scholarly journals Vehicle Speed Estimation Using GPS/RISS (Reduced Inertial Sensor System)

Author(s):  
T. O'Kane ◽  
J.V. Ringwood
Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4501 ◽  
Author(s):  
Qing Zhang ◽  
Lianwu Guan ◽  
Dexin Xu

Velocity information from the odometer is the key information in a reduced inertial sensor system (RISS), and is prone to noise corruption. In order to improve the navigation accuracy and reliability of a 3D RISS, a method based on a tracking differentiator (TD) filter was proposed to track odometer velocity and acceleration. With the TD filter, an input signal and its differential signal are estimated fast and accurately to avoid the noise amplification that is brought by the conventional differential method. The TD filter does not depend on an object model, and has less computational complexity. Moreover, the filter phase lag is decreased by the prediction process with the differential signal of the TD filter. In this study, the numerical simulation experiments indicate that the TD filter can achieve a better performance on random noises and outliers than traditional numerical differentiation. The effectiveness of the TD filter on a 3D RISS is demonstrated using a group of offline data that were obtained from an actual vehicle experiment. We conclude that the TD filter can not only quickly and correctly filter velocity and estimate acceleration from the odometer velocity for a 3D RISS, but can also improve the reliability of the 3D RISS.


2010 ◽  
Vol 46 (4) ◽  
pp. 298 ◽  
Author(s):  
Z. Shen ◽  
J. Georgy ◽  
M.J. Korenberg ◽  
A. Noureldin

2018 ◽  
Vol 18 (14) ◽  
pp. 5662-5673 ◽  
Author(s):  
Lu Wang ◽  
Aboelmagd Noureldin ◽  
Umar Iqbal ◽  
Abdalla M. Osman

2012 ◽  
Vol 44 (6) ◽  
pp. 652-656 ◽  
Author(s):  
M. J. McCracken ◽  
J. Kramer ◽  
K. G. Keegan ◽  
M. Lopes ◽  
D. A. Wilson ◽  
...  

2021 ◽  
pp. 1-1
Author(s):  
Slobodan Djukanovic ◽  
Jiri Matas ◽  
Tuomas Virtanen

Sign in / Sign up

Export Citation Format

Share Document