Two-station cooperative precision positioning algorithm with additional baseline constraints

Traditional Precise Point Positioning (PPP) has many advantages such as high accuracy and easy operation. PPP usually uses the Kalman filtering (KF) to solve unknown parameters. However, the positioning performance depends on the accurate kinematic model and filtering initial value. The inaccurate kinematic model or initial filtering value will lead to filter performance degradation or even divergence. In order to solve this problem, this paper proposes a twostation cooperative PPP positioning method with additional baseline constraint information. The algorithm uses the direction information and length information of the baseline to modify the estimated position of the two stations. By reduces the error covariance matrix of the floating-point solution, the algorithm improves the accuracy of the floating-point solution. Further validation test based on real GPS data shows that results from baseline vector constraint PPP effectively be improved compared with the traditional PPP parameter estimation method.