In SATCOM On-The-Move (SOTM) systems, an accurate pointing from moving antennas towards the communication satellite is the key to a stable signal tracking. To realize a reliable SOTM operation under GNSS-challenged environments with limited size, weight and power (SWaP), a low-cost two-antenna GNSS/INS attitude and position determination system is proposed in this paper, with ultra-short baseline ranging from 0.2 to 0.5 m, and consumer-grade navigation components. Specifically, INS dead reckoning aiding is introduced into the GNSS single-epoch baseline search, reducing the original spherical traversal to a one-dimension searching. A new ambiguity resolution acceptance test method is also developed to increase the success rate of ambiguity resolution. In the process of sensor fusion, an Innovation Pre-filtered Extended Kalman Filter (IPEKF) is proposed to deal with GNSS outliers resulting from noisy observations and multipath interference. At last, static and kinematic experiments were performed on a prototype SOTM terminal with 0.267 m baseline. Corresponding results verified a heading estimation accuracy of 0.3°, and in GNSS outage situations, the system was able to output usable attitude determination results within approximately 40 s. Applications of the proposed system could be further extended to attitude estimations of unmanned aerial vehicles (UAV) and ocean-going ships.


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    Title :

    A Low-Cost GNSS/INS Attitude and Position Determination System with Ultra-Short Baseline for SATCOM On-The-Move Terminals


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Sun, Jiadong (editor) / Yang, Changfeng (editor) / Guo, Shuren (editor) / Fan, Peirong (author) / Li, Wenyi (author) / Cui, Xiaowei (author) / Lu, Mingquan (author)

    Conference:

    China Satellite Navigation Conference ; 2018 ; Harbin, China May 23, 2018 - May 25, 2018



    Publication date :

    2018-05-04


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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