CN109186630B - MEMS (micro electro mechanical System) coarse alignment method and system based on improved threshold function wavelet denoising - Google Patents
MEMS (micro electro mechanical System) coarse alignment method and system based on improved threshold function wavelet denoising Download PDFInfo
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Abstract
The invention provides an MEMS (micro electro mechanical system) coarse alignment method and system based on improved threshold wavelet denoising, wherein the method comprises the following steps: acquiring sensor real-time data, wherein the sensor real-time data comprises accelerometer data and magnetometer data; performing wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving a threshold; acquiring a direction transfer matrix according to the data subjected to noise reduction; and outputting the obtained direction transfer matrix to a posture tracking system for posture tracking calculation. The invention can solve the problems of slow alignment speed and large error of the existing MEMS coarse alignment system.
Description
Technical Field
The invention relates to the field of inertial navigation, in particular to an MEMS (micro-electromechanical systems) coarse alignment method and system based on improved threshold function wavelet denoising.
Background
The initial alignment technology of the attitude is a key technology of inertial navigation and combined navigation applied to the inertial navigation, and the coarse alignment technology in a general vehicle-mounted positioning system is widely applied due to the fact that the alignment speed is high and the accuracy is enough to meet the requirement of vehicle-mounted positioning.
The precision of initial alignment in inertial navigation plays a crucial role in the navigation precision of the whole system, once the initial alignment error is dispersed, the origin and the actual position of the carrier coordinate system are seriously deviated, so that the subsequent error accumulation is accelerated, and the positioning is deviated.
The initial alignment acquires the inherent information of the earth at the position of the carrier through the gyroscope and the accelerometer of the inertial measurement unit, so as to deduce the position of the carrier on the earth surface. Numerous scholars have conducted intensive research on the basis, wherein the alignment method of the MEMS inertial navigation system based on wavelet denoising, researched by Sunwei et al, uses wavelet denoising to process raw data measured by a magnetometer and an accelerometer and then calculates an initial alignment matrix, and using the magnetometer to replace the conventional gyroscope can use stable and conveniently-measured geomagnetic information to replace earth rotation angular velocity information to calculate a direction transfer matrix under the conditions of low gyroscope precision, large measurement error and large fluctuation in the current stage, but when performing wavelet denoising on an inertial measurement device, a fixed threshold is used, which cannot meet the requirements of both edge characteristics of signals and reduction of signal oscillation, and when calculating a course angle, the calculation process is complicated, and has a certain influence on the alignment speed of a positioning system.
Therefore, to solve the above problems, the present invention provides a MEMS coarse alignment method and system based on improved threshold function wavelet denoising.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for MEMS coarse alignment based on improved threshold function wavelet denoising, so as to solve the problems of slow alignment speed and large error of the existing MEMS coarse alignment system.
The invention provides an MEMS (micro electro mechanical system) coarse alignment method based on improved threshold function wavelet denoising, which comprises the following steps of:
acquiring sensor real-time data, wherein the sensor real-time data comprises accelerometer data and magnetometer data;
performing wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving a threshold;
acquiring a direction transfer matrix according to the data subjected to noise reduction;
and outputting the obtained direction transfer matrix to a posture tracking system for posture tracking calculation.
The invention also provides an MEMS coarse alignment system based on improved threshold wavelet denoising, which comprises: a signal acquisition module and a data processing module, wherein,
the signal acquisition module is used for acquiring sensor real-time data, and the sensor real-time data comprises accelerometer data and magnetometer data;
the data processing module is used for performing wavelet denoising processing on the acquired sensor real-time data by adopting an improved threshold value and performing MEMS (micro electro mechanical system) coarse alignment processing on the denoised data.
According to the technical scheme, the MEMS coarse alignment method and system based on the improved threshold function wavelet denoising, provided by the invention, can be used for processing test data by improving the threshold wavelet and adopting the MEMS coarse alignment method with higher calculation speed and higher accuracy, so that the coarse alignment process of the inertial navigation system can be rapidly and accurately completed, and the coarse alignment process can be provided for an upper computer to complete attitude tracking.
To the accomplishment of the foregoing and related ends, one or more aspects of the invention comprise the features hereinafter fully described. The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Further, the present invention is intended to include all such aspects and their equivalents.
Drawings
Other objects and results of the present invention will become more apparent and more readily appreciated as the same becomes better understood by reference to the following description taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 is a schematic flow chart of a MEMS coarse alignment method based on improved threshold function wavelet denoising according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a logic structure of a MEMS coarse alignment system based on wavelet denoising with an improved threshold function according to an embodiment of the invention;
FIG. 3 is a comparison of attitude tracking pitch angles according to an embodiment of the present invention;
FIG. 4 is a comparison chart of pose tracking roll angles according to an embodiment of the present invention;
FIG. 5 is a chart of comparing attitude tracking heading angles according to an embodiment of the present invention.
The same reference numbers in all figures indicate similar or corresponding features or functions.
Detailed Description
Aiming at the problems of low alignment speed and large error of the existing MEMS coarse alignment system, the invention provides an MEMS coarse alignment method and system based on improved threshold function wavelet noise reduction.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details.
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In order to illustrate the MEMS coarse alignment method based on improved threshold function wavelet denoising provided by the present invention, fig. 1 shows a flow of the MEMS coarse alignment method based on improved threshold function wavelet denoising according to an embodiment of the present invention.
As shown in FIG. 1, the MEMS coarse alignment method based on improved threshold function wavelet denoising provided by the invention comprises the following steps:
s110: acquiring sensor real-time data, wherein the sensor real-time data comprises accelerometer data and magnetometer data;
s120: performing wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving a threshold;
s130: acquiring a direction transfer matrix according to the data subjected to noise reduction;
s140: and outputting the obtained direction transfer matrix to a posture tracking system for posture tracking calculation.
The above is a method how to solve the problems of slow alignment speed and large error in the present invention, wherein the hardware environment includes a data processing module and 3DM-E10A, wherein the parameters of the 3DM-E10A are that the error ranges of the pitch angle and the roll angle under the static condition are ± 0.1 °, the error range of the heading angle is ± 2 °, and the error of the heading angle resolution is less than 0.1 °. Measurement range of each sensor: the accelerometer is + -2 g, the rate gyroscope is + -300 deg./s, and the magnetometer is + -1.3 Gauss. The data processing module adopts a DSP28335 chip, the CPU main frequency is 150MHz, and 32-bit floating point number calculation can be carried out.
Firstly, local geomagnetic information is inquired and stored in a system algorithm, and the horizontal magnetic field strength H0 of the current test is set to be 0.3, and the geomagnetic declination is set to be 90 degrees. Before the equipment is started, the measurement module is placed to be horizontal to the carrier, then the electrified state is kept to be preheated for 30s, the triaxial acceleration value and the triaxial magnetic induction value are collected, and meanwhile, data are transmitted to the DSP28335 chip to perform noise reduction processing on initial data.
Wherein, the noise reduction process comprises the following steps:
1) initializing parameter setting;
setting a wavelet basis function, decomposing the layer number and improving the performance parameters of the wavelet threshold function; specifically, the number of decomposition layers for wavelet threshold denoising is set to be 4, and the magnetometer wavelet basis is selected as: sym6 (X-axis), coif4 (Y-axis, Z-axis); the wavelet basis of the accelerometer is selected as follows: dmey (X-axis, Y-axis), sym6 (Z-axis); the threshold function parameters are set as: 16 for the X-axis magnetometer, 20 for the Y-axis magnetometer, 20 for the Z-axis magnetometer, 9 for the X-axis accelerometer, 9 for the Y-axis accelerometer, 16 for the Z-axis accelerometer; the threshold function parameter is uniformly set to 1.
2) Wavelet decomposition, the decomposition equation is:
wherein, ω isj,kFor decomposed wavelet coefficients, #j,kIs of wavelet system, f isTo decompose the signal, aoIs a scale parameter, boFor shifting parameters, wherein the discrete wavelet system function ψj,kCan be written as:
wherein t is a wavelet system function support domain, and k is an adjusting coefficient.
3) Screening wavelet coefficients through a threshold function to complete signal denoising; the threshold function is:
wherein k and alpha are regulating coefficients which are always positive, and T is a set threshold value, and the formula is as follows:
wherein, N is the signal length, sigma is the noise standard deviation, and j is the number of decomposition layers;
4) wavelet reconstruction, namely reducing the wavelet coefficient processed by the threshold function into a useful signal, wherein the formula of the wavelet reconstruction is as follows:
wherein, X is the signal after noise reduction processing, and C is a constant independent of the signal.
In the implementation of the present invention, in the process of acquiring the direction transfer matrix from the data after the noise reduction processing, 1) a conversion matrix from the carrier coordinate system to the navigation coordinate system is constructed
The calculation formula of (a) is as follows:
wherein psi is a course angle, theta is a pitch angle, and gamma is a roll angle;
2) constructing a carrier attitude matrix and a navigation attitude matrix, wherein the attitude matrix is constructed through the output of the accelerometer and the magnetometer after the denoising treatment;
the attitude matrix constructed under the navigation coordinate system is as follows: [ f ] ofnfn×Mn(fn×Mn)×fn]；
The attitude matrix under the carrier coordinate system is as follows: [ f ] ofbfb×Mb(fb×Mb)×fb]；
Wherein:
3) according to the formula:
wherein the content of the first and second substances,
calculating to obtain a course angle psi, a pitch angle theta and a roll angle gamma, and obtaining a direction transfer matrix
And then outputting the obtained direction transfer matrix to an attitude tracking algorithm to finish the coarse alignment process.
Corresponding to the method, the invention also provides an MEMS coarse alignment system based on the improved threshold function wavelet denoising, and FIG. 2 shows the logic structure of the MEMS coarse alignment system based on the improved threshold function wavelet denoising according to the embodiment of the invention.
As shown in fig. 2, the present invention provides a MEMS coarse alignment system based on improved threshold wavelet noise reduction, comprising: the device comprises a signal acquisition module 1 and a data processing module 2, wherein the signal acquisition module 1 is used for acquiring sensor real-time data, and the sensor real-time data comprises accelerometer data and magnetometer data; the signal acquisition module 1 comprises three types of sensors, namely a three-axis MEMS gyroscope, a three-axis MEMS accelerometer and a three-axis magneto-resistive magnetometer, wherein the measurement range of the accelerometer is nonlinear, and the measurement range of the magnetometer is nonlinear;
and the data processing module 2 is used for performing wavelet denoising processing on the acquired real-time sensor data by adopting an improved threshold value and performing MEMS (micro electro mechanical system) coarse alignment processing on the denoised data.
The signal acquisition module 1 comprises an acceleration measurement unit 11 and a geomagnetic information measurement unit 12, wherein the acceleration measurement unit 11 is used for acquiring accelerometer data in real time; and a geomagnetic information measurement unit 12 configured to obtain magnetometer data in real time.
Wherein the data processing module 2 comprises a processor and a communication circuit 22, wherein
The processor is used for performing wavelet denoising processing on the acquired accelerometer data and the acquired magnetometer data through improving a threshold value and acquiring a direction transfer matrix according to the denoised data; outputting the obtained direction transfer matrix to an attitude tracking system for attitude tracking calculation;
wherein the processor is a DSP processor 21 comprising a memory unit 211 and a DSP28335 (212).
And the communication circuit 22 is used for transmitting the data obtained by calculation to an external device, wherein the communication circuit comprises a wireless communication unit 221 and an RS422 unit 222.
The communication circuit comprises a serial port communication unit and a wireless communication unit, the serial port communication unit is electrically connected with an upper computer through RS422, and the wireless communication unit is used for carrying out remote transmission through E3A-DTU-1W. That is to say, communication circuit includes wireless communication unit and RS422 serial units, and wireless communication unit can realize the remote transmission of data, and RS422 serial units is in order to adapt to current train on-board equipment standard, and the later maintenance and the update of being convenient for.
The inertial measurement unit 3DM-E10A of the signal acquisition module can obtain acceleration information and magnetic field information of the position of the measurement module, the inertial measurement unit is connected to the data processing module through a data line to provide data output of TTL level, the TTL-RS 232 circuit on the data processing module is used for converting the data and then processing and resolving the data to obtain a direction transfer matrix, and the direction transfer matrix is output to an upper computer through a communication circuit in the data processing module to finish attitude tracking.
The inertial measurement unit comprises three sensors, namely a triaxial MEMS accelerometer, a triaxial MEMS gyroscope, a triaxial magneto-resistive magnetometer and the like.
The data processing module is composed of a DSP28335 chip and peripheral storage equipment thereof, the DSP8335 chip is mainly used for data calculation and processing, the peripheral storage equipment thereof is used for storing calculation results and input parameters, and the DSP28335 chip can be used for control.
The DSP28335 module is adopted as the data processing module, wherein the CPU main frequency is 150MHz, 32-bit floating point number calculation can be carried out, a 28335 chip is provided with 256K FLASH, 34K SRAM, 8K BOOT ROM and 1K OPT ROM, and the memories are 16-bit memories.
In the embodiment of the invention, the test result shows that the improved threshold wavelet denoising processing can obviously inhibit additional noise signals, and particularly the denoising optimization effect of a magnetometer is obvious. The noise reduction method has a certain effect on the noise reduction of the accelerometer, and has a very obvious noise reduction effect on the magnetometer. The filtering method can inhibit the interference of environmental noise to a certain extent, reduce signal oscillation, track the change condition of the original data in real time and ensure that the result after filtering is not distorted.
After the processing is finished, the data subjected to noise reduction processing is led into an attitude angle solving matrix to be solved to obtain three attitude angles: the course angle, the pitch angle and the roll angle are compared with the traditional indirect coarse alignment method of the soft threshold wavelet assisted magnetometer and the indirect coarse alignment method of the improved threshold wavelet assisted magnetometer by taking the attitude angle output by the inertial navigation measuring platform as a reference value, and the result shows that the error of the roll angle is 0.0233 degrees, the pitch angle is 0.0063 degrees and the course angle is 0.2320 degrees after the improved threshold wavelet denoising is adopted, the error range of experimental equipment is within the error range, the precision requirement of the coarse alignment is met, and the signal oscillation is well inhibited compared with the traditional denoising method.
In order to further verify the optimization of the alignment result on the attitude tracking effect, the alignment attitude array is introduced into a quaternion attitude tracking algorithm. Fixing an inertia measurement unit on a test turntable, starting an inertial navigation system and then performing initial alignment, rotating the turntable clockwise around an x axis at a speed of 0.1 degree per second and anticlockwise around a z axis at a speed of 0.02 degree per second at the 8 th minute, recording experimental data, and introducing an attitude matrix obtained in coarse alignment into attitude estimation for data calculation and comparison, as shown in fig. 3 to 5. The result shows that the combined alignment method of the improved threshold auxiliary magnetometer optimizes the magnetometer alignment algorithm of the traditional soft threshold wavelet, so that the positioning error is greatly reduced, the data convergence is accelerated, the alignment time is shortened, and the system response speed is improved. It can be seen that after the improved algorithm is adopted, the error between the attitude angle and the reference data is basically smaller than that of the traditional method, and the data offset is also basically smaller than that of the traditional threshold wavelet assisted method, which can show that the initial attitude alignment result processed by the improved algorithm has a certain optimization function on the follow-up attitude tracking.
According to the MEMS coarse alignment method and system based on the improved threshold function wavelet denoising, provided by the invention, the test data is processed by the improved threshold wavelet, and the MEMS coarse alignment method with higher calculation speed and higher accuracy is adopted, so that the coarse alignment process of the inertial navigation system is rapidly and accurately completed, and the coarse alignment process is provided for an upper computer to complete attitude tracking.
The MEMS coarse alignment method and system based on improved threshold function wavelet denoising proposed according to the present invention are described above by way of example with reference to the accompanying drawings. However, it will be understood by those skilled in the art that various modifications may be made to the MEMS coarse alignment method and system based on improved threshold function wavelet denoising, which are proposed by the present invention, without departing from the scope of the present invention. Therefore, the scope of the present invention should be determined by the contents of the appended claims.
Claims (7)
1. A MEMS coarse alignment method based on improved threshold function wavelet denoising comprises the following steps:
acquiring sensor real-time data, wherein the sensor real-time data comprises accelerometer data and magnetometer data;
performing wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving a threshold;
acquiring a direction transfer matrix according to the data subjected to noise reduction;
outputting the obtained direction transfer matrix to an attitude tracking system for attitude tracking calculation;
wherein, in the process of carrying out wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving the threshold value,
1) initializing parameter setting;
setting a wavelet basis function, decomposing the layer number and improving the performance parameters of the wavelet threshold function;
2) wavelet decomposition, the decomposition equation is:
wherein, ω isj,kFor decomposed wavelet coefficients, #j,kIs a wavelet system, f is a signal to be decomposed, aoIs a scale parameter, boFor shifting parameters, wherein the discrete wavelet system function ψj,kCan be written as:
wherein t is a wavelet system function support domain, and k is an adjusting coefficient;
3) screening wavelet coefficients through a threshold function to complete signal denoising; the threshold function is:
wherein k and alpha are regulating coefficients which are always positive, and T is a set threshold value, and the formula is as follows:
wherein, N is the signal length, sigma is the noise standard deviation, and j is the number of decomposition layers;
4) wavelet reconstruction, namely reducing the wavelet coefficient processed by the threshold function into a useful signal, wherein the formula of the wavelet reconstruction is as follows:
wherein, X is the signal after noise reduction processing, and C is a constant independent of the signal.
2. The MEMS coarse alignment method based on improved threshold function wavelet denoising of claim 1,
the number of decomposition layers for wavelet threshold denoising is set to be 4;
the magnetometer wavelet basis is chosen to be: the X axis is sym6, and the Y axis and the Z axis are both coif 4;
the wavelet basis of the accelerometer is selected as follows: the X and Y axes are both dmey, and the Z axis is sym 6;
the threshold function parameters are set as: 16 for the X-axis magnetometer, 20 for the Y-axis magnetometer, 20 for the Z-axis magnetometer, 9 for the X-axis accelerometer, 9 for the Y-axis accelerometer, 16 for the Z-axis accelerometer;
the threshold function parameter is uniformly set to 1.
3. The MEMS coarse alignment method based on improved threshold function wavelet denoising of claim 1, wherein, in the process of acquiring the direction transfer matrix according to the denoised data,
1) constructing a transformation matrix from a carrier coordinate system to a navigation coordinate system
The calculation formula of (a) is as follows:
wherein psi is a course angle, theta is a pitch angle, and gamma is a roll angle;
2) constructing a carrier attitude matrix and a navigation attitude matrix, wherein the attitude matrix is constructed through the output of the accelerometer and the magnetometer after the denoising treatment;
the attitude matrix constructed under the navigation coordinate system is as follows: [ f ] ofnfn×Mn(fn×Mn)×fn]；
The attitude matrix under the carrier coordinate system is as follows: [ f ] ofbfb×Mb(fb×Mb)×fb]；
Wherein:
3) according to the formula:
wherein the content of the first and second substances,
calculating to obtain a course angle psi, a pitch angle theta and a roll angle gamma, and obtaining a direction transfer matrix
4. A MEMS coarse alignment system based on improved threshold function wavelet denoising, comprising: a signal acquisition module and a data processing module, wherein,
the signal acquisition module is used for acquiring sensor real-time data, and the sensor real-time data comprises accelerometer data and magnetometer data;
the data processing module is used for performing wavelet denoising processing on the acquired sensor real-time data by adopting an improved threshold value and performing MEMS (micro electro mechanical system) coarse alignment processing on the denoised data;
wherein, in the process of carrying out wavelet denoising processing on the acquired accelerometer data and magnetometer data by improving the threshold value,
1) initializing parameter setting;
setting a wavelet basis function, decomposing the layer number and improving the performance parameters of the wavelet threshold function;
2) wavelet decomposition, the decomposition equation is:
wherein, ω isj,kFor decomposed wavelet coefficients, #j,kIs a wavelet system, f is a signal to be decomposed, aoIs a scale parameter, boFor shifting parameters, wherein the discrete wavelet system function ψj,kCan be written as:
wherein t is a wavelet system function support domain, and k is an adjusting coefficient;
3) screening wavelet coefficients through a threshold function to complete signal denoising; the threshold function is:
wherein k and alpha are regulating coefficients which are always positive, and T is a set threshold value, and the formula is as follows:
wherein, N is the signal length, sigma is the noise standard deviation, and j is the number of decomposition layers;
4) wavelet reconstruction, namely reducing the wavelet coefficient processed by the threshold function into a useful signal, wherein the formula of the wavelet reconstruction is as follows:
wherein, X is the signal after noise reduction processing, and C is a constant independent of the signal.
5. The MEMS coarse alignment system based on improved threshold function wavelet noise reduction of claim 4, wherein the signal acquisition module comprises an acceleration measurement unit and a geomagnetic information measurement unit, wherein,
the acceleration measuring unit is used for acquiring accelerometer data in real time;
and the geomagnetic information measurement unit is used for acquiring magnetometer data in real time.
6. The MEMS coarse alignment system based on improved threshold function wavelet denoising of claim 4,
the data processing module comprises a processor and a communication circuit, wherein
The processor is used for performing wavelet denoising processing on the acquired accelerometer data and the acquired magnetometer data through improving a threshold value, and acquiring a direction transfer matrix according to the denoised data; outputting the obtained direction transfer matrix to an attitude tracking system for attitude tracking calculation;
and the communication circuit is used for transmitting the data obtained by calculation to an external device.
7. The MEMS coarse alignment system based on improved threshold function wavelet denoising of claim 6,
the signal acquisition module comprises three types of sensors, namely a three-axis MEMS gyroscope, a three-axis MEMS accelerometer and a three-axis magneto-resistive magnetometer, wherein the measurement range of the accelerometer is +/-2 g and nonlinear 0.2%, and the measurement range of the magnetometer is +/-1.3 Gauss and nonlinear 0.4%;
the communication circuit comprises a serial port communication unit and a wireless communication unit, wherein the serial port communication unit is electrically connected with an upper computer by adopting RS422, and the wireless communication unit is used for carrying out remote transmission by adopting E3A-DTU-1W.
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