GNSS/IMU/Odometer based Train Positioning

2020 ◽  
Author(s):  
Qing Li ◽  
Robert Weber
Keyword(s):  
Kalman Filter ◽  
Urban Areas ◽  
Moving Average ◽  
Accurate Determination ◽  
Integrated System ◽  
Single Frequency ◽  
Integration Algorithm ◽  
Speed Sensor ◽  
Train Positioning ◽  
Train Tracks

<p>Usually train positioning is realized via counting wheel rotations (Odometer), and correcting at fixed locations known as balises. A balise is an electronic beacon or transponder placed between the rails of a railway as part of an automatic train protection (ATP) system. Balises constitute an integral part of the European Train Control System, where they serve as “beacons” giving the exact location of a train. Unfortunately, balises are expensive sensors which need to be placed over about 250 000 km of train tracks in Europe.</p><p>Therefore, recently tremendous efforts aim on the development of satellite-based techniques in combination with further sensors to ensure precise train positioning. A fusion of GNSS receiver and Inertial Navigation Unit (IMU) observations processed within a Kalman Filter proved to be one of potential optimal solutions for train traction vehicles positioning.</p><p>Today several hundreds of trains in Austria are equipped with a single-frequency GPS/GLONASS unit. However, when the GNSS signal fails (e.g. tunnels and urban areas), we expect an outage or at least a limited positioning quality. To yet ensure availability of a reliable trajectory in these areas, the GNSS sensor is complemented by a strapdown IMU platform and a wheel speed sensor (odometer).</p><p>In this study a filtering algorithm based on the fusion of three sensors GPS, IMU and odometer is presented, which enables a reliable train positioning performance in post-processing. Odometer data are counts of impulses, which relate the wheel’s circumference to the velocity and the distance traveled by the train. This odometer data provides non-holonomic constraints as one-dimensional velocity updates and complements the basic IMU/GPS navigation system. These updates improve the velocity and attitude estimates of the train at high update rates while GPS data is used to provide accurate determination in position with low rates. In case of GNSS outages, the integrated system can switch to IMU/odometer mode. Using the exponentially weighted moving average method to estimate of measurement noise for odometer velocity helps to construct measurement covariance matrices. In the presented examples an IMU device, a GPS receiver and an Odometer provide the data input for the loosely coupled Kalman Filter integration algorithm. The quality of our solution was tested against trajectories obtained with the software iXCOM-CMD (iMAR) as reference.</p>

Improved exponential weighted moving average based measurement noise estimation for strapdown inertial navigation system/doppler velocity log integrated system

2020 ◽  
pp. 1-21
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Moving Average ◽  
Integrated System ◽  
Measurement Noise ◽  
Noise Estimation ◽  
Doppler Velocity ◽  
Forgetting Factor ◽  
Exponential Weighted Moving Average ◽  
Weighted Moving Average

Abstract The strapdown inertial navigation system (SINS) with integrated Doppler velocity log (DVL) is widely utilised in underwater navigation. In the complex underwater environment, however, the DVL information may be corrupted, and as a result the accuracy of the Kalman filter in the SINS/DVL integrated system degrades. To solve this, an adaptive Kalman filter (AKF) with measurement noise estimator to provide noise statistical characteristics is generally applied. However, existing methods like moving windows (MW) and exponential weighted moving average (EWMA) cannot adapt to a dynamic environment, which results in unsatisfactory noise estimation performance. Moreover, the forgetting factor has to be determined empirically. Therefore, this paper proposes an improved EWMA (IEWMA) method with adaptive forgetting factor for measurement noise estimation. First, the model for a SINS/DVL integrated system is established, then the MW and EWMA based measurement noise estimators are illustrated. Subsequently, the proposed IEWMA method which is adaptive to the various environments without experience is introduced. Finally, simulation and vehicle tests are conducted to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms the MW and EWMA methods in terms of measurement noise estimation and navigation accuracy.

Performance evaluation of real-time tightly-coupled GNSS PPP/MEMS-based inertial integration using an improved robust adaptive Kalman filter

2020 ◽  
Vol 14 (4) ◽  
pp. 413-430
Author(s):  
Abdelsatar Elmezayen ◽  
Ahmed El-Rabbany
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Integrated System ◽  
Adaptive Kalman Filter ◽  
Integration Algorithm ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
The Real ◽  
Tightly Coupled ◽  
Robust Adaptive

AbstractTypically, the extended Kalman filter (EKF) is used for tightly-coupled (TC) integration of multi-constellation GNSS PPP and micro-electro-mechanical system (MEMS) inertial navigation system (INS) to provide precise positioning, velocity, and attitude solutions for ground vehicles. However, the obtained solution will generally be affected by both of the GNSS measurement outliers and the inaccurate modeling of the system dynamic. In this paper, an improved robust adaptive Kalman filter (IRKF) is adopted and used to overcome the effect of the measurement outliers and dynamic model errors on the obtained integrated solution. A real-time IRKF-based TC GPS+Galileo PPP/MEMS-based INS integration algorithm is developed to provide precise positioning and attitude solutions. The pre-saved real-time orbit and clock products from the Centre National d’Etudes Spatials (CNES) are used to simulate the real-time scenario. The performance of the real-time IRKF-based TC GNSS PPP/INS integrated system is assessed under open sky environment, and both of simulated partial and complete GNSS outages through two ground vehicular field trials. It is shown that the real-time TC GNSS PPP/INS integration through the IRKF achieves centimeter-level positioning accuracy under open sky environments and decimeter-level positioning accuracy under GNSS outages that range from 10 to 60 seconds. In addition, the use of IRKF improves the positioning accuracy and enhances the convergence of the integrated solution in comparison with the EKF. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052°, 0.048°, and 0.165° for pitch, roll, and azimuth angles, respectively. This represents improvement of 44 %, 48 %, and 36 % for the pitch, roll, and azimuth angles, respectively, in comparison with the EKF-based counterpart.

A Rapid Computation Method for Kalman Filtering in Vehicular SINS/GPS Integrated System

2012 ◽  
Vol 182-183 ◽  
pp. 541-545 ◽  
Author(s):  
Qi Ju Zhu ◽  
Gong Min Yan ◽  
Peng Xiang Yang ◽  
Yong Yuan Qin
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Kalman Filtering ◽  
Integrated System ◽  
Computation Method ◽  
System Matrix ◽  
Computational Burden ◽  
Reduced Dimensions ◽  
Classic Method

A new rapid computation method for Kalman filtering is proposed. In this method, the prediction of state covariance matrix is expanded directly rather than computing by a looping program. Sequential filtering for measurement update is also applied. Furthermore, the subsidiary elements in system matrix are set to zero and a reduced-dimensions sub-optimal Kalman filter is presented. The proposed method greatly decreases computational burden and it is only 6.59% of the classic method. In the end, a vehicular test is carried out to prove the feasibility of the filtering.

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

2018 ◽  
Vol 72 (3) ◽  
pp. 741-758 ◽  
Author(s):  
W.I. Liu ◽  
Zhixiong Li ◽  
Zhichao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Laser Scanning ◽  
Inertial Navigation System ◽  
Laser Scanner ◽  
Inertial Navigation ◽  
Integration Algorithm ◽  
Biased Estimation ◽  
State Estimations ◽  
Estimation And Filtering

A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

1994 ◽  
Vol 116 (3) ◽  
pp. 550-553 ◽  
Author(s):  
Chung-Wen Chen ◽  
Jen-Kuang Huang
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Finite Number ◽  
Discrete Time ◽  
Stochastic System ◽  
Moving Average ◽  
Ar Model ◽  
Numerical Example ◽  
Measurement Noises ◽  
Time Invariant

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

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