CN111273202A - Array-based magnetic sensor compensation method - Google Patents
Array-based magnetic sensor compensation method Download PDFInfo
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- CN111273202A CN111273202A CN202010115552.2A CN202010115552A CN111273202A CN 111273202 A CN111273202 A CN 111273202A CN 202010115552 A CN202010115552 A CN 202010115552A CN 111273202 A CN111273202 A CN 111273202A
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Abstract
The invention relates to the technical field of magnetic sensor compensation, and discloses a magnetic sensor compensation method based on an array, which comprises the following steps: (1) magnetic sensor array: forming an array by using magnetic sensors, and detecting the magnetic field intensity at the same position; (2) preprocessing array data: preprocessing the output data of the magnetic sensor array; (3) establishing a compensation model: establishing a compensation model in a training and learning mode; (4) magnetic sensor array compensation: after the compensation model is established, magnetic sensor array data are obtained in real time, preprocessing is carried out on the magnetic sensor array data in the same way as training learning samples, preprocessing data of the magnetic sensor array are obtained, the magnetic sensor array preprocessing data are input into the compensation model, and a compensated result is automatically output. The invention utilizes the array information of the magnetic sensor and establishes the compensation model in a training and learning mode, thereby not only eliminating various error interferences of the magnetic sensor, but also improving the magnetic field resolution.
Description
Technical Field
The invention relates to the technical field of magnetic sensor compensation, in particular to a magnetic sensor compensation method based on an array.
Background
In the manufacturing process of the magnetic sensor, triaxial zero position, sensitivity and non-orthogonal errors can be formed, and meanwhile, surrounding soft and hard magnetic materials can also generate errors on the magnetic sensor. The traditional magnetic sensor compensation method mainly comprises a four-position method, an eight-position method, a aeromagnetic compensation method, an ellipse hypothesis method, a neural network method and the like. The four-position method and the eight-position method mainly use different position measurement mean values to replace real values; the aeromagnetic compensation method mainly utilizes a 16-order linear model to compensate the constant magnetic interference, the excitation interference and the vortex magnetic interference generated by the movement of a carrier in a geomagnetic field; the ellipse hypothesis method means that when errors exist, a magnetic field vector is changed into an ellipsoid from a spherical surface in a three-dimensional space, and a compensation coefficient is solved through the relation; the neural network method adopts a neural network to obtain an error compensation model through training, and the error is compensated.
The magnetic sensor compensation method can effectively eliminate error interference caused by non-orthogonal soft magnetic materials and hard magnetic materials in different application fields, but cannot improve the magnetic field resolution of the magnetic sensor. At present, low-cost magnetic sensors, such as anisotropic magnetoresistive sensors, are increasingly widely applied, but the low magnetic field resolution also limits the application field of the sensors.
Disclosure of Invention
In order to solve the problems, the invention provides a magnetic sensor compensation method based on an array, which utilizes the array information of a magnetic sensor and establishes a compensation model in a training and learning mode, so that various error interferences of the magnetic sensor can be eliminated, and the magnetic field resolution can be improved.
The invention discloses a magnetic sensor compensation method based on an array, which comprises the following steps:
magnetic sensor array: the magnetic sensors are used to form an array, the magnetic field intensity at the same position is detected, and the output of the magnetic sensor array is
Wherein m is 1, N is the number of samples; 1, wherein M is the number of magnetic sensors; j 1.. Q, Q is the dimension of the magnetic sensor; in addition, the magnetic sensors can be of the same type or different types, and the number of the magnetic sensors and the array space topological structure can be determined according to practical application;
preprocessing array data: because the original sensor array data may have the problems of containing a large amount of noise, redundant information, difference of different dimensional data and the like, the accuracy of the compensation model is seriously influenced, the preprocessed data are more reasonably distributed through array data preprocessing, and the accuracy of establishing the compensation model is improved; to magnetic sensor array output
After preprocessing, the magnetic sensor array is converted into
Wherein N1, N is the array dimension;
establishing a compensation model: the compensation model is established and obtained in a training and learning mode
Wherein
Preprocessing data for the magnetic sensor array, and outputting compensation y;
magnetic sensor array compensation: after a compensation model is established, magnetic sensor array data are obtained in real time, preprocessing is carried out on the magnetic sensor array data as a training learning sample, and preprocessed data of the magnetic sensor array are obtained
And input the compensation model
And automatically outputting the compensated result y.
Further, in the array data preprocessing step, the preprocessing includes filtering, denoising, feature extraction and normalization.
Further, in the step of establishing the compensation model, the acquisition of the training learning samples should take the universality and representativeness of the sample space into consideration, that is, the magnetic sensor array data should be acquired under different postures.
Further, in the step of establishing a compensation model, the real data of the training and learning is obtained through magnetic sensor measurement with higher precision than that of the magnetic sensors composing the array, or is obtained through an international geomagnetic reference model IGRF and a world geomagnetic model WMM.
Further, in the step of establishing the compensation model, an algorithm of the compensation model includes an artificial neural network.
Further, in the step of establishing the compensation model, the artificial neural network includes a radial basis function neural network, and the radial basis function neural network includes an input layer, a hidden layer, and an output layer.
The invention has the beneficial effects that:
(1) the compensation model is established by utilizing the array information of the magnetic sensor and in a training and learning mode, so that various error interferences of the magnetic sensor can be eliminated, and the magnetic field resolution can be improved;
(2) the array data preprocessing of the invention mainly comprises a filtering denoising technology, a feature extraction technology, a normalization technology and the like, so that the spatial distribution of the preprocessed data is more reasonable, and the accuracy of the compensation model is improved.
(3) When the method is used for collecting samples for training and learning, the data of the magnetic sensor array are collected under different postures, and the universality and the representativeness of a sample space are fully considered.
Drawings
FIG. 1 is a flow chart of an array magnetic sensor compensation process;
FIG. 2 is a spatial topology diagram of a magnetic sensor array;
FIG. 3 is a graph comparing total magnetic field strength of a magnetic sensor to actual values;
FIG. 4 is an enlarged view of true total field strength values;
FIG. 5 is a pre-and post-slip filter comparison graph of a first magnetic sensor;
FIG. 6 is a comparison graph of the second magnetic sensor before and after the sliding filter process;
FIG. 7 is a comparison graph before and after a sliding filter process of a third magnetic sensor;
FIG. 8 is a graph of compensation results for training learning samples;
FIG. 9 is a graph of compensation results for test samples.
Detailed Description
In order to more clearly understand the technical features, objects, and effects of the present invention, specific embodiments of the present invention will now be described. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a magnetic sensor compensation method based on an array, which can be applied to the elimination of magnetic sensor error interference and the improvement of magnetic field resolution.
To demonstrate the effectiveness of the method, the present example tested the proposed method and treated according to the process scheme shown in FIG. 1.
Three magnetic sensors HMC5883L are selected to form a magnetic sensor array, and the spatial topological configuration is shown in figure 2. The HMC5883L is a three-axis magnetic sensor with a magnetic field resolution of 0.1 mG. And the satellite navigation module is arranged on the magnetic sensor array and used for recording the geographical position information of the magnetic intensity detected by the magnetic sensor array.
The magnetic sensor array is used for collecting magnetic field intensity data of different postures and different positions to serve as training learning samples, and longitude and latitude information of satellite navigation is recorded at the same time. And inputting the longitude and latitude information into a world geomagnetic model WMM2015 to obtain magnetic field intensity data serving as a true value of a training and learning sample. In the embodiment, the total magnetic field intensity value measured by the magnetic sensor is used as a compensation target, and the invention can also compensate for a single axis of the magnetic sensor, and at the moment, the axis of the magnetic sensor needs to be aligned with the real value obtained by the WMM 2015.
Fig. 3 and 4 are graphs of the total magnetic field strength output by three sensors of a magnetic sensor array compared to the true total magnetic field strength obtained by WMM 2015. As shown in fig. 3, due to the influence of various disturbances, the total magnetic field intensity detected by the magnetic sensor is relatively noisy, and cannot reflect the real situation. As shown in the enlarged view of the true total field strength values in fig. 4, the maximum change is only 0.0054mG, while the resolution of the HMC5883L is only 0.1mG, and the change of the true total field strength values is much lower than the resolution of the HMC5883L, and the magnetic sensor cannot detect the change of the field strength in this range.
The total magnetic field intensity of the magnetic sensor is selected as a characteristic value, 50-order sliding filtering is carried out on the characteristic value to remove part of noise, and the filtering is carried out before and after the filtering, for example, as shown in FIGS. 5-7. And the data was directly divided by 1000 to normalize the data to the [0,1] range.
And selecting a radial basis function neural network in the artificial neural network as a compensation model. The topology structure of the radial basis function neural network is a three-layer forward network: an input layer, a hidden layer, and an output layer. The output expression of the radial basis neuron is shown as:
a＝f(||W-P||b)＝radbas(||W-P||b) (1)
in the formula, | | W-P | | is Euclidean distance, b is a threshold value and is used for adjusting the sensitivity of neurons, and radbas is a kernel function of hidden layer neurons.
The kernel function and the Euclidean distance expression are respectively as follows:
where n is the argument of the Gaussian function, R is the number of input vector elements, w1,iAs a weight vector, piIs the network input quantity.
The verification test is total to 1900 sample data, the samples are divided into training learning samples and testing samples, 1300 samples are selected as the training learning samples, and the remaining 600 samples are used as the testing samples. Use trainingTraining learning samples, training learning the network with the real values obtained from WMM2015, the training learning result is shown in FIG. 8, and the mean square error is 7.68994 × 10-14。
The trained network model is tested by using the test samples, the test result is shown in fig. 9, after compensation, the total magnetic field intensity value measured by the magnetic sensor array can reflect the change of the real total magnetic field intensity, and the mean square error is 5.3627 × 10-13。
In summary, the array-based magnetic sensor compensation method provided by the invention can eliminate the error interference of the magnetic sensor and improve the magnetic field resolution, so that the effectiveness of the invention is proved.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A method of array-based compensation of magnetic sensors, comprising the steps of:
magnetic sensor array: the magnetic sensors are used to form an array, the magnetic field intensity at the same position is detected, and the output of the magnetic sensor array is
Wherein m is 1, N is the number of samples; 1, wherein M is the number of magnetic sensors; j 1.. Q, Q is the dimension of the magnetic sensor;
preprocessing array data: to magnetic sensor array output
Preprocessing the magnetic sensor array, and converting the preprocessed magnetic sensor array into a magnetic sensor array
Wherein N1, N is the array dimension;
establishing a compensation model: the compensation model is established and obtained in a training and learning mode
Wherein
Preprocessing data for the magnetic sensor array, and outputting compensation y;
magnetic sensor array compensation: after a compensation model is established, magnetic sensor array data are obtained in real time, preprocessing is carried out on the magnetic sensor array data as a training learning sample, and preprocessed data of the magnetic sensor array are obtained
And input the compensation model
And automatically outputting the compensated result y.
2. The array-based magnetic sensor compensation method of claim 1, wherein in the array data preprocessing step, the preprocessing comprises filtering denoising, feature extraction and normalization.
3. The array-based magnetic sensor compensation method of claim 1, wherein in the step of establishing the compensation model, the training learning samples are collected in consideration of the sample space universality and representativeness, that is, the magnetic sensor array data are collected at different postures.
4. The array-based magnetic sensor compensation method of claim 1, wherein in the step of establishing the compensation model, the actual data of the training and learning is obtained by a higher-precision magnetic sensor measurement than the magnetic sensors composing the array, or by an international geomagnetic reference model IGRF and a world geomagnetic model WMM.
5. The method of claim 1, wherein the step of creating the compensation model comprises an algorithm of an artificial neural network.
6. The method of claim 5, wherein in the step of modeling the compensation, the artificial neural network comprises a radial basis function neural network, the radial basis function neural network comprising an input layer, a hidden layer, and an output layer.
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