Journal of Geodesy and Geoinformation Science ›› 2021, Vol. 4 ›› Issue (4): 74-83.doi: 10.11947/j.JGGS.2021.0406

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Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

Li LIU1(),Qingfeng XU2(),Hui GU1,Lei ZHOU1,Zhenfu LIU1,Lili CAO1   

  1. 1. Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
    2. Shanghai General Satellite Navigation Co., Ltd., Shanghai 200040, China
  • Received:2020-01-20 Accepted:2020-05-16 Online:2021-12-20 Published:2021-12-30
  • Contact: Qingfeng XU;
  • About author:Li LIU (1978—), female, senior engineer, engaged in develop satellite T.T.&C. and payload electronics.E-mail:
  • Supported by:
    National key special projects for major scientific instruments and equipment development(2017YFF0107400)


The magnetic compensation of aeromagnetic survey is an important calibration work, which has a great impact on the accuracy of measurement. In an aeromagnetic survey flight, measurement data consists of diurnal variation, aircraft maneuver interference field, and geomagnetic field. In this paper, appropriate physical features and the modular feedforward neural network (MFNN) with Levenberg-Marquard (LM) back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data. LM algorithm is a kind of least square estimation algorithm of nonlinear parameters. It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method. LM algorithm’s computing efficiency is high and fitting error is very low. The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON (T-L) model with the linear least square (LS) solution by fitting experiments with five different aeromagnetic surveys’ data.

Key words: modular feedforward neural network; aeromagnetic compensation; LM back propagation algorithm; regularization