CN113701618B - Eddy current sensor data processing method and system based on Kalman filtering - Google Patents

Abstract

The invention discloses a Kalman filtering-based eddy current sensor data processing method and a Kalman filtering-based eddy current sensor data processing system, wherein the method comprises the following steps: step S1, digital detection is carried out on a voltage signal output by an eddy current sensor, and a high-frequency carrier signal in the voltage signal is eliminated, so that an actually required measurement voltage signal is obtained; step S2, carrying out phase detection on the voltage signal to determine the positive value and the negative value of the voltage signal; and step S3, carrying out fixed-point operation by using a Kalman filter according to the measured voltage signal and the positive and negative values, and carrying out filtering treatment on random noise. The method can meet the requirements of engineering projects on speed and bottom signal processing, simultaneously can reduce occupied resources, flexibly improves the functions of the system, and can realize the resolution of 100 nm.

Description

Eddy current sensor data processing method and system based on Kalman filtering

Technical field

The invention relates to the technical field of eddy current sensor detection, and in particular to an eddy current sensor data processing method and system based on Kalman filtering.

Background technique

The digital eddy current sensor uses the FPGA digital output signal as the square wave excitation source to realize the digitization of the carrier source; based on the FPGA on-chip programming, the digital detection, phase detection and digital filtering modules of the sensor signal are designed to complete the sensor signal Digital processing has gradually replaced the traditional amplitude modulated eddy current displacement sensor with the advantages of simplifying the circuit and reducing power consumption.

In related technology, a digital eddy current displacement sensor design based on an amplitude modulated eddy current displacement sensor is proposed. The digital output signal of the field programmable gate array (FPGA) is used to design the carrier signal of the sensor, and the measurement is performed through the software design of the FPGA. The output signal of the circuit undergoes data processing. The external interference suffered by the sensor itself and the noise interference caused by hardware circuits such as A/D conversion will bring certain disturbances to the voltage measured by the detection and measurement module. Therefore, digital filtering needs to be used to filter out the noise interference. This design uses a sliding average filtering method. This filtering method (treats N consecutive sample values as a queue, the length of the queue is fixed to N, each time a new data is sampled, it is placed at the end of the queue and thrown away It turns out that the primary data at the head of the queue (first in, first out principle), the N data in the queue are arithmetic averaged, and a new filtering result is obtained), which has a good inhibitory effect on periodic interference and has high smoothness. However, it has the disadvantages of low sensitivity, poor suppression of occasional pulse interference, difficulty in eliminating sampling value deviations caused by pulse interference, not suitable for occasions with severe pulse interference, and waste of RAM.

Contents of the invention

The present invention aims to solve one of the technical problems in the related art, at least to a certain extent.

To this end, the first purpose of the present invention is to propose an eddy current sensor data processing method based on Kalman filtering.

The second object of the present invention is to propose an eddy current sensor data processing system based on Kalman filtering.

The third object of the present invention is to provide an electronic device.

The fourth object of the present invention is to provide a non-transitory computer-readable storage medium.

In order to achieve the above object, the first embodiment of the present invention proposes an eddy current sensor data processing method based on Kalman filtering, which includes the following steps: Step S1, digitally detect the voltage signal output by the eddy current sensor, and eliminate the interference in the voltage signal. The high-frequency carrier signal is used to obtain the actually required measured voltage signal; step S2, perform phase detection on the voltage signal to determine the positive and negative values of the voltage signal; step S3, based on the measured voltage signal and the Positive and negative values, use the Kalman filter to perform fixed-point operations to filter random noise.

The eddy current sensor data processing method based on Kalman filtering in the embodiment of the present invention can not only meet the requirements for speed and underlying signal processing in engineering projects, but also reduce the occupied resources and flexibly improve the functions of the system. The most important thing is The accuracy can be improved and a resolution of 100nm can be achieved, which is significantly higher than the accuracy of the existing moving average filtering method in processing eddy current sensor data.

In addition, the eddy current sensor data processing method based on Kalman filtering according to the above embodiments of the present invention may also have the following additional technical features:

Further, in one embodiment of the present invention, the Kalman filter includes a time update equation and a measurement update equation, wherein the time update equation is responsible for estimating the values of the current state variables and error covariance estimates forward in time, so that A prior estimate is constructed for the next time state, and the measurement update equation is responsible for feedback, combining the prior estimate with the new measured variables to construct an improved posterior estimate.

Further, in one embodiment of the present invention, the time update equation is:

in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, Q is the process excitation noise covariance matrix;

The state update equation is:

Among them, K _k is the Kalman gain, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> Posterior covariance estimate, I is the identity matrix.

In order to achieve the above object, the second embodiment of the present invention proposes an eddy current sensor data processing system based on Kalman filtering, including: a digital detection module for digitally detecting the voltage signal output by the eddy current sensor and eliminating the voltage signal. The high-frequency carrier signal in the signal is used to obtain the actual required measurement voltage signal; the phase detection module is used to perform phase detection on the voltage signal to determine the positive and negative values of the voltage signal; the digital filtering module is used to determine the positive and negative values of the voltage signal according to the required The measured voltage signal and the positive and negative values are measured, and the Kalman filter is used to perform fixed-point operation to filter the random noise.

The eddy current sensor data processing system based on Kalman filtering in the embodiment of the present invention can not only meet the requirements for speed and underlying signal processing in engineering projects, but can also flexibly improve the functions of the system, and most importantly, can improve the accuracy. It can achieve a resolution of 100nm and is significantly more accurate than the existing moving average filtering method in processing eddy current sensor data.

In addition, the eddy current sensor data processing system based on Kalman filtering according to the above embodiments of the present invention may also have the following additional technical features:

Further, in one embodiment of the present invention, the Kalman filter is implemented using FPGA, where fixed-point arithmetic is used in the FPGA, and the fixed-point decimal is set according to the actual situation.

Further, in one embodiment of the present invention, the Kalman filter includes a time update equation and a measurement update equation, wherein the time update equation is responsible for estimating the values of the current state variables and error covariance estimates forward in time, so that Constructing a priori estimates for the next time state, the measurement update equation is responsible for feedback, combining the prior estimates with new measured variables to construct an improved posterior estimate.

Optionally, in one embodiment of the present invention, the time update equation is:

in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, Q is the process excitation noise covariance matrix;

The state update equation is:

Among them, K _k is the Kalman gain at the current moment, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> is the prior state estimate, P _k is the posterior covariance estimate, and I is the identity matrix.

In order to achieve the above object, a third embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program , to implement the above-mentioned eddy current sensor data processing system based on Kalman filtering.

In order to achieve the above object, a fourth embodiment of the present invention proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Kalman filter-based method is implemented. Eddy current sensor data processing system.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

Figure 1 is a flow chart of an eddy current sensor data processing method based on Kalman filtering according to one embodiment of the present invention;

Figure 2 is a specific processing flow chart of an eddy current sensor data processing method based on Kalman filtering according to an embodiment of the present invention;

Figure 3 is a schematic diagram of the time update and measurement update process according to an embodiment of the present invention;

Figure 4 is a schematic diagram of the implementation principle of the FPGA-based Kalman filter algorithm according to one embodiment of the present invention;

Figure 5 is a schematic structural diagram of an eddy current sensor data processing system based on Kalman filtering according to an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and are not to be construed as limiting the present invention.

The eddy current sensor data processing method and system based on Kalman filtering proposed according to the embodiments of the present invention will be described below with reference to the accompanying drawings. First, the eddy current sensor data processing method based on Kalman filtering proposed according to the embodiments of the present invention will be described with reference to the accompanying drawings.

Figure 1 is a flow chart of an eddy current sensor data processing method based on Kalman filtering according to an embodiment of the present invention.

As shown in Figure 1, the eddy current sensor data processing method based on Kalman filtering includes the following steps:

In step S1, the voltage signal output by the eddy current sensor is digitally detected, the high-frequency carrier signal in the voltage signal is eliminated, and the actual required measurement voltage signal is obtained.

Specifically, as shown in Figure 2, the digital detection adopts the voltage-doubling envelope detection mode for digital design. The working principle of the voltage-doubling envelope detection is to calculate the difference between the two envelopes by obtaining the upper and lower envelopes of the entire measurement signal. value, the high-frequency carrier signal in the voltage signal can be eliminated and the actual required measurement voltage signal can be obtained, thereby achieving high-precision and high-precision voltage measurement.

In step S2, phase detection is performed on the voltage signal to determine the positive and negative values of the voltage signal.

Specifically, the digital detection in step S1 cannot determine the phase information of the signal, so a phase detection module needs to be designed to determine the positive and negative values of the voltage signal. In differential measurement, the maximum displacement output signals of the rotor in the two directions at the equilibrium position differ by 180°. The main function of phase detection is to determine whether the extreme value appears in the first half or the second half of the processing unit. According to the situation, the output voltage can be set to the maximum value minus the minimum value to output a positive voltage.

In step S3, based on the measured voltage signal and positive and negative values, the Kalman filter is used to perform fixed-point operation to filter the random noise.

Because the external interference of the eddy current sensor itself and the noise interference caused by hardware circuits such as A/D conversion will bring certain disturbances to the voltage measured by the detection and measurement module, it is necessary to use digital filtering to filter out the noise interference. The present invention The digital filtering in the embodiment uses Kalman filtering, and the FPGA is used to implement the Kalman filter. The operating frequency of the FPGA is consistent with the A/D sampling frequency, and the FPGA can continuously process each collected signal. In the FPGA in the embodiment of the present invention Fixed-point arithmetic is used, and the fixed-point decimal is set according to the actual situation.

Furthermore, the principle of the Kalman filter is to use the minimum mean square error as the best estimation criterion, adopt the state space model of signal and noise, and use the estimated value at the previous moment and the observed value at the current moment to update the estimate of the state variable. To obtain the estimated value at the current moment, the algorithm makes an estimate that satisfies the minimum mean square error for the signal to be processed based on the established system equation and observation equation.

The specific derivation formula is as follows:

Kalman filter is used to estimate the state variables of discrete time processes. This discrete process time is described by the following discrete stochastic difference equation:

x _k ＝Ax _k-1 +Bu _k-1 +w _k (1)

_zk ＝ _Hxk + _vk (2)

x _k is the state estimate, A is the state transition matrix, x _k-1 is the state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable, the random signal w _k , v _k is the process excitation noise and Observation noise obeys the following distribution: Q and R are the process excitation noise covariance matrix and the observation noise covariance matrix respectively.

Bundle (prior state estimate),/> (posterior state estimate), while defining the error:

Among them, e _k is the posterior error, is the prior error.

Define covariance:

P _k =E[e _k e _k ^T ] (6)

in, is the prior covariance estimate, E is the mathematical expectation, and P _k is the posterior covariance estimate.

Then construct the expression of Kalman filter,

is the posterior state estimate,/> It is a priori state estimation, where K is called Kalman gain, which plays the role of a weight, z _k is the measurement vector, and H is the conversion matrix from state variables to measurements.

The filter estimates the state of the process at a certain moment and then obtains feedback in the form of (noisy) measured variables. Therefore, the Kalman filter can be divided into two parts: the time update equation and the measurement update equation. The time update equation is responsible for extrapolating the values of the current state variables and error covariance estimates forward in time in order to construct a priori estimates for the next time state. The measurement update equation is responsible for feedback—that is, it combines the prior estimate with the new measured variables to construct an improved posterior estimate.

The time update equation is as follows:

in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, and Q is the process excitation noise covariance matrix.

Pay attention here is an estimator, which is not the same thing as x _k in the discrete time difference equation mentioned earlier.

The status update equation is as follows:

K _k is the Kalman gain at the current moment, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> is the prior state estimate, P _k is the posterior covariance estimate, and I is the identity matrix.

Formula (11) is obtained when the control variable U is 0, and K _k is determined by using the minimum P _k . The relationship between the time update equation and the state update equation is shown in Figure 3.

Furthermore, the Kalman filter directly uses mathematical calculations to filter the signal, and there are no complex operations similar to Fourier transform calculations. The idea is simple and the speed is better than other filtering methods. In addition, it uses the minimum average Square error estimate, the error value will be very small. The principle of implementing the Kalman filter on FPGA is shown in Figure 4. The most important part of the Kalman filter lies in the five filtering equations that make up it. The filtering equation is composed of four simple arithmetic operations: addition, subtraction, multiplication and division. In this way, the design of the Kalman filter becomes a combination of four arithmetic operations, and when designing the addition and subtraction of the Kalman filter equation, it is necessary to convert the various parameters and register values to the IEEE 754 standard to improve the measurement resolution. force.

Next, it should be noted that the FPGA comes with a floating-point operation IP core, but it requires several clock cycles to output data. The built-in floating-point operation IP core uses the IEEE-754 standard to define floating-point operations, and has high accuracy. However, it consumes a lot of resources. The embodiment of the present invention changes the floating-point operation in the FPGA to a fixed-point operation. The fixed-point decimal means that the position of the decimal point is designed by those skilled in the art according to the actual situation. Therefore, no additional storage space is needed to store the decimal point.

There are two key points in the calculation of fixed-point decimals, one is the use of operation symbols, and the other is the interception of intermediate results. Fixed-point decimals need to be quantified first when calculating. Reflected in RTL, they are left shift and right shift. Right shift is divided into arithmetic right shift and logical right shift. Logical right shift does not consider the sign bit. Arithmetic right shift calculation is The sign bit is taken into account, so the two's complement operation must use arithmetic right shift. After quantization, the calculation is equivalent to treating it as an integer, but at this time this binary segment is already a fixed-point decimal representation, because the position of the decimal point is set by itself, the high 8 bits are the integer part, and the low 8 bits are the decimal part. What should be noted at this time is that the result of the multiplication calculation needs to be shifted to the right by 8 bits to ensure that the decimal point is aligned.

Furthermore, in one embodiment of the present invention, a signal amplification module is also included. After processing the noise using the Kalman filter, the optimized voltage signal is amplified to facilitate subsequent applications.

In summary, the eddy current sensor data processing method based on Kalman filtering proposed by the embodiment of the present invention has the following advantages:

The accuracy and speed are improved, and at the same time, it can be processed inside the FPGA, avoiding the hardware circuit structure, reducing the interference of the external environment, and the distortion and zero drift caused by the design of hardware circuit parameters;

The disadvantages of performing floating-point operations in FPGA are large delays, large resource usage, and more clock cycles are required to complete an operation. After changing to fixed-point operations, fewer logic units are occupied and fewer clock cycles are required. Just one operation can be completed;

The method of using FPGA to implement the Kalman filter can not only meet the requirements for speed and underlying signal processing in engineering projects, but also flexibly improve the functions of the system. The most important thing is that it can improve the accuracy and achieve a resolution of 100nm. Compared with The accuracy of existing moving average filtering methods in processing eddy current sensor data is significantly improved.

Next, the eddy current sensor data processing system based on Kalman filtering proposed according to the embodiment of the present invention is described with reference to the accompanying drawings.

Figure 5 is a schematic structural diagram of an eddy current sensor data processing system based on Kalman filtering according to an embodiment of the present invention.

As shown in FIG. 5 , the system 10 includes: a digital detection module 100 , a phase detection module 200 and a digital filtering module 300 .

Among them, the digital detection module 100 is used to digitally detect the voltage signal output by the eddy current sensor, eliminate the high-frequency carrier signal in the voltage signal, and obtain the actual required measurement voltage signal. The phase detection module 200 is used to perform phase detection on the voltage signal to determine the positive and negative values of the voltage signal. The digital filtering module 300 is used to filter random noise by using the Kalman filter to perform fixed-point operations based on the measured voltage signal and positive and negative values.

Further, in one embodiment of the present invention, the Kalman filter is implemented using FPGA, in which fixed-point arithmetic is used in the FPGA, and the fixed-point decimal is set according to the actual situation.

Further, in one embodiment of the present invention, the Kalman filter includes a time update equation and a measurement update equation, wherein the time update equation is responsible for estimating the values of the current state variables and error covariance estimates forward in time in order to provide the next time The state constructs a priori estimates, and the measurement update equation is responsible for feedback, combining the prior estimates with new measured variables to construct an improved posterior estimate.

Further, in one embodiment of the present invention, the time update equation is:

in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, Q is the process excitation noise covariance matrix;

The status update equation is:

Among them, K _k is the Kalman gain at the current moment, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> is the prior state estimate, P _k is the posterior covariance estimate, and I is the identity matrix.

It should be noted that the foregoing explanations focusing on the embodiments of the eddy current sensor data processing method based on Kalman filtering are also applicable to the eddy current sensor data processing system based on Kalman filtering in the embodiments of the present invention. The implementation principles are similar and will not be used here. Again.

The eddy current sensor data processing system based on Kalman filtering proposed according to the embodiment of the present invention has the following advantages:

The accuracy and speed are improved, and at the same time, it can be processed inside the FPGA, avoiding the hardware circuit structure, reducing the interference of the external environment, and the distortion and zero drift caused by the design of hardware circuit parameters;

The disadvantages of performing floating-point operations in FPGA are large delays, large resource usage, and more clock cycles are required to complete an operation. After changing to fixed-point operations, fewer logic units are occupied and fewer clock cycles are required. Just one operation can be completed;

The method of using FPGA to implement the Kalman filter can not only meet the requirements for speed and underlying signal processing in engineering projects, but also flexibly improve the functions of the system. The most important thing is that it can improve the accuracy and achieve a resolution of 100nm. Compared with The accuracy of existing moving average filtering methods in processing eddy current sensor data is significantly improved.

In order to implement the above embodiments, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above embodiments are implemented. Eddy current sensor data processing method based on Kalman filtering.

In order to implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor, the eddy current sensor data based on Kalman filtering is implemented as in the previous embodiments. Approach.

In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the invention. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and should not be construed as limitations of the present invention. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present invention. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (4)

1. An eddy current sensor data processing method based on Kalman filtering, characterized by including the following steps: Step S1, digitally detect the voltage signal output by the eddy current sensor. The digital detection adopts the double voltage envelope detection mode for digital design. By obtaining the upper and lower envelopes of the entire measurement signal, the difference between the two envelopes is calculated to eliminate The high-frequency carrier signal in the voltage signal obtains the actual required measurement voltage signal; Step S2: Perform phase detection on the voltage signal to determine whether the extreme value appears in the first half or the second half of the processing unit to determine the positive and negative values of the voltage signal; Step S3, use the Kalman filter to perform fixed-point calculations to filter random noise according to the measured voltage signal and the positive and negative values; Use FPGA to implement the Kalman filter, where fixed-point arithmetic is used in the FPGA, and the fixed-point decimal is set according to the actual situation; The Kalman filter includes a time update equation and a measurement update equation, wherein the time update equation is responsible for estimating the values of the current state variables and error covariance estimates forward in time in order to construct a priori estimates for the next time state, the The measurement update equation is responsible for feedback, combining prior estimates with new measured variables to construct improved posterior estimates; The time update equation is: in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, Q is the process excitation noise covariance matrix, and P _k-1 is the posterior covariance estimate at the previous moment; The state update equation is: Among them, K _k is the Kalman gain at the current moment, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> is the prior state estimate, P _k is the posterior covariance estimate, and I is the identity matrix. 2. An eddy current sensor data processing system based on Kalman filtering, which is characterized by including: The digital detection module is used to digitally detect the voltage signal output by the eddy current sensor. The digital detection adopts the voltage doubler envelope detection mode for digital design. By obtaining the upper and lower envelopes of the entire measurement signal, the difference between the two envelopes is calculated. value, eliminate the high-frequency carrier signal in the voltage signal, and obtain the actual required measurement voltage signal; A phase detection module, used to perform phase detection on the voltage signal, and determine whether the extreme value appears in the first half or the second half of the processing unit, so as to determine the positive and negative values of the voltage signal; A digital filtering module, configured to use a Kalman filter to perform fixed-point operations to filter random noise based on the measured voltage signal and the positive and negative values; Use FPGA to implement the Kalman filter, where fixed-point arithmetic is used in the FPGA, and the fixed-point decimal is set according to the actual situation; The Kalman filter includes a time update equation and a measurement update equation, wherein the time update equation is responsible for estimating the values of the current state variables and error covariance estimates forward in time in order to construct a priori estimates for the next time state, the The measurement update equation is responsible for feedback, combining prior estimates with new measured variables to construct improved posterior estimates; The time update equation is: in, is the prior state estimate, A is the state transition matrix,/> is the posterior state estimate at the previous moment, B is the input control matrix, u _k-1 is the state control variable,/> is the prior covariance estimate, Q is the process excitation noise covariance matrix, and P _k-1 is the posterior covariance estimate at the previous moment; The state update equation is: Among them, K _k is the Kalman gain at the current moment, is the prior covariance estimate, H is the transformation matrix from state variable to measurement, R is the measurement noise covariance,/> is the posterior state estimate, A is the state transition matrix,/> is the prior state estimate, P _k is the posterior covariance estimate, and I is the identity matrix. 3. An electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the process as claimed in claim 1 is implemented. The above mentioned eddy current sensor data processing method based on Kalman filtering. 4. A non-transitory computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the Kalman filter-based eddy current sensor data as claimed in claim 1 is realized Approach.