@Article{cmc.2020.011592,
AUTHOR = {Dah-Jing Jwo},
TITLE = {Complementary Kalman Filter as a Baseline Vector Estimator for  GPS-Based Attitude Determination},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {993--1014},
URL = {http://www.techscience.com/cmc/v65n2/39859},
ISSN = {1546-2226},
ABSTRACT = {The Global Positioning System (GPS) offers the interferometer for attitude 
determination by processing the carrier phase observables. By using carrier phase 
observables, the relative positioning is obtained in centimeter level. GPS interferometry 
has been firstly used in precise static relative positioning, and thereafter in kinematic 
positioning. The carrier phase differential GPS based on interferometer principles can 
solve for the antenna baseline vector, defined as the vector between the antenna 
designated master and one of the slave antennas, connected to a rigid body. Determining 
the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude 
determination. The conventional solution of the baseline vectors based on least-squares 
approach is inherently noisy, which results in the noisy attitude solutions. In this article, 
the complementary Kalman filter (CKF) is employed for solving the baseline vector in 
the attitude determination mechanism to improve the performance, where the receiversatellite double differenced observable was utilized as the measurement. By using the 
carrier phase observables, the relative positioning is obtained in centimeter level.
Employing the CKF provides several advantages, such as accuracy improvement, 
reliability enhancement, and real-time assurance. Simulation results based on the 
conventional method where the least-squares approach is involved, and the proposed 
method where the CKF is involved are compared and discussed.},
DOI = {10.32604/cmc.2020.011592}
}