Unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on feedforward network

A feedforward network and magnetic interference technology, applied in the field of UAV aeromagnetic full-axis gradient magnetic interference compensation, can solve the problems of algorithm generalization performance limitation, inverse matrix instability, poor maneuverability of UAV, etc. Effects of Axial Gradient Magnetic Interference Compensation, Extending Generalization Performance, Avoiding Overfitting Problems

Active Publication Date: 2017-06-13
INST OF ELECTRONICS CHINESE ACAD OF SCI
View PDF7 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The compensation method based on the least squares algorithm is easy to cause overfitting, and the generalization performance of the algorithm is limited. There is a problem of complex collinearity in the traditional aeromagnetic compensation algorithm model. When using the least squa

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on feedforward network
  • Unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on feedforward network
  • Unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on feedforward network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0056] The invention provides a method for compensating UAV aeromagnetic full-axis gradient magnetic interference based on a feedforward network. The stable network parameters are obtained by training the feedforward network, which is used as a compensation network to compensate the data of the exploration flight to obtain the full Axial gradient data, effectively avoiding the multicollinearity problem in the least squares algorithm, and avoiding the overfitting problem by introducing a regularization factor, and also expanding the generalization performance of the feedforward network, without the need for the drone to implement roll , pitch, yaw and other large maneuvering flight actions, adapt to the flight mode of the UAV, and realize the compensation of the UAV's full-axis gradient magnetic interference.

[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on a feedforward network. The method includes the steps that calibrated flight data is subjected to preprocessing operation of wavelet transform and normalization, normalized feedforward network input vectors are transmitted into the established feedforward network to be trained, a regularized cost function is adopted, stable feedforward network parameters are iteratively obtained through an error back-propagating algorithm, and a finally convergent network serves as a compensation network; exploration flight data is subjected to identical preprocessing operation and transmitted to the obtained compensation network to be calculated, estimation of an airplane disturbing magnetic field is obtained, and magnetic disturbance compensation is obtained. The stable network parameters are obtained by training the feedforward network, the network serves as the compensation network for compensating for the exploration flight data, the problems of inverse matrix instability and over-fitting in a least squares algorithm are effectively avoided, the generalization performance of the feedforward network is expanded, and unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation is achieved.

Description

technical field [0001] The invention belongs to the field of geophysical aeromagnetic exploration, and relates to a method for compensating unmanned aerial vehicle aeromagnetic full-axis gradient magnetic interference based on a feedforward network. Background technique [0002] As an important means of airborne geophysical prospecting, aeromagnetic prospecting has been widely used in the field of geophysics. The traditional aeromagnetic prospecting platform is mainly manned and manned. In the past ten years, with the development of UAV technology, UAVs have been It is widely used in the field of airborne magnetic prospecting. Compared with manned aircraft, unmanned aerial vehicles have significant advantages such as low cost, high efficiency, and safety. However, due to the characteristics of small aircraft size and short baseline between probes, unmanned aerial vehicles interfere with the magnetic field in the acquired aeromagnetic gradient data. The impact is very signif...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01V3/38
CPCG01V3/38
Inventor 张群英吴佩霖陈路昭费春娇许鑫朱万华方广有
Owner INST OF ELECTRONICS CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products