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Image and geomagnetism combined aircraft attitude resolving system and method

An aircraft and geomagnetic technology, applied in the field of aircraft attitude calculation system, can solve the problems of low gyro accuracy, gyro drift, large attitude, error, etc., and achieve the effect of strong anti-interference and broad-spectrum adaptability

Pending Publication Date: 2021-10-26
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Gyroscopes have the following problems: (1) traditional gyroscopes are expensive; (2) common MEMS gyroscopes have low precision; (3) gyroscope drift will cause large attitude errors in a short period of time; (4) when rolling the aircraft In the state of high speed, the range and accuracy of the gyro are difficult to meet the standard
[0005] GPS satellite assistance has the following problems: Although the attitude of the aircraft can be determined in real time by performing differential processing on the carrier phase information measured by multiple GPS antennas installed on the aircraft, there are some problems that the satellite signals cannot be obtained in the actual process. At this time, the GPS information cannot be used to calculate the attitude of the aircraft

Method used

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  • Image and geomagnetism combined aircraft attitude resolving system and method
  • Image and geomagnetism combined aircraft attitude resolving system and method
  • Image and geomagnetism combined aircraft attitude resolving system and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0151] Use some pictures and roll angles in the ImageNet training library to train the BP neural network, and use the trained neural network to identify other pictures in the training library to obtain the roll angle.

[0152] Among them, the neuron output of the input layer in the training neural network is:

[0153] The input to the hidden layer neurons is:

[0154] The output of hidden layer neurons is:

[0155] The activation function of the hidden layer is log-sigmoid,

[0156] The output of the neurons in the output layer is:

[0157] During training, the weights are updated as follows:

[0158]

[0159] In the process of using the BP neural network to identify the picture to obtain the roll angle, follow the steps below:

[0160] S11. Preprocessing the image to reduce the size of the image so that the image can be represented by a pixel array;

[0161] S12. Use the neural network to identify the rotation angle of the image.

[0162] In step S11, the fol...

Embodiment 2

[0179] After 600 times of training, the neural network in Example 1 was carried on an aircraft to perform a half-physical simulation test of flight attitude calculation.

[0180] Among them, the roll angle γ of the aircraft is output by the neural network;

[0181] Obtain the geomagnetic component of the aircraft under the current environmental magnetic field by using the geomagnetic sensor, and convert it into the magnetic component of the aircraft coordinates The relationship between is:

[0182]

[0183] Substituting the roll angle γ into the magnetic component In the relational formula, by solving it, the yaw angle ψ and pitch angle θ of the aircraft can be obtained.

experiment example 1

[0189] Count the roll angle errors identified in Example 1 and Comparative Example 1, and the results are as follows Figure 4 As shown, it can be seen from the figure that the method in Embodiment 1 has obvious advantages compared with the traditional EKF algorithm, and the error can always be kept within a small range.

[0190] The comparison between the roll angles identified in Example 1 and Comparative Example 1 and the real values ​​recorded in the training database is shown in the figure Figure 5 As shown, it can be seen from the figure that the BP neural network in Embodiment 1 has a very good effect, and it can identify the roll angle very well from the beginning, and no convergence time is required.

[0191] Statistical embodiment 2 and the flight attitude that identification obtains in comparative example 2, wherein pitch angle identification result is as follows Figure 6 As shown, the roll angle identification result is as follows Figure 7 As shown, the yaw an...

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Abstract

The invention discloses an image and geomagnetism combined aircraft attitude resolving system and method, and the method comprises the steps: recognizing a shot image based on a neural network to obtain a rotation angle, obtaining a geomagnetic component according to geomagnetic information, and synthesizing the image rotation angle and the geomagnetic component to obtain an aircraft attitude. The image and geomagnetism combined aircraft attitude resolving system and method provided by the invention have the advantages of high resolving accuracy, strong anti-interference performance and the like.

Description

technical field [0001] The invention relates to an aircraft attitude calculation system and method, in particular to an aircraft attitude calculation system and method combining images and geomagnetism, and belongs to the field of aircraft attitude control. Background technique [0002] In modern society, the navigation accuracy and reliability of aircraft are increasingly required, and obtaining accurate attitude information is a prerequisite for the normal flight of the aircraft. [0003] Traditional aircraft attitude calculations usually use gyroscopes and GPS satellites to estimate the attitude of the aircraft. [0004] Gyroscopes have the following problems: (1) traditional gyroscopes are expensive; (2) common MEMS gyroscopes have low precision; (3) gyroscope drift will cause large attitude errors in a short period of time; (4) when rolling the aircraft In the state of high speed, the range and accuracy of the gyro are difficult to meet the standard. [0005] GPS sate...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20G06N3/04G06N3/08
CPCG01C21/005G01C21/20G06N3/084G06N3/045
Inventor 王辉刘灿林德福王伟王江宋韬何绍溟孙昕宾域如
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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