CN107247819B - Filtering methods and filters for sensors - Google Patents

Abstract

The invention discloses a filtering method of a sensor. Performing primary filtering on the original output value of the sensor, and outputting the original output value A of the sensor by the ith primary filtering_iThe ith filtering step comprises the following steps: s1, judging A according to the trip point judgment rule_iAnd then the original output values of the continuous N sensors are jumping points or non-jumping points, N is more than or equal to 1, and the step S2 is carried out; s2, if A_iIs a trip point and A_iIf the non-skip point exists in the original output values of the subsequent continuous N sensors, the step is switched to the step S3, otherwise, A_iThe original value is used as a type of filtering predicted value; s3, comparing the current maximum gradient with A_i‑1The type I filter prediction value of (1) is added, and the result is used as A_iThe current maximum gradient is A_iAnd the gradient value with the maximum gradient absolute value in the previous continuous M type-one filtering prediction values. The invention solves the technical problem of abnormal skip point elimination.

Description

Filtering methods and filters for sensors

technical field

The present invention relates to digital signal filtering, and more particularly, to a digital signal filtering and filter of a sensor.

Background technique

The source of sensor error can be roughly divided into two categories: deterministic error and random error. Usually, deterministic error can be compensated by testing and calibration under laboratory conditions, while random error is more complicated, and its variation law is random. , it is necessary to obtain their statistical laws through statistical methods, and establish a reasonable mathematical model for error compensation.

Among the commonly used random error analysis and compensation methods, the autocorrelation function method is easy to introduce more errors, resulting in low error modeling accuracy and inability to realize real-time error compensation; the power spectral density analysis method only gives the power spectral density of the signal. The curve of the function in the frequency domain cannot directly give the time domain characteristics of each error, so the identification results need to be further processed, and the method is relatively troublesome; the finite impulse response filtering method has high cost and large signal delay; recursive digital The phase of the filter is nonlinear.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a technical solution for removing abnormal jump points from sensor data.

According to a first aspect of the present invention, a filtering method of a sensor is provided.

First-type filtering is performed on the original output value of the sensor, and the i-th first-type filtering outputs the first-type filtering predicted value corresponding to the original output value A _i of the sensor, i≥1, the first type of the original output values of M consecutive sensors before A ₁ The filter prediction value defaults to its corresponding original output value of the sensor, M ≥ 3, and the steps of the i-th type 1 filtering include: S1, according to the jump point judgment rule, determine that A _i and the original output values of consecutive N consecutive sensors after it are Jump point or non-jump point, N≥1, go to step S2; S2, if A _i is a jump point and there is a non-jump point in the original output values of N consecutive sensors after A _i , go to step S3, otherwise A _i The original value is used as its type-1 filtering prediction value; S3, the current maximum gradient is added to the type-1 filtering prediction value of A _i-1 , and the result is used as the type-1 filtering prediction value of A _i , and the current maximum gradient is before A _i The gradient value with the largest absolute value of the gradient among the consecutive M one-type filtering predicted values; S4, outputting the one-type filtering predicted value of A _i .

其中i≤n≤i+N，D_n-1为所述数列中A_n之前紧邻的数据点，k为可设定的一个系数，0.3≤k≤0.8，则判定A_n为跳点，否则A_n为非跳点。Optionally, the jump point determination method is: the i-th type-1 filtering, M before A _i of the type-1 filtering predicted values and A _i and consecutive N sensor raw output values after A _i according to their respective correspondences. The time sequence of the appearance of the original output values of the sensor is composed of a sequence, and the gradient of each data point is calculated. If the following two conditions are met at the same time: Condition ₁ , the absolute value of the gradient of An _is greater than the maximum absolute value of the gradient of the M data points before An. value, condition 2, the absolute value of the gradient of An is greater _{than the maximum value of error offset1 and error offset2 , where}

where i≤n≤i+N, D _n-1 is the data point immediately before A _n in the sequence, k is a coefficient that can be set, 0.3≤k≤0.8, then it is determined that A _n is a jump point, otherwise _{An is} a non-jump point.

Optionally, according to the type-2 filtering judgment rule, perform type-2 filtering on Q consecutive predicted values of type-1 filtering, and output their corresponding type-2 filtering output values, Q≥5, and the judgment rule for type-2 filtering is: : when there are two or more inflection points in the consecutive Q first-type filtering predicted values, perform second-type filtering on the consecutive Q first-type filtering predicted values; otherwise, the consecutive Q first-type filtering predicted values The second-type filtering output values of the first-type filtering predicted values are their respective first-type filtering predicted values.

Optionally, according to the type-2 filtering judgment rule, perform type-2 filtering on Q consecutive predicted values of type-1 filtering, and output their corresponding type-2 filtering output values, Q≥5, and the judgment rule for type-2 filtering is: : when there are two or more inflection points in the consecutive Q first-type filtering predicted values, perform second-type filtering on the consecutive Q first-type filtering predicted values; otherwise, the consecutive Q first-type filtering predicted values The second-type filtering output values of the first-type filtering predicted values are their respective first-type filtering predicted values.

Optionally, the numerical values of the several gradient data points and their adjacent non-inflection points form an arithmetic progression.

The present invention also provides a filter, comprising an input end, a type-1 filter output storage module and a type-1 filter module; the type-1 filter module includes a jump point marking module, a jump point determination module and a type-1 filter control module; The input terminal is connected to the type-1 filter control module; the type-1 filter output storage module is connected to the type-1 filter control module, and the type-1 filter output storage module is set to store the output value of the filter ; Described jump point judgment module is connected with described one type filtering control module, and described jump point judgment module is set to judge that the data point in described one type filtering output storage module is jump point or non-jump point; The jumping point marking module is connected with the jumping point judging module, and the jumping point marking module is set to mark the data point in the first-type filtering output storage module as a jumping point according to the judgment result of the jumping point judging module. point or non-jump point; the type 1 filter control module is also connected with the jump point marking module, and the type 1 filter control module is set to determine the corresponding jump point of the data point to be filtered in the type 1 filter output storage module The point marking module is marked as a jump point, and the consecutive N data points after the data point are marked as non-jump points in the corresponding jump point marking module, then the absolute gradient of the M data points before the data point to be filtered is absolute. The gradient value of the data point with the largest value is added to the previous data point of the data point to be filtered, and the result is reassigned to the data point to be filtered, M ≥ 3, N ≥ 1, otherwise, the first-type filtering output storage The module value is unchanged, the initial state, the original value of the initial M+N+1 data points at the input end is assigned to the data point corresponding to the type-1 filter output storage module, and the jump point marking module stores the type-1 filter output storage module. The first M data points of are correspondingly marked as non-jumping points.

其中k为一个可设定的系数，0.3≤k≤0.8。Optionally, the jump point determination module is set so that the data point in the first type filtering output storage module satisfies the following two conditions at the same time, then it is determined that the corresponding mark of the data point in the jump point mark module is: Jump point, otherwise the data point is marked as a non-jump point in the jump point marking module. Condition 1, the absolute value of the gradient of the data point is greater than the maximum value of the absolute value of the gradient of the M data points before the data point, M≥3; Condition 2, the absolute value of the gradient of the data point is greater than the maximum value of error _offset1 and error _offset2 , where

Where k is a settable coefficient, 0.3≤k≤0.8.

Optionally, the filter further includes a type 2 filter module, the type 2 filter module is connected to the type 1 filter storage module, and the type 2 filter module is configured to output the storage module for the type 1 filter. Perform type-2 filtering on consecutive Q data points in the middle, and output corresponding Q type-2 filtering output values, Q≥5. The type-2 filtering module includes a type-2 filtering input terminal, a type-2 filtering judgment module, and a type-2 filtering compensation module. and a type-2 filter output terminal, the type-2 filter determination module is connected with the type-2 filter input terminal, the type-2 filter output terminal and the type-2 filter compensation module, and the type-2 filter determination module is set In order to determine that the number of inflection points of the Q data points to be filtered by the type-2 filter input terminal is less than 2, the original value of the Q data points is output to the output terminal of the type-2 filter, or the Q data points are output to the type-2 filter output terminal. The point is output to the second-type filter output after the second-type filter compensation is performed by the second-type filter compensation module. The second-type filter compensation module is also connected to the second-type filter output. The second-type filter compensation module is It is set to perform type-2 filtering compensation on the Q data points and output to the output terminal of type-2 filtering.

Optionally, the type II filter compensation module is set to interpolate several gradient values between the non-inflection points and the non-inflection points of the Q data points, and perform a quadratic effect on all the gradient values and the Q data points. The function is fitted, and the fitting values corresponding to the Q data points obtained after fitting are output to the output terminal of the secondary filter.

Optionally, the numerical values of the several gradient values and their adjacent non-inflection points are arithmetic progressions in numerical value.

The inventor of the present invention has found that, in the prior art, it is an effective model for eliminating abnormal jump points of sensor signals. Therefore, the technical tasks to be achieved by the present invention or the technical problems to be solved are never thought or expected by those skilled in the art, so the present invention is a new technical solution.

Other features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.

Figure 1 is a flow chart of the use of Type 1 filtering in conjunction with Type 2 filtering.

FIG. 2 is a block diagram of a corresponding filter of a type of filtering method.

Figure 3 is a flow diagram of one embodiment of a Type 2 filtering method.

FIG. 4 is a filtering effect diagram of the first type filtering.

Fig. 5 is a filtering effect diagram of the first type filtering.

FIG. 6 is a filtering effect diagram of the second type filtering.

FIG. 7 is a filtering effect diagram of the type 2 filtering.

FIG. 8 is a filtering effect diagram of the second type filtering.

Detailed ways

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

In all examples shown and discussed herein, any specific values should be construed as illustrative only and not limiting. Accordingly, other instances of the exemplary embodiment may have different values.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further discussion in subsequent figures.

It should be noted that any two modules, components, circuits, etc. described in the present invention are connected, either directly or indirectly through other modules, components, circuits, etc. .

FIG. 1 is a flow chart of the combined operation of the first-type filtering and the second-type filtering, showing the general design concept of the filtering method of the sensor provided by the present invention. The sensor data 100 is subjected to the first-type filtering 200 and then the second-type filtering 300, and finally the stationary data 400 is output.

The first-type filtering can effectively eliminate large abnormal jump points, and the second-type filtering can realize the fitting and smooth processing of small continuous burrs. Combining the two can get more accurate and stable data.

In specific implementation, the first-type filtering and the second-type filtering can also be implemented independently.

The specific implementation process of the first-type filtering and the second-type filtering is described in detail below.

At the beginning of filtering, no judgment is made on the original output values of the first five sensors output by the sensor. By default, the predicted value of the first-type filter corresponding to the five original output values is their respective original output value, and the five predicted values of the first-type filter are calculated. A total of 4 gradient values can be obtained.

In actual operation, it is also possible to not judge the original output values of the M sensors first output by the sensor, and M is greater than or equal to 3; the meaning of this setting is to obtain at least two gradient values first, and select the maximum absolute value of the gradient as the first. Reference data for a type-one filter output data point.

All gradients described in this patent are the difference of a data point with respect to the previous data point of the data point.

The "||" symbol in all formulas in this patent is the symbol of absolute value.

The first-type filtering predicted value corresponding to the original output value A _i of the first-type filter output sensor, i≥1, the first-type filtering predicted value of the original output value of the five consecutive sensors before A ₁ defaults to the original output value of the corresponding sensor Output value, the steps of the i-th first-type filtering include: S1, according to the jump point determination rule, determine that A _i and the original output values of two consecutive sensors after it are jump points or non-jump points, go to step S2; S2, if A _i is a jump point and there is a non-jump point in the original output values of the two consecutive sensors after A _i , go to step S3, otherwise the original value of A _i is used as its type 1 filtering prediction value and go to step S4; S3, the current maximum value The gradient is added to the predicted value of the first-type filtering of A _i-1 , and the result is used as the predicted value of the first-type filtering of A _i , and the current maximum gradient is the gradient with the largest absolute value of the gradient among the four consecutive predicted values of the first-type filtering before A _i . value; S4, output the first-type filtering predicted value of A _i .

其中i≤n≤i+2，D_n-1为所述数列中A_n之前紧邻的数据点，k为可设定的一个系数，0.3≤k≤0.8，本实施例中k＝0.5，则判定A_n为跳点，否则A_n为非跳点。Optionally, the jump point determination method is: the i-th type-1 filtering, the five predicted values of the type-1 filtering before A _i and the original output values of the two consecutive sensors after A _i and A _i according to their respective correspondences. The time sequence of the appearance of the original output values of the sensor is composed of a sequence, and the gradient of each data point is calculated. If the following two conditions are met at the same time: Condition ₁ , the absolute value of the gradient of An _is greater than the maximum absolute value of the gradient of the M data points before An. value, condition 2, the absolute value of the gradient of An is greater _{than the maximum value of error offset1 and error offset2 , where}

where i≤n≤i+2, D _n-1 is the data point immediately before A _n in the sequence, k is a settable coefficient, 0.3≤k≤0.8, in this embodiment k=0.5, then It is determined that _{An is} a jump point, otherwise _{An is} a non-jump point.

The 0-drift value of the BMI160 sensor of BOSCH company is 40mg. The 0-drift value of each type of sensor of each company can refer to its specification book, or can be determined by actual measurement.

The meaning of this setting is that the output value of the sensor, especially the output value of the motion sensor, in the time corresponding to 1+N data points, it is impossible to have a momentary change in motion followed by a slow change process; jumping point It does not mean that it is a wrong point, it may be the beginning of the next movement, so it is not the same movement state as the previous points.

Such a type of filter delays the time corresponding to the two data points to output the type-one filter compensation value of the original output value of the sensor; taking the sampling time of 5ms as an example, regardless of the calculation time of the filter itself, the actual filter delay is 10ms, Realize the technical effect of filtering abnormal jump points in near real time.

FIG. 2 is a block diagram of a filter corresponding to a type of filtering method.

The exemplary explanation of this embodiment takes M=5, N=2 as an example for illustration, and the data points to be filtered when the filter performs one-type filtering need to refer to the five type-one filtering prediction values before it and the following 2 raw sensor output value.

Those skilled in the art can use hardware circuits to implement the functions of each module of the filter, and can also use software to implement the functions of each module of the filter.

The filter 201 includes an input end 202 , an output storage module 203 , and a type-1 filtering module 204 .

The raw data 100 of the sensor enters the filter 201 through the input terminal 202, and can be stored in an independent memory; it can also be assigned to the type-1 filter output storage module 203, and after each type-1 filter is performed, it is determined whether to correct the type-1 filter output storage. A data point in module 203.

The type-1 filter output storage module 203 is connected to the type-1 filter control module 207. The minimum data size of the type-1 filter output storage module 203 is 3, that is, it saves the latest 3 original output values of the sensors to be filtered by type-1, and performs type-1 filtering. After filtering, output the first type-1 filtering predicted value of the original output value of the sensor to be type-1 filtering; there may be other output control modules after the type-1 filtering output storage module 203, which specifically controls whether the output data of the filter 201 is output in real time .

The type-1 filtering control module 207 determines that the original output values of the five sensors received first need not be corrected, and directly outputs or does not output the reference data for subsequent type-1 filtering only.

Starting from the first type-1 filtering, each time the type-1 filtering is performed, the type-1 filtering control module 207 drives the jump point determination module 206 to determine whether the original output values of the latest three sensors to be subjected to the type-1 filtering are jump points or non-jump points, and mark the judgment result in the jump point marking module 205; exemplarily, the jump point is marked as 1, and the non-jump point is marked as 0; the jump point judgment module uses the jump point judgment method described above when performing the jump point judgment, or Set other judgment methods.

The first-type filtering control module 207 determines that the jump points corresponding to the original output values of the latest three sensors to be subjected to the first-type filtering are marked as 100, 101, and 110, and the preceding numbers correspond to the original output values of the sensors that appeared earlier in time, that is, a jump point There are non-jump points in the original output values of the following two consecutive sensors, and the type-1 filtering control module 207 controls the 4 type-1 filtering predicted values before the original output value of the first sensor to be subjected to type-1 filtering. Among the gradient values, the gradient value with the largest absolute value of the gradient is used as the gradient value of the original output value of the first sensor to be subjected to one-type filtering, and the addition result of the gradient value and the current last one-type filtering prediction value is assigned to The storage unit of the type-1 filtering output storage module corresponding to the original output value of the first sensor to be subjected to type-1 filtering.

Optionally, the marker hopping point marking module 205 is recalculated before the end of the first type filtering, or the marker hopping point marking module 205 can also be calculated at the beginning of each type 1 filtering.

Fig. 3 is a flow chart of an embodiment of 5-point type 2 filtering, and the scale of type 2 filtering can be expanded to larger scales such as 6-point filtering and 7-point filtering according to actual needs; the flow chart of the embodiment of the second type filtering The figure is not only an explanation of the type 2 filtering method, wherein each module can also be understood as a circuit module of hardware or a program module of software.

Type 2 filtering can be implemented alone, or after type 1 filtering, the corresponding filter can implement type 2 filtering alone, or type 2 filtering can be implemented after type 1 filtering.

In this embodiment, the type 2 filtering is a quadratic function fitting filtering, and other forms of filtering may also be used according to the characteristics of the actual data.

Taking 5 points as the research object, if the sampling interval is 6ms and the research time is 24ms, it is impossible to complete the movement once in such a short period of time, and it is shown on the image as follows: the curve formed by these 5 points can only be All the way down or all the way up, there may be up and down or down and up at most once, it is impossible to appear down, up, down or up, down, up, etc. Mathematically, it is called at most one inflection point; when there are more than one inflection point When the inflection point is reached, it means that the value of some points inside is inaccurate, that is, there is a burr phenomenon.

Use the quadratic function for fitting, because the quadratic function has only one inflection point. At the non-inflection point, because it is more accurate, it is hoped that the fitted curve passes through such a point, but not the real inflection point, and the least squares method is used for processing. .

The mathematical way that the function passes through the non-inflection points is weighting. For example, 5 points are interpolated between the non-inflection points, and the quadratic function fitting is performed on these 10 data points. Because the inserted 5 points are located in 2 adjacent non-inflection points. Between the inflection points, the probability of the fitting curve passing through or close to these two non-inflection points is greatly increased, thereby improving the accuracy of the interpolation and conforming to the physical expression process.

According to actual needs, 3 points, 4 or 6 points can also be inserted between the non-inflection points. There is no limit to the number, but the scale of the calculation amount needs to be considered, and the five output values of the one-type filter are not interpolated.

The value of the interpolation point is between two adjacent non-inflection points, and it can be an equidistant relationship or other models. In actual operation, the implementer can choose according to the specific situation.

In the specific implementation, first enter the module 301 to update the data of the latest five real values, and then judge the number of inflection points according to the number of transitions of the gradient sign. If the number of inflection points is equal to 0, then the five point values are in the same direction, The data value is always rising or falling, which is normal, enter module 307 for output; if the number of inflection points is 1, it means that there is a new starting point of movement within 24ms, which is also normal, enter module 307 for output Output; if the number of inflection points is greater than or equal to 2, it means that there is at least one complete movement within 24ms. For the sensor applied to the VR handle, this is obviously unconventional, so the correction of the fitting filter should be performed.

The data that needs to be filtered enters module 303, and 5 new data points are interpolated between adjacent non-inflection points. The 5 new data points and their adjacent non-inflection points are arithmetic progressions. For all the above 10 data points Perform the quadratic function fitting of the least squares method, and the fitting values corresponding to the original output values of the five sensors after fitting are taken as the output values of their respective two-type filtering.

Finally, enter the module 304 to output the filtered data.

Figures 4 and 5 show actual filtering results for one embodiment of a type-one filtering.

There are 2 consecutive jump points at the top of Figure 4 and 1 jump point at the bottom. The red curve is the result after filtering, and it can be seen that the jump points are filtered out.

There are 6 consecutive jump points at the top of Figure 5. According to the setting of the first-type filter, if the continuous jump point exceeds 3, it is no longer an error, but a new motion. Therefore, as shown in the red curve, these 6 points are save.

In this embodiment, the first type of filtering is set to 3 points. In practical applications, it needs to be analyzed according to the specific situation. Setting 8 and 10 points, etc., all meet the calculation requirements.

Figure 6, Figure 7, and Figure 8 are comparisons of three cases after fitting and filtering.

Before fitting filtering, the inflection point calculation of the nearest 5 points must be performed. When the number of inflection points is greater than or equal to 2, fitting filtering will be performed.

The data points in Figure 6 keep rising, the number of inflection points is 0, and no fitting filter is required, so the red curve after fitting and the black point before fitting are completely coincident; Figure 7 has only one inflection point, and the present invention believes that this may be another movement In the beginning of 24ms, it conforms to the law of motion, and does not need fitting filtering; in Figure 8, the number of inflection points is 2, and the first point and the second point obtained after calculation are points far away from the inflection point, and the present invention considers them to be true , their values should be changed as little as possible, so these 2 points need to be weighted and emphasized when fitting and filtering. The result after fitting is shown in the red curve. The values of the first 2 points are retained, and the last 3 points are points are obtained by fitting a quadratic curve.

The invention relates to a set of new low-latency near-real-time filtering method. After two-step filtering, larger jump points and smaller continuous burr data can be filtered out to form stable data that can be used for attitude settlement. Depending on the characteristics, each parameter can be adjusted.

In the first type of filtering, the biggest advantage of the present invention is that it combines the attitude calculation algorithm and the sensor characteristics, and sets two thresholds of gradient and error _offset , which are well explained in mathematics and physics. And according to the actual sampling time interval of 6ms, the number of continuous jumping points to become credible new motion points is 3, and the parameter '3' can be adjusted. In this way, a filtering that is judged in a physical sense is realized.

When fitting filtering, the method of using weighting to retain the points less affected by the inflection point is also proposed for the first time.

The method of fitting weighting is to interpolate between the points that need to be weighted, insert 3-8 more gradient points, and bring these gradient points into the fitting equation as the points to be fitted.

The advantage of this fitting method is that compared with other smoothing methods, the value of the real point obtained by judgment is preserved to the greatest extent, and the value of the real point is not affected by the error point and becomes unrealistic.

The filters described in the present invention may be hardware circuits and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the present invention.

A computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above. Computer-readable storage media, as used herein, are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.

The computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, or to an external computer or external storage device over a network such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .

The computer program instructions for carrying out the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages. Source or object code, written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through the Internet connect). In some embodiments, custom electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can be personalized by utilizing state information of computer readable program instructions. Computer readable program instructions are executed to implement various aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer or other programmable data processing apparatus to produce a machine that causes the instructions when executed by the processor of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams. These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium on which the instructions are stored includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.

Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more functions for implementing the specified logical function(s) executable instructions. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation in hardware, implementation in software, and implementation in a combination of software and hardware are all equivalent.

Various embodiments of the present invention have been described above, and the foregoing descriptions are exemplary, not exhaustive, and not limiting of the disclosed embodiments. Numerous modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims (8)

1. A filtering method for a sensor, characterized in that, the original output value of the sensor is subjected to type-1 filtering, and the i-th type-1 filtering outputs the type-1 filtering predicted value corresponding to the original output value A _i of the sensor, i≥1, A The first-type filtering predicted values of the original output values of the M continuous sensors before ₁ are defaulted to their respective corresponding sensor original output values, M≥3, and the steps of the i-th type-1 filtering include: S1, according to the jump point determination method, determine that the original output values of A _i and subsequent N consecutive sensors are jump points or non-jump points, N≥1, and go to step S2; S2, if A _i is a jump point and there is a non-jump point in the original output values of the consecutive N sensors after A _i , go to step S3, otherwise the original value of A _i is used as its type 1 filtering prediction value; S3, the current maximum gradient is added to the predicted value of the first-type filtering of A _i-1 , and the result is taken as the predicted value of the first-type filtering of A _i , The current maximum gradient is the gradient value with the largest absolute value of the gradient in the M continuous M one-type filter prediction values before A _i ; S4, output the first-type filtering predicted value of A _i , Wherein, the jump point determination method is: the i-th type-1 filtering, the M prediction values of the type-1 filtering before A _i and A _i and the original output values of consecutive N sensors after A _i according to their corresponding The time sequence of the appearance of the original output values of the sensor forms a sequence, and the gradient of each data point is calculated, if the following two conditions are met at the same time: Condition ₁ , the absolute value of the gradient of An _is greater than the maximum value of the absolute value of the gradient of the M data points before An, Condition 2, the absolute value of the gradient of An is greater _{than the maximum value of error offset1 and error offset2 , where}

where i≤n≤i+N, D _n-1 is the data point immediately before A _n in the sequence, k is a settable coefficient, 0.3≤k≤0.8, Then it is determined that _{An is} a jump point, otherwise _{An is} a non-jump point. 2. The filter method of the sensor according to claim 1, wherein, According to the two-type filtering judgment rule, perform two-type filtering on consecutive Q first-type filtering predicted values, and output their corresponding two-type filtering output values, Q≥5, The two-type filtering judgment rule is: When there are two or more inflection points in the consecutive Q first-type filtering predicted values, perform second-type filtering on the consecutive Q first-type filtering predicted values; Otherwise, the type-2 filtering output values of the Q consecutive type-1 filtering prediction values are their respective type-1 filtering prediction values. 3. The filter method of the sensor according to claim 2, characterized in that The second-type filtering is a quadratic function fitting filter, and several gradient data points are inserted between the non-inflection points and the non-inflection points of the Q consecutive predicted values of the first-type filtering; Perform quadratic function fitting on all the gradient data points inserted in the Q one-type filtering predicted values and the Q one-type filtering predicted values, and the Q one-type filtering predicted values obtained after fitting correspond to The fitted values are taken as their respective corresponding Type 2 filter output values. 4. The filter method of the sensor according to claim 3, wherein, The numerical values of the several gradient data points and their adjacent non-inflection points form an arithmetic progression. 5. A filter, characterized in that, It includes an input end, a type-1 filter output storage module and a type-1 filter module; The type-1 filtering module includes a jumping point marking module, a jumping point judging module and a type-1 filtering control module; the input end is connected to the type-1 filter control module; The type-1 filter output storage module is connected to the type-1 filter control module, and the type-1 filter output storage module is set to store the output value of the filter; The jumping point determination module is connected with the first-type filtering control module, and the jumping-point determining module is set to determine whether the data points in the first-type filtering output storage module are jumping points or non-jumping points; The jumping point marking module is connected with the jumping point judging module, and the jumping point marking module is set to mark the data point in the first-type filtering output storage module according to the judgment result of the jumping point judging module. Jump point or non-jump point; The type 1 filtering control module is also connected with the jumping point marking module, and the type 1 filtering control module is set to: It is determined that the data point to be filtered in the type-1 filtering output storage module is marked as a jump point in the corresponding jump point marking module, and consecutive N data points after the data point are marked as non-jump points in the corresponding jump point marking module, Then the gradient value of the data point with the largest absolute gradient value among the M data points before the data point to be filtered is added to the previous data point of the data point to be filtered, and the result is reassigned to the data point to be filtered, M ≥3, N≥1, Otherwise, the value of the one-type filtering output storage module remains unchanged, In the initial state, the first M data points at the input end are determined as non-jumping points, Wherein, the jump point determination module is set so that if the data point in the type-1 filter output storage module satisfies the following two conditions at the same time, it is determined that the corresponding mark of the data point in the jump point mark module is a jump point , otherwise the data point is marked as a non-jump point in the jump point marking module, Condition 1, the absolute value of the gradient of the data point is greater than the maximum value of the absolute value of the gradient of the M data points before the data point, M≥3; Condition 2, the absolute value of the gradient of the data point is greater than the maximum value of error _offset1 and error _offset2 , where

Where k is a settable coefficient, 0.3≤k≤0.8. 6. The filter according to claim 5, wherein the filter further comprises a type 2 filter module, the type 2 filter module is connected with the type 1 filter storage module, and the type 2 filter module is connected to the type 1 filter storage module. is set to perform type-2 filtering on consecutive Q data points in the type-1 filtering output storage module, and output corresponding Q type-2 filtering output values, Q≥5 The type-2 filter module includes a type-2 filter input terminal, a type-2 filter determination module, a type-2 filter compensation module and a type-2 filter output terminal, The type-2 filter determination module is connected to the type-2 filter input terminal, the type-2 filter output terminal and the type-2 filter compensation module, and the type-2 filter determination module is configured to determine the type-2 filter If the number of inflection points of the Q data points to be filtered by the input terminal is less than 2, the original value of the Q data points is output to the output terminal of the type 2 filter; otherwise, the Q data points are filtered by the type 2 filter. The compensation module performs the type-2 filter compensation and outputs it to the output terminal of the type-2 filter. The type-2 filter compensation module is further connected to the type-2 filter output terminal, and the type-2 filter compensation module is configured to perform type-2 filter compensation on the Q data points and output to the type-2 filter output terminal. 7. The filter of claim 6, wherein The type II filter compensation module is set to interpolate several gradient values between the non-inflection points and the non-inflection points of the Q data points, and perform quadratic function fitting on all the gradient values and the Q data points, The fitting values corresponding to the Q data points obtained after fitting are output to the output terminal of the secondary filter. 8 . The filter according to claim 7 , wherein the numerical values of the several gradient values and their adjacent non-inflection points are arithmetic progressions in numerical value. 9 .

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