scholarly journals Train Localization using Unscented Kalman Filter – Based Sensor Fusion

2018 ◽  
Vol 1 (2) ◽  
pp. 35-41
Author(s):  
I Faruqi ◽  
M. B. Waluya ◽  
Y. Y. Nazaruddin ◽  
T. A. Tamba ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Precision Measurement ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Experimental Results ◽  
Fusion Method ◽  
Localization Problem ◽  
Rail Systems ◽  
Fusion Methods

This paper presents an application of sensor fusion methods based on Unscented Kalman filter (UKF) technique for solving train localization problem in rail systems. The paper first reports the development of a laboratory-scale rail system simulator which is equipped with various onboard and wayside sensors that are used to detect and locate the train vehicle movements in the rail track. Due to the low precision measurement data obtained by each individual sensor, a sensor fusion method based on the UKF technique is implemented to fuse the measurement data from several sensors. Experimental results which demonstrate the effectiveness of the proposed UKF-based sensor fusion method for solving the train localization problem is also reported.

scholarly journals Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization

2020 ◽  
Vol 10 (15) ◽  
pp. 5045 ◽  
Author(s):  
Ming Lin ◽  
Byeongwoo Kim
Keyword(s):  
Kalman Filter ◽  
Real World ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Localization Problem ◽  
Simulation Results ◽  
Non Gaussian ◽  
Self Driving Cars

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

scholarly journals Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

2016 ◽  
Vol 16 (06) ◽  
pp. 1550016 ◽  
Author(s):  
Mohsen Askari ◽  
Jianchun Li ◽  
Bijan Samali
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Measurement Data ◽  
Linear Structure ◽  
Structural Identification ◽  
Input Output ◽  
Output Measurement ◽  
Highly Nonlinear

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

DATA FUSION OF ROBOT WRIST FORCES BASED ON FINGER FORCE SENSORS AND MLF NEURAL NETWORK

2005 ◽  
Vol 02 (02) ◽  
pp. 101-111
Author(s):  
LI-BIAO TONG ◽  
WEN-JUN LU ◽  
XIN HONG ◽  
TAO MEI ◽  
KE-JUN XU
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Data Fusion ◽  
Sensor Fusion ◽  
Robot Control ◽  
Experimental Results ◽  
Force Sensors ◽  
Finger Force ◽  
Fusion Methods ◽  
Robot Wrist

Quantitative analysis of wrist forces for robot grippers is an important issue for robot control and operation safety. An approach is proposed to deduce the wrist forces from distributed force sensors in the robot fingers. A multi-layer forward (MLF) neural network is designed to fuse the data from finger force sensors. The experimental results demonstrate that the maximum deducing error of the wrist forces is decreased to 4.8% from 18.7% comparing with previous sensor fusion methods.

scholarly journals GNSS Signal Processing with EKF and UKF for Stationary User Position Estimation

2021 ◽  
Vol 17 ◽  
pp. 75-80
Author(s):  
Mert Sever ◽  
Chingiz Hajiyev
Keyword(s):  
Signal Processing ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Important Process ◽  
Moving Platforms ◽  
Linearization Methods ◽  
Nonlinear Motion

Precise and accurate estimation of state vectors is an important process during position determination. In this study, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) of stationary user, state vectors defined in Earth Centered Inertial (ECI) coordinate system, accompanied by GNSS measurement data. It is aimed to make estimations with methods. EKF and UKF methods were compared with each other. In this study, the effects of nonlinear motion analysis and linearization methods on state vector estimations were investigated. Thanks to this study, estimations of the positioning information required during the specific tasks of many moving platforms have been made.

scholarly journals Multiple Road-Objects Detection and Tracking for Autonomous Driving

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Estimation Accuracy ◽  
Time Performance ◽  
Detection And Tracking ◽  
Objects Detection

In this paper, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique show how outstanding is the improvement in tracking performance compared to the use of a single device (-29% RMES with lidar and -38% RMSE with radar).

scholarly journals General Model and SOC Estimation of Battery

2021 ◽  
Vol 15 ◽  
pp. 155-163
Author(s):  
Li Meng ◽  
Haipeng Guo ◽  
Xiaowei Zhao
Keyword(s):  
Kalman Filter ◽  
General Model ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Work Conditions ◽  
Filter Method ◽  
Soc Estimation ◽  
The Difference ◽  
Kalman Filter Method ◽  
Set Up

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.

Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

2020 ◽  
pp. 095965182097552
Author(s):  
Wael Farag
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Mean Squared Error ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Root Mean Squared Error ◽  
Time Performance ◽  
Detection And Tracking ◽  
Squared Error

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).

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