GNSS dynamic Kalman filtering method in cluster autonomous cooperation

Abstract

The invention discloses a GNSS dynamic Kalman filtering method in cluster autonomous cooperation. The method comprises the following steps: calculating a double-difference observation value, calculating an observation equation, calculating a double-difference pseudorange observation value, calculating a double-difference pseudorange observation equation, calculating a double-difference observationequation based on a pseudorange and a carrier phase, and listing the double-difference observation equation and Kalman filtering calculation. According to the invention, the receiver clock error andthe satellite clock error are eliminated by calculating the double-difference observation value. Satellites with high elevation angles are called as the first choice of reference satellites. The accuracy of each double-difference observation value is ensured. Pseudo-range measurement values corresponding to different stations and between satellites form a double-difference pseudo-range, and the corresponding double-difference pseudo-range is smoothed by utilizing a double-difference carrier phase, so that the measurement noise of a double-difference pseudo-range observation value is reduced, and the smoothed or filtered double-difference pseudo-range observation value has the advantages of relatively low measurement noise and no integer ambiguity.

Description

technical field [0001] The invention relates to the technical field of cluster autonomous coordination, in particular to a GNSS dynamic Kalman filter method in cluster autonomous coordination. Background technique [0002] In the future informationized battlefield, drones will be more and more widely used to perform various lethal combat tasks. Under the prospect of highly informationized battlefields, the combat mode of drones will also change. The autonomous combat mode has been transformed into a combat mode in which the fleet is against the fleet and the fleet is attacking ground / surface targets. This is the UAV swarm coordinated operations. The UAV swarm forms a scale advantage and has excellent battlefield survivability and mission completion capabilities. It can be used to complete tasks such as coordinated search, coordinated interference, coordinated attack, coordinated inspection / strike, and cluster confrontation in complex confrontation environments; [0003] Amo...

AI Technical Summary

Problems solved by technology

[0003] Among them, the key issues that need to be solved include large-scale UAV management and control, multi-UAV autonomous formation flight, swarm perception and situation sharing, swarm penetration and attack, swarm combat mission control station, etc., which require the use of Satellite navigation system, with the successive completion of various satellite navigation systems, more and more satellite signals are available in the sky. In recent years, the receivers on the market have the characteristics of multi-system and multi-frequency points, and usually can receive more than 200 channels at the same time. Satellite signal; in practical application, in the process of multi-antenna receiver receiving satellite signal, the signal passes through the carrier, and the carrier often has to pass through complex road sections, the signal strength changes drastically or even disappears, and the receiver has clock error and satellite clock error , RTK positioning cannot be performed, so it is necessary to perform observation calculations on double differences, and there is noise in double difference observations, and it is difficult to ensure the accuracy of each double difference observation value. Therefore, the present invention proposes a GNSS dynamic Kalman filter in cluster autonomous collaboration method to solve the problems existing in the prior art

Images