Research On Error Analysis And Calibration Algorithm Of Aeromagnetic Compensation System

Aeromagnetic exploration is a detection method that uses some sensors mounted on an aircraft platform and data processing algorithms to determine whether there is a target that can cause magnetic field signal distortion in a specified area.In order to effectively detect abnormal target signals,aircraft platform magnetic interference compensation technology is one of the cores of aeromagnetic exploration.The complete aeromagnetic compensation system mainly includes three parts: data acquisition,interference compensation and data processing.And the data acquisition module composed of sensors is the foundation of the entire system.However,during the data acquisition period,the sensors are inevitably introduced measurement errors to the measurement results due to the influence of many factors.The measurement error of the sensor will be propagated through the interference compensation module,and further restrict the compensation accuracy and data analysis results.In highprecision aeromagnetic exploration,the measurement uncertainty of the sensor cannot be ignored.In light of that,this thesis focuses on the error problem in aeromagnetic compensation,starting from the two aspects of error analysis and error calibration.Aiming to clarify the transmission relationship of the sensor error in the system,and improving the aeromagnetic compensation accuracy from the perspective of reducing the impact of error.The main contents of this thesis are listed as follows:Firstly,this thesis derives the error propagation relationship from the measurement error of the sensor to the final compensation result.Based on the sensor measurement principle and error cause,the nonlinear transfer relationship between the sensor measurement uncertainty and the final compensation result is systematically derived,from the calibration process and compensation process of the classic aeromagnetic compensation Tolles-Lawson model.Furthermore,the weak process that is more sensitive to errors in the compensation algorithm is found,which can provide theoretical support for the more scientific calibration schemes.Secondly,this thesis studies on the error robust aeromagnetic compensation coefficient solution algorithm in order to solve the problem that the parameter estimation part in the compensation model is sensitive to noise.It is proposed to use the total least squares regression with its extension method,and the recursive least squares regression method to estimate the compensation model parameters.The results based on simulation data and measured data show that the algorithm can effectively suppress the influence of sensor measurement errors,and improve the robustness of the parameter estimation process to noise in aeromagnetic compensation.Thirdly,this thesis proposes three-axis flugate sensor error calibration algorithms to solve the problem that this sensor plays an important role in the aeromagnetic compensation system while its measurement accuracy is low.First of all,establish an error calibration model based on comprehensive analysis of the threeaxis fluxgate's error source.By solving the model parameters,the magnetic field value measured by the three-axis fluxgate is calibrated.What's more,a data fusion algorithm based on BP neural network is proposed to calibrate the direction cosine value which is calculated indirectly by the three-axis fluxgate measurement data.Experimental results show that both algorithms can effectively correct the measurement error of the three-axis fluxgate,and further enhance the precision of aeromagnetic interference compensation.