Shitu Luo

Nonlinear temperature compensation of fluxgate magnetometers with a least-squares support vector machine

Fluxgate magnetometers are widely used for magnetic field measurement. However, their accuracy is influenced by temperature. In this paper, a new method was proposed to compensate the temperature drift of fluxgate magnetometers, in which a least-squares support vector machine (LSSVM) is utilized. The compensation performance was analyzed by simulation, which shows that the LSSVM has better performance and less training time than backpropagation and radical basis function neural networks. The temperature characteristics of a DM fluxgate magnetometer were measured with a temperature experiment box. Forty-five measured data under different magnetic fields and temperatures were obtained and divided into 36 training data and nine test data. The training data were used to obtain the parameters of the LSSVM model, and the compensation performance of the LSSVM model was verified by the test data. Experimental results show that the temperature drift of magnetometer is reduced from 109.3 to 3.3 nT after compensation, which suggests that this compensation method is effective for the accuracy improvement of fluxgate magnetometers.

A New Calibration Method of Three Axis Magnetometer With Nonlinearity Suppression

Nonlinearity is a prominent limitation to the calibration performance of vector magnetometers. A new calibration model is proposed to suppress the nonlinearity of magnetometers and improve calibration performance, in which the nonlinearity coefficients of scale factors are considered. The experimental system mainly consists of a three-axis fluxgate magnetometer (MAG3300), a 2D nonmagnetic rotation equipment, and a proton magnetometer (CZM-3), in which the nonmagnetic rotation equipment is used to change the position of three-axis fluxgate magnetometer, and the scalar value of magnetic field is obtained with the proton magnetometer and considered to be the true value. The principle of this new calibration method is analyzed, and the calibration procedures are introduced. Experimental results show that after nonlinearity suppression, the root-mean-square error of calibration can be reduced by three times. It suggests an effective way to improve the calibration performance of three-axis magnetometers.

An improved moment-preserving auto threshold image segmentation algorithm

When moment-preserving auto threshold algorithm is used to segment image whose histogram is unimodal or monotonic function, there is serious background interference, and the segmentation accuracy is greatly affected by the size variance of the object, so this paper puts forward an improved moment-preserving auto threshold algorithm. Aiming at the original algorithms shortage of neglecting image details, this algorithm takes advantage of the feature that the grey level difference between object borders and adjacent background is great while the difference among pixels in an object region or background region is small, and then adds gradient adjustment based on object edge pixels to moment-preserving auto thresholding, in order to look after both the whole and the details of image in segmentation result. This algorithm needs no iteration or search, and it is fast enough to satisfy the demand for real time. As simulation results show, this algorithm can segment object image effectively.

Calibration of three-axis magnetometer diversionary error based on equipment and LMS adaptive algorithm

Bias of magnetometers and target total value is obtained precisely via calibration equipment. Then, real time calibration weight matrix is obtained using LMS adaptive algorithm. It is proved through experiment that bias is obtained with good stability and accuracy compared with parameter estimation; after calibration, diversionary error is reduced from 33nT to 4nT. Furthermore, diversionary error is calibrated well using a proton magnetometer without rotating the magnetometer over one circle. Experiment results show that it not only reduces diversionary error fluctuation but also eliminates system error compared with other methods.

The Application of Geomagnetic Spatial Frequency Spectrum in Geomagnetic Localization

In order to reduce the bad impact of the geomagnetic signal’s error on geomagnetic localization, this paper pay much attention on studying the character of geomagnetic signal and error using method of spatial frequency spectrum analysis. First, Fourier analysis method is used to study frequency feature of geomagnetic signal and error, an important result is get that in Fourier domain the character of geomagnetic signal is almost the same with signal which contains noise. Secondly, based on the above result, Time-Frequency Matching (TFM) method is used to carry out geomagnetic localization. Finally, this paper compares TFM with Contour Constraint Matching method in application of geomagnetic localization, and the results show that when the geomagnetic signal contains white noise, the localization error of TFM is smaller. So the analysis of Geomagnetic Spatial Frequency Spectrum has great importance in geomagnetic localization application.

Design of an optical ultrasonic signal testing system using LabVIEW

Optical methods are used in ultrasonic testing to overcome the limitation of conventional contact transducers in bad situations and have become a hot branch in ultrasonic testing methods. However, the sensitivity in optical methods is lower than that of contact transducers and the use of laser devices brings more noises, which make the signal to be tested more complex. In view of the characteristics of the signal in optical testing, wavelet transform and complex analytic wavelet envelope detection are introduced to extract the signal and its features. Then an optical ultrasonic signal testing virtual instrument system based on LabVIEW is designed, integrating signal acquisition, digital filtering, noise suppression, power spectrum analysis, peak extraction and envelope detection as a whole. Compared with those heavy and expensive equipments, the friendly interface, convenient operation and high extendibility prove its flexibility and value. Finally, experiments are carried out to verify the reliability and precision of the system. Results show a relatively error below 1% in multiple measures.