Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Heading estimation algorithm for complex environment based on GPS single baseline

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2295292,
        author={Zengshan  Tian and Shuai  Lu and Wei  He and Mu  Zhou and Zhi  Jiang},
        title={Heading estimation algorithm for complex environment based on GPS single baseline},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={gps heading estimation kalman filtering carrier phase},
        doi={10.4108/eai.27-8-2020.2295292}
    }
    
  • Zengshan Tian
    Shuai Lu
    Wei He
    Mu Zhou
    Zhi Jiang
    Year: 2020
    Heading estimation algorithm for complex environment based on GPS single baseline
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2295292
Zengshan Tian1, Shuai Lu1,*, Wei He1, Mu Zhou1, Zhi Jiang1
  • 1: Chongqing University of Posts and Telecommunications
*Contact email: lushuai.139@163.com

Abstract

With the rapid development of unmanned technology, the high-precision heading angle determines the accuracy of this unmanned automatic navigation. However, traditional least square method is used to solve the vehicle’s heading angle will jump in the complex terrain environment. Therefore, we propose an unmanned vehicle heading estimation algorithm based on single GPS baseline. First, we establish GPS single residual and double residual observation models to eliminate measurement errors, and combine code phase and carrier phase to form ambiguity combination. Second, in order to eliminate the effects of instability and noise that may be brought by complex environments, this paper proposes model equations based on the prediction and update equations of Kalman filter, the Kalman filtering performs real-time status update on the single residual ambiguity. Finally, the integer ambiguity is searched to find the heading angle of the vehicle. In addition, we performed an algorithm performance test in the actual unmanned vehicle operating environment. The test results shown that the estimated error of the heading when the vehicle is traveling straight and turning is within 1.5◦.