Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm

A BP neural network and genetic algorithm technology, applied in the field of magnetometer calibration, can solve the problems of increasing computer processing complexity, increasing constraints, and not having real-time performance

Active Publication Date: 2014-06-18
SOUTHEAST UNIV
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However, most methods need to transform the magnetometer error model into an ellipsoid model, and then use some methods to fit the ellipsoid to obtain the parameters of the ellipsoid model. It reduces the processing complexity of the c

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  • Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm
  • Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm
  • Magnetometer correcting method with optimized and modified BP neural network based on genetic algorithm

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[0037] The present invention will be further described below in conjunction with the accompanying drawings.

[0038] The magnetometer correction method based on the genetic algorithm optimization BP neural network provided by the present invention proposes the optimal real-time estimation of the parameters of the error model of the magnetometer based on the genetic algorithm optimization BP neural network, and then the effective compensation for the magnetometer output error method. The invention can be applied to error correction of heading angle in an inertial integrated navigation and positioning system composed of an inertial measurement unit (IMU) and a magnetometer (or electronic compass). Since the basic method of the BP neural network algorithm is the gradient descent method, it cannot overcome the defect that it may fall into a local minimum solution. In order to solve this shortcoming of the BP neural network, the present invention introduces a genetic algorithm (GA...

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Abstract

The invention discloses a magnetometer correcting method with an optimized and modified BP neural network based on a genetic algorithm. An error model of a magnetometer is converted into the three-layer modified BP neural network (namely the parameters of the error model of the magnetometer serve as weights of the BP neural network for optimized estimation), then the weights of the modified BP neural network are optimized in real time through the genetic algorithm, and therefore correction and compensation of the magnetometer are achieved; the structural principle of the modified BP neural network is shown in the picture 2. The correction method can effectively improve the accuracy of the magnetometer, further achieves high-accuracy positioning and navigation, lowers the processing complexity of a computer, obtains a good effect, has the advantages of being high in practicability, real-time performance and reliability, low in cost, high in accuracy, simple in calculation process, high in stability and the like, and can effectively improve the accuracy of the course angle of a navigation system.

Description

technical field [0001] The invention relates to a method for correcting a magnetometer based on a genetic algorithm to optimize and improve a BP neural network, which can correct the magnetometer in a navigation system. Background technique [0002] In the application of the navigation system, whether the navigation system can provide the accurate attitude and heading angle information of the carrier is very important. For an inertial measurement unit (IMU) composed of only accelerometers and gyroscopes, the accuracy of the attitude and heading parameters output by the IMU cannot meet the requirements of some navigation systems due to the errors and drift of the accelerometers and gyroscopes themselves. The attitude and heading measurement system composed of inertial measurement unit (IMU) and magnetometer can improve the accuracy of attitude angle and heading angle. Magnetometer technology is widely used abroad, but there are still some problems in magnetic field compensat...

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Application Information

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IPC IPC(8): G01V13/00G01C25/00G06N3/02G06N3/12
Inventor 陈熙源吕才平黄浩乾方琳
Owner SOUTHEAST UNIV
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