Unmanned aerial vehicle integrated navigation method and system based on unscented Kalman filtering

A technology of unscented Kalman and integrated navigation, applied in radio wave measurement system, satellite radio beacon positioning system, navigation and other directions, can solve the problem of loss of accuracy and achieve the effect of reducing requirements, reducing calculation amount and high precision

Pending Publication Date: 2021-04-20
西安因诺航空科技有限公司
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AI Technical Summary

Problems solved by technology

[0003] In realizing the self-navigation and positioning of UAVs, the Kalman filter algorithm is commonly used. This filter algorithm performs very well when the processing system is a linear system. For nonlinear systems, the system needs to be linearized, which leads to the extended Kalman filter. (EKF), the Jacobian matrix of the system needs to be obtained during the linearization process, which will lose part of the accuracy

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  • Unmanned aerial vehicle integrated navigation method and system based on unscented Kalman filtering
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  • Unmanned aerial vehicle integrated navigation method and system based on unscented Kalman filtering

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Embodiment

[0218] The invention does not need to linearize the state equation, and the calculation amount is less than that of the EKF combined navigation algorithm with the same state variable. A typical embodiment will be described below in combination with the aforementioned steps.

[0219] The initial attitude of the UAV (roll angle roll and pitch angle pitch) can be calculated by the static adder value:

[0220] The initial heading angle of the UAV can be calculated by the magnetometer:

[0221] Then the four elements of UAV attitude can be determined through the initial attitude

[0222] [q 0 q 1 q 2 q 3 ]=[1 0 0 0]

[0223] The initial speed of the UAV can be obtained through GNSS:

[0224] [v x v y v z ]=[0 0 0]

[0225] The initial position of the UAV can be obtained through GNSS:

[0226] [p x p y p z ]=[0 0 0]

[0227] The zero bias of the UAV gyroscope and accelerometer can be set to zero:

[0228]

[0229] So we can get the initial state x of the i...

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Abstract

The invention discloses an unmanned aerial vehicle integrated navigation method and system based on unscented Kalman filtering, and the method can achieve the estimation of the position, speed and attitude of an unmanned aerial vehicle through an integrated navigation algorithm. Compared with an integrated navigation algorithm based on Kalman filtering and expansion Kalman filtering of the same dimension, the integrated navigation algorithm provided by the invention has higher precision; the integrated navigation algorithm provided by the invention can be used as another standby algorithm in a navigation system to perform redundancy design, so that the stability of the system is improved; according to the invention, a sequential filtering mode is adopted, the calculated amount in an embedded system can be reduced, and the requirement for configuration is lowered.

Description

【Technical field】 [0001] The invention belongs to the technical field of unmanned aerial vehicles and relates to a positioning and navigation method for unmanned aerial vehicles, in particular to an unscented Kalman filter-based integrated navigation method and system for unmanned aerial vehicles. 【Background technique】 [0002] UAVs have great development prospects in the civilian field, especially in the fields of aerial photography, oil pipeline inspection, power inspection, rescue, and investigation. In the process of controlling the drone to fly, the drone needs to know its own position accurately in real time, and in order to know its own position, the drone is equipped with GPS, IMU, ultrasonic, vision, barometer and other sensors. It's all about realizing your own navigation and positioning. [0003] In realizing the self-navigation and positioning of UAVs, the commonly used is the Kalman filter algorithm. This filter algorithm performs very well when the processing...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G01C21/18G01C21/16G01C21/00G01S19/39G01S19/45G01S19/47G06F17/18
Inventor 李定涌
Owner 西安因诺航空科技有限公司
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