GNSS (Global Navigation Satellite System)-based Kalman relative positioning method

Abstract

The invention discloses a GNSS (Global Navigation Satellite System)-based Kalman relative positioning method. The method comprises steps: 1, a double difference carrier phase is built; 2, relative positioning of a triple difference carrier phase is carried out; 3, a Kalman filter dynamic model is improved adaptively; 4, the Kalman filter state is updated; 5, satellite geometrical configuration changes are processed; 6, Kalman filter measurement is updated; 7, integer ambiguity is solved; and 8, relative position information is outputted. The method is better adaptive to relative positioning of a variably accelerated motion carrier, the precision of ambiguity floating point solution and the integer ambiguity fixing success rate are improved, the initialization time of a relative positioning system is shortened, and the real-time performance and the reliability of relative positioning are enhanced.

Description

technical field [0001] The invention belongs to the technical field of satellite navigation, and more specifically relates to a relative positioning method based on satellite navigation. Background technique [0002] Global Navigation Satellite System (GNSS, Global Navigation Satellite System) is a comprehensive system that uses radio signals emitted by artificial earth satellites for navigation. The satellite navigation system that can provide navigation services on a global scale can provide users with global, all-weather, Real-time 3D navigation service. GNSS-based relative positioning is to install two sets of GNSS receivers and antenna equipment on two carriers respectively, receive GNSS measurement information at the same time, and transmit the GNSS measurement information received by one carrier to the other carrier in real time through the communication data link. In the computer equipment, the relative position information between the two carriers can be obtained b...

AI Technical Summary

Problems solved by technology

[0005] (1) The commonly used Kalman filter adopts a uniform motion dynamic model. By setting a large dynamic noise to weaken the influence of the dynamic model on the navigation system under various uncertain motion conditions, only the measurement information is used for filter estimation , for uniform acceleration and variable acceleration motion carriers, it is difficult to build a precise dynamic model, which in turn brings a large relative positioning floating-point solution error

[0006] (2) The commonly used Kalman filtering method must rely on external sensors, such as inertial navigation data, when constructing a dynamic model of non-uniform motion. It is difficult to perform real-time high-precision relative position

[0007] (3) The commonly used Kalman filter does not make full use of the carrier phase measurement information, and does not construct a three-difference carrier phase measurement model to assist the dynamic model, and the accuracy of the ambiguity floating-point solution is low, resulting in a long fixed time for the entire ambiguity and a fixed success rate Low, reducing the real-time and reliability of the relative positioning system

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