scholarly journals Analyzing the Unscented Kalman Filter Robustness for Orbit Determination through Global Positioning System Signals

2013 ◽  
Vol 5 (4) ◽  
pp. 395-408 ◽  
Author(s):  
Paula C. P. M. Pardal ◽  
Helio Koiti Kuga ◽  
Rodolpho Vilhena De Moraes
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Positioning System ◽  
Global Positioning

scholarly journals The Use of Kalman Filter Techniques for Ship Track Estimation

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Dynamic Systems ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Positioning System ◽  
Global Positioning ◽  
Systems Identification ◽  
Simulation Results ◽  
Marine Vessels

Unscented Kalman Filter (UKF) is a technique used in non-linear applications and dynamic systems identification (e.g. tracking marine vessels and ships) that require state and parameter estimation. This paper studies Kalman Filter (KF) based techniques for tracking ships using Global Positioning System (GPS) data. The present work proposes to exploit information from GPS sensors in order to track a ship in real-time. The absence and presence problem of a ship is handled by a applying KF theory to analyze GPS coordinates and compare current marine vessel routes to previously recorded ones. To study tracking performance, the system was implemented in C++ and simulation results demonstrate the feasibility and high accuracy of the proposed tracking method

scholarly journals Research on a mixed prediction method to vehicle integrated navigation systems

2019 ◽  
Vol 16 (6) ◽  
pp. 172988141988525
Author(s):  
Di Zhao ◽  
Huaming Qian ◽  
Dingjie Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Radial Basis Function ◽  
Global Positioning System ◽  
Basis Function ◽  
Unscented Kalman Filter ◽  
Positioning System ◽  
Global Positioning ◽  
Radial Basis

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.

Combined Tracking Strategy Based on Unscented Kalman Filter for Global Positioning System L2C CM/CL Signal

2015 ◽  
Vol 65 (5) ◽  
pp. 395 ◽  
Author(s):  
Xuefen Zhu ◽  
Fei Shen ◽  
Jianfeng Chen ◽  
Yang Yang ◽  
Dongrui Yang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Dominant Role ◽  
Tracking Error ◽  
Doppler Frequency ◽  
Positioning System ◽  
Tracking Loop ◽  
Signal Tracking ◽  
Global Positioning

<p>In a global positioning system receiver, the tracking algorithm plays a dominant role since the code delay and Doppler frequency shift need to be accurately estimated as well as their variation over time need to be continuously updated. Combine unscented Kalman filter (UKF) with CM/CL signal to improve the signal tracking precision is proposed. It allow weighting assignment between CM code and CL code incoming signal, masked by a mass of noise, and to describe a UKF tracking loop aiming at decreasing numerical errors. UKF here involves state and measuring equations which calculate absolute offsets to adjust initial code and carrier phase then dramatically decrease the tracking error. In particular, the algorithm is implemented in both open space and jammed environment to highlight the advantages of tracking approach, by comparing single code and combined code, UKF and EKF tracking loop. It proves that signal tracking based on UKF, with low energy dissipation as well as high precision, is particularly appealing for a software receiver implementation.</p>

scholarly journals A Robust Method of Vehicle Stability Accurate Measurement Using GPS and INS

2015 ◽  
Vol 15 (6) ◽  
pp. 294-303 ◽  
Author(s):  
Zhibin Miao ◽  
Hongtian Zhang ◽  
Jinzhu Zhang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Vehicle Stability ◽  
Positioning System ◽  
Robust Method ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Global Positioning

Abstract With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) is a very practical method to get high-precision measurement data. Usually, the Kalman filter is used to fuse the data from GPS and INS. In this paper, a robust method is used to measure vehicle sideslip angle and yaw rate, which are two important parameters for vehicle stability. First, a four-wheel vehicle dynamic model is introduced, based on sideslip angle and yaw rate. Second, a double level Kalman filter is established to fuse the data from Global Positioning System and Inertial Navigation System. Then, this method is simulated on a sample vehicle, using Carsim software to test the sideslip angle and yaw rate. Finally, a real experiment is made to verify the advantage of this approach. The experimental results showed the merits of this method of measurement and estimation, and the approach can meet the design requirements of the vehicle stability controller.

Sign in / Sign up

Export Citation Format

Share Document