scholarly journals Sensor Fusion INS/GNSS based on Fuzzy Logic Adaptive Error-State Kalman Filter and Unscented Kalman Filter

2020 ◽  
Author(s):  
Gabriel de Cunto
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Error State

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.

A Comparison of SLAM Prediction Densities Using the Kolmogorov Smirnov Statistic

2016 ◽  
Vol 04 (04) ◽  
pp. 245-254
Author(s):  
Akshay Rao ◽  
Wang Han ◽  
P. G. C. N. Senarathne
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Fusion Algorithm ◽  
Central Difference ◽  
Localization Algorithms ◽  
Kolmogorov Smirnov ◽  
Prediction Density ◽  
Smirnov Statistic

Accurate pose and trajectory estimates, are necessary components of autonomous robot navigation system. A wide variety of Simultaneous Localization and Mapping (SLAM) and localization algorithms have been developed by the robotics community to cater to this requirement. Some of the sensor fusion algorithms employed by SLAM and localization algorithms include the particle filter, Gaussian Particle Filter, the Extended Kalman Filter, the Unscented Kalman Filter, and the Central Difference Kalman Filter. To guarantee a rapid convergence of the state estimate to the ground truth, the prediction density of the sensor fusion algorithm must be as close to the true vehicle prediction density as possible. This paper presents a Kolmogorov–Smirnov statistic-based method to compare the prediction densities of the algorithms listed above. The algorithms are compared using simulations of noisy inputs provided to an autonomous robotic vehicle, and the obtained results are analyzed. The results are then validated using data obtained from a robot moving in controlled trajectories similar to the simulations.

scholarly journals A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

2021 ◽  
pp. 014233122110114
Author(s):  
Mostafa Osman ◽  
Mohamed W Mehrez ◽  
Mohamed A Daoud ◽  
Ahmed Hussein ◽  
Soo Jeon ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Optimization Problems ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Intelligent Vehicles ◽  
Superior Performance ◽  
Moving Horizon Estimation ◽  
Missing Measurements ◽  
Fusion Scheme

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

Crane Guidance Gesture Tracking and Recognition With Nonlinear Estimation and Fuzzy Logic

2018 ◽  
Author(s):  
Xin Wang ◽  
Chris Gordon ◽  
Edwin E. Yaz
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Kalman Filtering ◽  
Unscented Kalman Filter ◽  
Visual Sensor ◽  
Inference System ◽  
Time Dynamic ◽  
Gesture Tracking ◽  
Nonlinear Kalman Filtering ◽  
Tracking And Recognition

This paper presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. Kinect visual sensor and MYO armband sensor are jointly utilized to perform data fusion in providing more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real-time. Dynamic equations for arm gesture movement are formulated with Newton-Euler equations based on Denavit-Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gestures recognition. Hardware experiments have shown the efficacy of proposed method for crane guidance applications.

scholarly journals Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

2019 ◽  
Vol 94 ◽  
pp. 02004
Author(s):  
Dah-Jing Jwo ◽  
Shu-Ming Chang ◽  
Jen-Hsien Lai
Keyword(s):  
Fuzzy Logic ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Statistical Characteristics ◽  
Signal Quality ◽  
Time Varying ◽  
Multiple Model ◽  
Gps Navigation ◽  
Satellite Signal

A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

Sign in / Sign up

Export Citation Format

Share Document