CN113364431A - Filtering method for improving signal quality of acceleration sensor - Google Patents
Filtering method for improving signal quality of acceleration sensor Download PDFInfo
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- CN113364431A CN113364431A CN202110533510.5A CN202110533510A CN113364431A CN 113364431 A CN113364431 A CN 113364431A CN 202110533510 A CN202110533510 A CN 202110533510A CN 113364431 A CN113364431 A CN 113364431A
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0248—Filters characterised by a particular frequency response or filtering method
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- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
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- H03H17/026—Averaging filters
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Abstract
The invention provides a filtering method for improving the signal quality of an acceleration sensor, which comprises the following steps: and filtering the raw data of the acceleration sensor by a Butterworth filter and a moving average filter in sequence. The filtering method only needs to accumulate L numbers of the sliding interval for the first time to obtain a sliding average filtering numerical value, and then the sliding average numerical value only needs to be calculated on average after newly acquired data is added and oldest data is subtracted, so that the operation times are reduced, and the probability that a data point deviates from an actual value upwards or downwards is equal. The curve can be smoother by increasing the period number of the moving average filtering algorithm, the noise elimination effect is more ideal and the signal-to-noise ratio is more improved by selecting the reasonable period number, and after the filtering method is adopted, the peak noise and the random noise of the sensor are filtered, and the signal-to-noise ratio can be obviously increased.
Description
Technical Field
The invention belongs to the field of sensor testing, and particularly relates to a filtering method for improving the signal quality of an acceleration sensor.
Background
The acceleration sensor is widely used for acceleration measurement in various industrial fields, and in the application of rail transit, due to the fact that the arrangement of various devices inside and outside a train is dense, the acceleration sensor is mounted inside a train body or outside the train body and faces uncertainty of the devices and the environment, a lot of noises exist in original signals collected by the acceleration sensor, for example, noises randomly distributed in the whole frequency range, spike noises with short duration and large amplitude, and the like. These all bring influence to the follow-up processing of acceleration signal, in order to improve the quality of sensor signal, be convenient for higher level system collection analysis, it is very necessary to carry out certain filtering to acceleration sensor output signal.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a filtering method and a device for improving the signal quality of an acceleration sensor. The curve can be smoother by increasing the period number of the moving average filtering algorithm, the noise elimination effect is more ideal and the signal-to-noise ratio is more improved by selecting the reasonable period number, and after the filtering method is adopted, the peak noise and the random noise of the sensor are filtered, and the signal-to-noise ratio can be obviously increased.
In order to achieve the above purpose of the present invention, the following technical solutions are adopted:
in a first aspect, the present invention discloses a filtering method for improving the signal quality of an acceleration sensor, including:
filtering the original data of the acceleration sensor by a Butterworth filter and a moving average filter in sequence;
wherein, the order N of the normalized analog low-pass filter of the Butterworth filter is:
αsfor minimum attenuation of stop band, αpNormalized for the maximum attenuation of the passband, αpLet λ be Ω as 1s/ΩpWherein Ω issIs the stop band cut-off frequency, omegapIs the passband cutoff frequency;
the method of the moving average filter comprises the following steps: assuming that N is the total number of dynamic test data and L is an odd number, the algorithm is as follows:
when L is an even number, the algorithm is:
l is the number of periods of the moving average, ytIs a predicted value for the next period; f. oft-LThe data actual value is dynamically tested.
The filtering method of the invention abandons the limitation of the prior common filtering algorithm, adopts a novel filtering algorithm, namely an algorithm combining a Butterworth low-pass filter and moving average filtering to process data of the sensor signal, and the Butterworth filter is a high-grade (at least more than 4 orders) Butterworth filter, because clutter of each grade needs to be filtered on the acceleration sensor, the invention belongs to higher-grade filtering, in order to be better suitable for the field, the smoothing algorithm and the Butterworth algorithm are correspondingly improved, and the invention is more suitable for the acceleration monitoring field after combination.
In a second aspect, the invention discloses a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method according to the first aspect.
In a third aspect, the invention discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect when executing the program.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a detailed flow chart of data flow provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a specific structure of a MEMS device according to an embodiment of the present invention;
FIG. 3 is a flow chart of a process for fabricating a MEMS device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
In the figure: 1-fixed frame, 2-mass block, 3-front beam and 4-back beam.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and the detailed description, but those skilled in the art will understand that the following described embodiments are some, not all, of the embodiments of the present invention, and are only used for illustrating the present invention, and should not be construed as limiting the scope of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The invention discloses a filtering method for improving the signal quality of an acceleration sensor, which comprises the following steps:
filtering the original data of the acceleration sensor by a Butterworth filter and a moving average filter in sequence;
wherein, the order N of the normalized analog low-pass filter of the Butterworth filter is:
αsfor minimum attenuation of stop band, αpNormalized for the maximum attenuation of the passband, αpLet λ be Ω as 1s/ΩpWherein Ω issIs the stop band cut-off frequency, omegapIs the passband cutoff frequency;
the method of the moving average filter comprises the following steps: assuming that N is the total number of dynamic test data and L is an odd number, the algorithm is as follows:
when L is an even number, the algorithm is:
l is the number of periods of the moving average, ytIs a predicted value for the next period; f. oft-LThe data actual value is dynamically tested.
In the prior art, a general software filtering algorithm is performed by using a schwirler outlier removing criterion to screen and remove outliers, and such a method is effective for preprocessing a series of measured values that obey a certain probability distribution under the same measurement condition, but is limited to several cases:
1) when different types of complex noise or high-frequency interference are mixed in an original measuring signal of the sensor, the noise cannot be removed fundamentally.
2) When an ambiguous abnormal value is generated, the amount of data is reduced.
Based on the limitation of a common software filtering algorithm, in order to ensure the integrity and the reasonability of data, the invention adopts a newly disclosed filtering algorithm in academia, namely an algorithm based on the combination of a Butterworth low-pass filter and a moving average filter to process data of a sensor signal, and the circulation process of a specific data stream is shown in figure 1.
The Butterworth filter belongs to an IIR digital filter, not only has amplitude-frequency corresponding characteristics of the maximum flat limit, but also has good linear phase characteristics, and has a good filtering effect on spike noise; the moving average filtering algorithm utilizes a point function value to represent a deterministic change rule of dynamic test data to eliminate random fluctuation in the data, and in terms of a Butterworth filter, the invention adopts a high-grade (at least more than 4 orders) Butterworth filter, the higher N is, the better the amplitude-frequency characteristic is, the higher the fidelity of a low-frequency detection signal is, and therefore, a low-pass filter with a higher cut-off order is selected as far as possible.
Preferably as a further implementable partyThe passband cut-off frequency is (omega)p) Is 0-50Hz, and the maximum attenuation (alpha) of the passbandp) Less than 3 dB.
Preferably, as a further implementable solution, the stopband cut-off frequency (Ω)s) Is 500Hz-2500Hz, and the stop band has minimum attenuation (alpha)s) Greater than 80 dB.
In the aspect of a smoothing algorithm, partial lost data is supplemented on the basis of a classical method, the invention divides the period number of the moving average into an odd number section and an even number section, the total length is L, thus when L is a base number, a calculation method is adopted, when L is an even number, another calculation method is adopted, and the filtering effect is improved through the targeted calculation method.
After the method is adopted, the L numbers of the sliding interval are accumulated to obtain the sliding average filtering value only for the first time, and the subsequent sliding average value is calculated only by adding newly acquired data and subtracting the oldest data, so that the operation times are reduced, and the probability that the data points deviate from the actual value upwards or downwards is equal. Increasing the period number L of the moving average filtering algorithm makes the curve smoother, and by selecting a reasonable period number, the more desirable the noise cancellation effect will be, the more the signal-to-noise ratio will be improved. In the present invention, the term L is preferably from 90 to 100, and more preferably, 100 is used. According to the disclosed experimental data, the spike noise and random noise of the sensor are filtered out, and the signal-to-noise ratio can be obviously increased.
In order to improve the filtering effect from the aspect of hardware, the invention adopts the latest ultra-low noise and high stability micro-electro-mechanical system (MEMS) accelerometer as an acceleration sensing device. The device is a double-sided fixed support structure and comprises a fixed frame 1, a mass block 2, 8 front beams 3 and 4 back beams 4, wherein the front and the back of the mass block are fixed in the fixed frame through the front beams and the back beams to improve the balance degree, and the specific structure is shown in figure 2.
The force-sensitive resistors are symmetrically distributed on the upper surface of the front beam. The Z axis of the sensitive direction is vertical to the front surface of the mass block, the X axis and the Y axis are vertical to the Z axis, and the three axes are completely symmetrical and can be equivalently interchanged. Elastic modulus E of single crystal silicon used as elastic beam is 1.7x1011Pa, density rho 2328kg/m3. When the axes in all directions have acceleration, the mass block generates inertia force. The resistance value of the force sensitive resistor on the front beam changes correspondingly, and the voltage amplitude corresponding to the acceleration is output.
Preferably, as a further implementable scheme, the range of the MEMS is no less than 3000g at most, the sensitivity is greater than 0.5uV/g/10VDC, the frequency bandwidth is greater than 500kHz, the lateral sensitivity ratio is less than 3%, the fabrication process of the MEMS is completed based on the currently mature bulk silicon process, the sensitive structure can be completed by using the processes of ion implantation, annealing, metallization, double-sided lithography, reactive ion etching and the like, and the specific process flow is shown in fig. 3.
By adopting the latest MEMS design, the low noise and high stability of the induction device in the hardware design can be realized. On the basis of the MEMS sensing device, the high-precision analog-to-digital converter (ADC), the band-pass filter and the digital filter are combined, so that the noise in the surrounding equipment and environment of the acceleration sensor can be further removed. The effective signal is output to the superior system through the logic control circuit and the interface circuit.
Fig. 4 is a schematic structural diagram of a computer device disclosed in the present invention, and referring to fig. 4, the computer device 300 at least includes a memory 302 and a processor 301; the memory 302 is connected to the processor via a communication bus 303 for storing computer instructions executable by the processor 301, and the processor 301 is configured to read the computer instructions from the memory 302 to implement the steps of the filtering method according to any of the above embodiments.
Technical solutions of the present invention have been described with reference to preferred embodiments shown in the drawings, but it is apparent that the scope of the present invention is not limited to these specific embodiments, as will be readily understood by those skilled in the art. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
For the above-mentioned apparatus embodiments, since they basically correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. One of ordinary skill in the art can understand and implement it without inventive effort.
Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal magnetic disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Finally, it should be noted that: while this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Further, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The above description is only exemplary of the present disclosure and should not be taken as limiting the disclosure, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (9)
1. A filtering method for improving the signal quality of an acceleration sensor is characterized by comprising the following steps:
filtering the original data of the acceleration sensor by a Butterworth filter and a moving average filter in sequence;
wherein, the order N of the normalized analog low-pass filter of the Butterworth filter is:
αsfor minimum attenuation of stop band, αpNormalized for the maximum attenuation of the passband, αpLet λ be Ω as 1s/ΩpWherein Ω issIs the stop band cut-off frequency, omegapIs the passband cutoff frequency;
the method of the moving average filter comprises the following steps: assuming that N is the total number of dynamic test data and L is an odd number, the algorithm is as follows:
when L is an even number, the algorithm is:
l is the number of periods of the moving average, ytIs a predicted value for the next period; f. oft-LThe data actual value is dynamically tested.
2. The filtering method according to claim 1, wherein the passband cutoff frequency is 0-50Hz and the passband maximum attenuation is less than 3 dB.
3. The filtering method as claimed in claim 1, wherein the stopband cut-off frequency is between 500Hz and 2500Hz, and the stopband minimum attenuation is greater than 80 dB.
4. The filtering method according to claim 1, wherein the period number L of the moving average is between 90 and 100.
5. The filtering method according to claim 1, wherein said acceleration sensor is selected as a MEMS.
6. The filtering method according to claim 5, wherein the MEMS comprises a fixed frame, a mass block, 8 front beams and 4 back beams, wherein the front and back surfaces of the mass block are fixed in the fixed frame through the front beams and the back beams to improve balance.
7. The filtering method according to claim 5, wherein the range of the MEMS is not less than 3000g at most, the sensitivity is more than 0.5uV/g/10VDC, the frequency bandwidth is more than 500kHz, and the lateral sensitivity ratio is less than 3%.
8. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed, carries out the steps of the method of any one of claims 1 to 4.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-4 are implemented when the program is executed by the processor.
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| CN113842135A (en) * | 2021-09-18 | 2021-12-28 | 吉林大学 | BilSTM-based sleep breathing abnormality automatic screening method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113842135A (en) * | 2021-09-18 | 2021-12-28 | 吉林大学 | BilSTM-based sleep breathing abnormality automatic screening method |
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