Heading estimation algorithm for complex environment based on GPS single baseline

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◦.