  
Kalman Filter ◽  
Robust Kalman Filter ◽  
Heavy Tailed

The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

2014 ◽  
Vol 694 ◽  
pp. 80-84
Author(s):  
Xiao Tong Yin ◽  
Chao Qun Ma ◽  
Liang Peng Qu
Keyword(s):  
Road Traffic ◽  
Velocity Estimation ◽  
Least Square ◽  
Single Type ◽  
Estimation Model ◽  
Average Speed ◽  
Urban Road ◽  
Traffic State ◽  
Floating Car Data ◽  
Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

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