CN118114498A - Data filtering method, device, computer equipment, storage medium and program product - Google Patents

Abstract

The invention relates to the technical field of permanent magnet synchronous motors, and discloses a data filtering method, a device, computer equipment, a storage medium and a program product, wherein the data filtering method is applied to a Kalman filter and comprises the following steps: acquiring motion state data of a target vehicle in running, wherein the motion state data comprise motion state data of a first moment and a second moment; determining a first prior estimated covariance of the current moment based on the motion state data; and filtering the first priori estimation data at the current moment based on the first priori estimation covariance to obtain the optimal estimation of the motion state data at the current moment. Based on the method, the process noise Q can be not predicted, but the first priori estimated covariance of the current moment is determined through the motion state data of the first moment and the second moment, so that the accuracy of the optimal estimation of the motion state data of the current moment is improved on the basis of getting rid of the dependence on the accuracy Q.

Description

Data filtering method, device, computer equipment, storage medium and program product

Technical Field

The present invention relates to the technical field of permanent magnet synchronous motors, and in particular to a data filtering method, device, computer equipment, storage medium and program product.

Background technique

In the process of developing new energy vehicles, the motors used are usually permanent magnet synchronous motors, which reduce vehicle energy consumption and extend the battery life. However, considering that the field-oriented vector control method of the permanent magnet synchronous motor drive will generate positive and negative sixth harmonic currents in the synchronous reference system, it leads to torque fluctuations and deterioration of control performance.

Based on this, Kalman filters are usually used in the field of new energy vehicles to filter out harmonic currents. Generally speaking, the filtering effect of the Kalman filter depends on the prediction of the process noise Q. However, during the driving of the car, the road conditions cannot be predicted, so the accurate process noise Q cannot be obtained, which affects the prediction accuracy of the Kalman filter.

Summary of the invention

In view of this, the present invention provides a data filtering method, device, computer equipment, storage medium and program product to solve the problem that the accurate process noise Q cannot be determined during the driving of new energy vehicles, thereby affecting the prediction accuracy of the Kalman filter.

In a first aspect, the present invention provides a data filtering method, applied to a Kalman filter, the method comprising:

Acquire motion state data of the target vehicle during driving, wherein the motion state data includes motion state data at a first moment and a second moment;

Determine a first a priori estimation covariance at a current moment based on the motion state data, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimation covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori;

The first a priori estimation data at the current moment is filtered based on the first a priori estimation covariance to obtain an optimal estimate of the motion state data at the current moment, wherein the first a priori estimation data is used to indicate a priori estimation of the motion state data at the current moment.

In an optional implementation, determining the first a priori estimated covariance at the current moment based on the motion state data includes:

Determine, based on the motion state data, a priori adjustment amount corresponding to the first moment, wherein the priori adjustment amount is used to indicate an adjustment amount when correcting an error of the second priori estimate covariance at the first moment;

The first a priori estimation covariance at the current moment is determined according to the sum of the a priori adjustment amount and the second a priori estimation covariance.

In an embodiment of the present disclosure, in the relevant Kalman filter algorithm, noise Q is required to calculate the first prior estimate covariance corresponding to the current moment k. However, in the present disclosure, noise Q is discarded when calculating the first prior estimate covariance, thereby improving the accuracy of the determined first prior estimate covariance, and further improving the accuracy of the optimal estimate of the motion state data at the current moment.

In an optional implementation, the motion state data at the first moment includes a posteriori state data corresponding to the first moment, wherein the a posteriori state data is used to indicate the optimal estimate corresponding to the first moment;

Determining a priori adjustment amount corresponding to a first moment based on the motion state data includes:

Obtaining a second a priori estimated covariance at the first moment that is determined in advance based on the motion state data at the second moment;

Determine a posterior residual corresponding to the first moment based on a difference between the posterior state data and the second a priori estimated data;

The second a priori estimated covariance is corrected based on the posterior residual to obtain a priori adjustment amount corresponding to the first moment.

In the embodiment of the present disclosure, the prior adjustment amount corresponding to the first moment can be calculated based on the operation data corresponding to the first moment and the second moment, so as to calculate the first prior estimated covariance corresponding to the current moment k based on the prior adjustment amount, thereby getting rid of the dependence on noise Q in the calculation process.

In an optional implementation, filtering the first a priori estimation data at the current moment based on the first a priori estimation covariance to obtain the optimal estimate at the current moment includes:

Acquire a posteriori information of the target vehicle, and determine the a posteriori state data corresponding to the motion state data at the first moment according to the a posteriori information;

Generate first a priori estimation data at the current moment according to the a priori state data;

The Kalman filter matrix corresponding to the current moment is determined according to the first a priori estimate covariance, and the first a priori estimate data is filtered according to the Kalman filter matrix to obtain the optimal estimate at the current moment.

In the embodiment of the present disclosure, first a priori estimation data at the current moment can be generated based on the a priori state data corresponding to the first moment, and the first prior estimation data can be filtered according to the Kalman filter matrix corresponding to the current moment, so as to filter out the influence of noise on the first prior estimation data as much as possible.

In an optional implementation, generating first a priori estimation data at the current moment according to the a priori state data includes:

Obtaining a target state dynamic model corresponding to the target vehicle, wherein the target state dynamic model is used to predict the motion state data of the target vehicle at each moment;

Predicting the motion state data at the first moment according to the target state dynamic model to obtain a prediction result;

The prediction result is adjusted based on the posterior state data to obtain the first a priori estimation data at the current moment.

In the embodiment of the present disclosure, the prediction result of the motion state data at the first moment can be adjusted based on the posterior state data at the first moment to obtain the first a priori estimation data at the current moment, thereby providing a technical basis for subsequently calculating the optimal estimate of the current moment based on the first a priori estimation data.

In an optional implementation, filtering the first a priori estimation data according to the Kalman filter matrix to obtain an optimal estimation of the motion state data at the current moment includes:

Obtaining angle information of the target vehicle at the current moment, and calculating the residual between the angle information and the first a priori estimation data;

The first a priori estimation data is filtered based on the Kalman filter matrix and the residual to obtain an optimal estimation of the motion state data at the current moment.

In an optional implementation, the optimal estimation includes: estimated values of angular velocity information and angle information of the target vehicle at the current moment.

In the disclosed embodiment, the residual between the angle information at the current moment and the first a priori estimation data can be calculated, and the first a priori estimation data can be corrected based on the residual to obtain the optimal estimate corresponding to the current moment, thereby improving the accuracy of the optimal estimate.

In a second aspect, the present invention provides a data filtering device, the device comprising:

An acquisition module is used to acquire motion state data of the target vehicle during driving, wherein the motion state data includes motion state data at a first moment and a second moment;

A determination module, used to determine a first a priori estimation covariance at a current moment based on the motion state data, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimation covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori;

A filtering module is used to filter the first prior estimation data at the current moment based on the first prior estimation covariance to obtain the optimal estimate of the motion state data at the current moment, wherein the first prior estimation data is used to indicate the prior estimation of the motion state data at the current moment.

In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the data filtering method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the data filtering method of the first aspect or any corresponding embodiment thereof.

In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the data filtering method of the first aspect or any corresponding embodiment thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of a data filtering method according to an embodiment of the present invention;

FIG2 is a schematic flow chart of another data filtering method according to an embodiment of the present invention;

FIG3 is a schematic flow chart of another data filtering method according to an embodiment of the present invention;

FIG4 is a calculation block diagram of a Kalman filter of another data filtering method according to an embodiment of the present invention;

FIG5 is a structural block diagram of a data filtering device according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

In conjunction with the application scenarios on which the execution of the data filtering method depends, the application scenarios are described here.

In the process of developing new energy vehicles, the motors used are usually permanent magnet synchronous motors, which reduce vehicle energy consumption and extend the battery life. However, considering that the field-oriented vector control method of the permanent magnet synchronous motor drive will generate positive and negative sixth harmonic currents in the synchronous reference system, it leads to torque fluctuations and deterioration of control performance.

Based on this, Kalman filters are usually used in the field of new energy vehicles to filter out harmonic currents. Generally speaking, the filtering effect of the Kalman filter depends on the prediction of the process noise Q. However, during the driving of the car, the road conditions cannot be predicted, so the accurate process noise Q cannot be obtained, which affects the prediction accuracy of the Kalman filter.

For example, the algorithm in the relevant Kalman filter is as follows:

The time update equation of the Kalman filter is as follows:

The state update equation of the Kalman filter is as follows:

in, and/> They represent the posterior state estimates at time k-1 and time k respectively, which are one of the results of filtering, that is, the updated result, also called the optimal estimate (the estimated state, it is theoretically impossible to measure the exact result of the state at each moment in real time, so it is called an estimate).

It represents the prior state estimate at time k, which is the intermediate calculation result of filtering, that is, the result of time k predicted based on the optimal estimate at the previous time (time k-1), and is the result of the prediction equation.

P _K represents the posterior estimated covariance at time k (i.e. The covariance of , representing the uncertainty of the state), is one of the results of filtering.

H represents the conversion matrix from state variables to measurements (observations), which represents the relationship connecting the state and observations. In Kalman filtering, it is a linear relationship. It is responsible for converting m-dimensional measurements to n-dimensional ones to conform to the mathematical form of state variables, and is one of the prerequisites for filtering.

z _k represents the measurement value (observation value) and is the input of the filter.

K _k represents the filter gain matrix, which is the intermediate calculation result of the filter, the Kalman gain, or the Kalman coefficient.

A represents the state transfer matrix, which is actually a conjecture model of the target state transition. For example, in maneuvering target tracking, the state transfer matrix is often used to model the target's motion, which may be uniform linear motion or uniformly accelerated motion. When the state transfer matrix does not conform to the target's state transition model, the filter will diverge quickly.

Q represents the process excitation noise covariance (covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. Because we cannot directly observe the process signal, the value of Q is difficult to determine. It is the state variable used by the Kalman filter to estimate the discrete time process, also known as the noise brought by the prediction model itself.

R represents the measurement noise covariance. When the filter is actually implemented, the measurement noise covariance R can generally be observed and is a known condition of the filter.

B represents the matrix that transforms input into state.

It represents the residual between the actual observation and the predicted observation, and together with the Kalman gain, it corrects the prior (prediction) to obtain the posterior.

The embodiment of the present invention provides a data filtering method, which can first obtain the motion state data of the target vehicle during driving, wherein the motion state data includes the motion state data at the first moment and the second moment, and then, based on the motion state data, determine the first a priori estimated covariance at the current moment, wherein the current moment is the moment after the first moment and the second moment, and the first a priori estimated covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori. Next, the first a priori estimated data at the current moment can be filtered based on the first a priori estimated covariance to obtain the optimal estimate of the motion state data at the current moment, wherein the first a priori estimated data is used to indicate the a priori estimate of the motion state data at the current moment. Based on this, the present disclosure can determine the first a priori estimated covariance of the current moment through the motion state data at the first moment and the second moment without predicting the process noise Q, thereby improving the accuracy of the optimal estimate of the motion state data at the current moment on the basis of getting rid of the dependence on accurate Q.

According to an embodiment of the present invention, an embodiment of a data filtering method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

In this embodiment, a data filtering method is provided, which can be used in the driving control system of the new energy vehicle. Specifically, it can be applied to the Kalman filter. FIG. 1 is a flow chart of a data filtering method according to an embodiment of the present invention. As shown in FIG. 1 , the process includes the following steps:

Step S101, obtaining motion state data of a target vehicle during driving, wherein the motion state data includes motion state data at a first moment and a second moment.

In the embodiment of the present disclosure, the above-mentioned motion state data may include the angular velocity ω _e and the angle θ of the target vehicle during operation. The motion state data x can be expressed as a state vector When the angular velocity ω _e is determined, the travel speed v and the turning radius r of the target vehicle can be obtained, where ω _e =v/r. At the same time, the angle θ can be obtained based on the angle sensor installed at the wheel position of the target vehicle.

When acquiring the above motion state data, the motion state data of the first moment and the second moment can be acquired respectively. It should be understood that the data filtering process in the present disclosure is performed in real time during the driving process of the target vehicle. Relative to the current moment k, the first moment k-1 is the moment before k, and the second moment k-2 is the moment before k-1. Specifically, the interval between moments can be seconds, milliseconds, etc., which is subject to actual use requirements, and the present disclosure does not limit this.

Step S102, based on the above motion state data, determine the first a priori estimated covariance of the current moment, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimated covariance is used to indicate the covariance of the error when performing a priori estimation on the motion state data at the current moment.

In the disclosed embodiment, it should be understood that the Kalman filter algorithm is divided into two steps: state prediction and state update, wherein the state prediction is used to predict the first a priori estimated covariance P _k|k-1 at the current moment k, and the state update is used to predict the motion state at the current moment based on the motion state of the target vehicle at the previous moment, specifically including: filtering the first a priori estimated data at the current moment based on the first a priori estimated covariance to obtain the optimal estimate of the motion state data at the current moment k. The specific method for determining the first a priori estimated covariance is as follows and will not be repeated here.

Step S103, filtering the first a priori estimation data at the current moment based on the first a priori estimation covariance to obtain an optimal estimate of the motion state data at the current moment, wherein the first a priori estimation data is used to indicate a priori estimation of the motion state data at the current moment.

In the disclosed embodiment, it can be seen from the above that the Kalman filter algorithm includes state prediction and state update, wherein the state update is used to predict the motion state of the target vehicle at the current moment according to the motion state of the target vehicle at the previous moment. When performing the state update, it includes: filtering the first a priori estimation data at the current moment based on the first a priori estimation covariance to obtain the optimal estimate of the k motion state data at the current moment.

Specifically, when performing the above state update, the first a priori estimated data corresponding to the current time k can be determined. It should be understood that the first a priori estimation data often has a certain deviation. Therefore, the first a priori estimation data can be filtered by the first a priori estimation covariance P _k|k-1 at the current moment, so as to obtain the optimal estimation of the motion state data at the current moment. It should be understood that the /> It can be used as the optimal estimate of the current moment output by the Kalman filter, and can also be input into the Kalman filter as the posterior state data of the current moment, participating in the calculation process of the optimal estimate for the next moment k+1.

It can be known from the above description that in the embodiment of the present disclosure, first, the motion state data of the target vehicle during driving can be obtained, wherein the motion state data includes the motion state data of the first moment and the second moment, and then, based on the motion state data, the first a priori estimation covariance of the current moment can be determined, wherein the current moment is the moment after the first moment and the second moment, and the first a priori estimation covariance is used to indicate the covariance of the error when the motion state data of the current moment is estimated a priori. Next, the first a priori estimation data of the current moment can be filtered based on the first a priori estimation covariance to obtain the optimal estimate of the motion state data of the current moment, wherein the first a priori estimation data is used to indicate the a priori estimate of the motion state data of the current moment. Based on this, the present disclosure can determine the first a priori estimation covariance of the current moment through the motion state data of the first moment and the second moment without predicting the process noise Q, thereby improving the accuracy of the optimal estimate of the motion state data of the current moment on the basis of getting rid of the dependence on the precise Q.

In this embodiment, another data filtering method is provided, which can be used for the driving control system of the new energy vehicle. Specifically, it can be applied to the Kalman filter. FIG. 2 is a flow chart of the data filtering method according to an embodiment of the present invention. As shown in FIG. 2, the process includes the following steps:

Step S201, obtaining the motion state data of the target vehicle during driving, wherein the motion state data includes the motion state data at the first moment and the second moment. Please refer to step S101 of the embodiment shown in FIG1 for details, which will not be repeated here.

Step S202, based on the above motion state data, determine the first a priori estimated covariance of the current moment, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimated covariance is used to indicate the covariance of the error when performing a priori estimation on the motion state data at the current moment.

Specifically, the above step S202 includes:

Step S2021, based on the motion state data at the first moment and the second moment, determine the a priori adjustment amount corresponding to the first moment, wherein the a priori adjustment amount is used to indicate the adjustment amount when correcting the error of the second a priori estimate covariance at the first moment.

Step S2022: determining the first a priori estimated covariance at the current moment according to the sum of the a priori adjustment amount and the second a priori estimated covariance.

In the embodiment of the present disclosure, the a priori adjustment amount can be recorded as ΔP _k-1 , and the first a priori estimated covariance at the current moment can be recorded as P _k|k-1 . Specifically,

P _k|k-1 =P _k-1|k-2 +ΔP _k-1

Wherein, P _k-1|k-2 is the second prior estimation covariance corresponding to the first moment k-1.

Here, when determining the second a priori estimated covariance P _k-1|k-2 , it can be determined based on the a priori adjustment amount corresponding to the second moment, and the a priori adjustment amount can be determined based on the a posteriori state data corresponding to the motion state data at the second moment And the third prior estimation data/> The specific method of determining the prior adjustment amount at the second moment is as described in the following embodiment of determining the prior adjustment amount corresponding to the first moment, and will not be repeated here.

Step S203, based on the first a priori estimation covariance, the first a priori estimation data at the current moment is filtered to obtain the optimal estimation of the motion state data at the current moment, wherein the first a priori estimation data is used to indicate the a priori estimation of the motion state data at the current moment. For details, please refer to step S101 of the embodiment shown in FIG1 , which will not be described in detail here.

In the embodiment of the present disclosure, it can be seen from the above that in the relevant Kalman filter algorithm, noise Q is needed to calculate the first prior estimate covariance corresponding to the current moment k, while in the present disclosure, noise Q is discarded when calculating the first prior estimate covariance, thereby improving the accuracy of the determined first prior estimate covariance, and further improving the accuracy of the optimal estimate of the motion state data at the current moment.

In some optional implementations, the motion state data at the first moment includes a posteriori state data corresponding to the first moment, wherein the a posteriori state data is used to indicate the optimal estimate corresponding to the first moment, and the above step S2021 includes:

Step a1, obtaining a second a priori estimated covariance at the first moment that is determined in advance based on the motion state data at the second moment.

Step a2: determining the a posteriori residual corresponding to the first moment based on the difference between the a posteriori state data and the second a priori estimated data.

Step a3: correcting the second a priori estimated covariance based on the a priori residual to obtain a priori adjustment amount corresponding to the first moment.

In the embodiment of the present disclosure, the a posteriori state data in the motion state data at the first moment can be recorded as The second priori estimation data can be recorded as/> Based on this, the posterior residual corresponding to the first moment

Next, the second a priori estimated covariance can be corrected according to the above a posteriori parameter Δx _k-1 to obtain the a priori adjustment amount ΔP _k-1 corresponding to the first moment, specifically:

in, is the transposed matrix, K _k-1 is the Kalman filter matrix corresponding to the k-1 moment, P _k-1|k-2 is the prior estimated covariance corresponding to the first moment k-1, and H is the state variable to measurement conversion matrix, which represents the relationship connecting the state and observation. It is responsible for converting the m-dimensional measurement value to the n-dimensional one so that it conforms to the mathematical form of the state variable. It is one of the known filtering prerequisites in the Kalman filter.

Specifically, the method for determining the above K _k-1 is as described in the embodiment of determining the Kalman filter matrix corresponding to the current time k below, and will not be repeated here. It should be understood that the above P _k-1|k-2 is the posterior state data corresponding to the motion state data at the second time k-2. And the third prior estimation data/> Therefore, in the previous step at the current moment, the / > With/> After that, you can put the /> With/> Pre-stored in the cache space so that it can be directly read in subsequent operations.

In the embodiment of the present disclosure, the prior adjustment amount corresponding to the first moment can be calculated based on the operation data corresponding to the first moment and the second moment, so as to calculate the first prior estimated covariance corresponding to the current moment k based on the prior adjustment amount, thereby getting rid of the dependence on noise Q in the calculation process.

In this embodiment, another data filtering method is provided, which can be used for the driving control system of the new energy vehicle. Specifically, it can be applied to the Kalman filter. FIG3 is a flow chart of the data filtering method according to an embodiment of the present invention. As shown in FIG3, the process includes the following steps:

Step S301, obtaining the motion state data of the target vehicle during driving, wherein the motion state data includes the motion state data at the first moment and the second moment. Please refer to step S101 of the embodiment shown in FIG1 for details, which will not be repeated here.

Step S302, based on the above motion state data, determine the first a priori estimated covariance of the current moment, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimated covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori. For details, please refer to step S102 of the embodiment shown in Figure 1, which will not be repeated here.

Step S303: filtering the first a priori estimation data at the current moment based on the first a priori estimation covariance to obtain the optimal estimation at the current moment.

Specifically, the above step S303 includes:

Step S3031, obtaining a posteriori information of the target vehicle, and determining the a posteriori state data corresponding to the motion state data at the first moment according to the a posteriori information.

Step S3032, generating first a priori estimation data at the current moment according to the a priori state data.

Step S3033, determining the Kalman filter matrix corresponding to the current moment according to the first a priori estimate covariance, and filtering the first a priori estimate data according to the Kalman filter matrix to obtain the optimal estimate at the current moment.

In the disclosed embodiment, the above-mentioned posterior information may be the optimal estimate output by the Kalman filter when predicting each moment before the current moment, wherein the optimal estimate at each moment may be used as the posterior state data for calculating the optimal estimate at subsequent moments.

After determining the first a priori estimation data Afterwards, based on this Determine the Kalman filter matrix K _k at the current moment, specifically:

K _k = P _k|k-1 H[P _k|k-1 H ^T + R] ^-1

Wherein, H is as described above and will not be described again here. R is the measurement noise covariance. When the Kalman filter is actually implemented, the measurement noise covariance R can generally be observed and is a known condition of the filter.

In the embodiment of the present disclosure, first a priori estimation data at the current moment can be generated based on the a priori state data corresponding to the first moment, and the first prior estimation data can be filtered according to the Kalman filter matrix corresponding to the current moment, so as to filter out the influence of noise on the first prior estimation data as much as possible.

In some optional implementations, the above step S3032 includes:

Step b1, obtaining a target state dynamic model corresponding to the target vehicle, wherein the target state dynamic model is used to predict the motion state data of the target vehicle at each moment.

Step b2: predicting the motion state data at the first moment according to the target state dynamic model to obtain a prediction result.

Step b3, adjusting the prediction result based on the posterior state data to obtain the first a priori estimation data at the current moment.

In the embodiment of the present disclosure, the target state dynamic model can be recorded as f(x), and the target state dynamic model is used to predict the motion state data at the first time k-1 to obtain the prediction result Then, based on the a posteriori state data Δx _k-1 at the first moment, Adjust to obtain the first prior estimation data/> specific:

Among them, T is the transposed matrix, and B is used to convert the input u _k-1 into the state matrix.

In the embodiment of the present disclosure, the prediction result of the motion state data at the first moment can be adjusted based on the posterior state data at the first moment to obtain the first a priori estimation data at the current moment, thereby providing a technical basis for subsequently calculating the optimal estimate of the current moment based on the first a priori estimation data.

In some optional implementations, the above step S3033 includes:

Step c1, obtaining the angle information of the target vehicle at the current moment, and calculating the residual between the angle information and the first prior estimation data.

Step c2, filtering the first priori estimation data based on the Kalman filter matrix and the residual to obtain an optimal estimation of the motion state data at the current moment.

In the embodiment of the present disclosure, the angle information y can be used to indicate the angle of the target vehicle during operation, where y=θ. As can be seen from the above, the angle information θ can be obtained based on the angle sensor installed at the wheel position of the target vehicle. The specific process of calculating the residual of the angle information and the first prior estimation data is as follows:

The residual can be used to represent the residual between the actual data observation and the predicted data observation.

Next, the first prior estimation data can be corrected based on this parameter together with the Kalman filter matrix To obtain the optimal estimate corresponding to the current time k/> The specific correction process is as follows:

In some optional implementations, the above optimal estimate Includes: the estimated value of the angular velocity information and angle information of the target vehicle at the current moment, which can be expressed as a vector/>

In the disclosed embodiment, the residual between the angle information at the current moment and the first a priori estimation data can be calculated, and the first a priori estimation data can be corrected based on the residual to obtain the optimal estimate corresponding to the current moment, thereby improving the accuracy of the optimal estimate.

In this embodiment, another data filtering method is provided, which can be used in the driving control system of the new energy vehicle. Specifically, it can be applied to the Kalman filter. The algorithm in the Kalman filter is as follows:

K _k =P _k|k-1 H[P _k|k-1 H ^T +R] ^-1 (16)

Specifically, the calculation block diagram of the Kalman filter of another data filtering method corresponding to the above algorithm is shown in FIG4 , wherein the input data of the Kalman filter is the a posteriori state data at the first moment k-1 above. The target vehicle's angle information y and the second a priori estimated covariance P _k-1|k-2 corresponding to the first moment, the output data is the optimal estimate of the current moment k/>

In summary, in the embodiment of the present disclosure, the motion state data of the target vehicle during driving can be first obtained, wherein the motion state data includes the motion state data at the first moment and the second moment, and then, based on the motion state data, the first a priori estimated covariance at the current moment can be determined, wherein the current moment is the moment after the first moment and the second moment, and the first a priori estimated covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori. Next, the first a priori estimated data at the current moment can be filtered based on the first a priori estimated covariance to obtain the optimal estimate of the motion state data at the current moment, wherein the first a priori estimated data is used to indicate the a priori estimate of the motion state data at the current moment. Based on this, the present disclosure does not need to predict the process noise Q, but determines the first a priori estimated covariance at the current moment through the motion state data at the first moment and the second moment, thereby improving the accuracy of the optimal estimate of the motion state data at the current moment on the basis of getting rid of the dependence on the precise Q.

In this embodiment, a data filtering device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and/or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

This embodiment provides a data filtering device, as shown in FIG5 , including:

An acquisition module 501 is used to acquire motion state data of a target vehicle during driving, wherein the motion state data includes motion state data at a first moment and a second moment;

A determination module 502 is used to determine a first a priori estimation covariance at a current moment based on the motion state data, wherein the current moment is a moment after the first moment and the second moment, and the first a priori estimation covariance is used to indicate the covariance of the error when the motion state data at the current moment is estimated a priori;

The filtering module 503 is used to filter the first prior estimation data at the current moment based on the first prior estimation covariance to obtain the optimal estimation of the motion state data at the current moment, wherein the first prior estimation data is used to indicate the prior estimation of the motion state data at the current moment.

In some optional implementations, the determining module 502 includes:

A first determining unit, configured to determine a priori adjustment amount corresponding to a first moment based on the motion state data, wherein the priori adjustment amount is used to indicate an adjustment amount when correcting an error of a second priori estimate covariance at the first moment;

The second determining unit is used to determine the first a priori estimated covariance at a current moment according to the sum of the a priori adjustment amount and the second a priori estimated covariance.

In some optional implementations, the motion state data at the first moment includes a posteriori state data corresponding to the first moment, wherein the a posteriori state data is used to indicate the optimal estimate corresponding to the first moment, and the first determining unit includes:

An acquisition subunit, used to acquire a second a priori estimated covariance at the first moment determined in advance based on the motion state data at the second moment;

A first determination subunit, configured to determine a posterior residual corresponding to a first moment based on a difference between the posterior state data and the second a priori estimation data;

The correction subunit is used to correct the second a priori estimated covariance based on the posterior residual to obtain the a priori adjustment amount corresponding to the first moment.

In some optional implementations, the filtering module 503 includes:

An acquisition unit, used to acquire a posteriori information of the target vehicle, and determine the a posteriori state data corresponding to the motion state data at the first moment according to the a posteriori information;

A generating unit, configured to generate first a priori estimation data at a current moment according to the a priori state data;

The filtering unit is used to determine the Kalman filter matrix corresponding to the current moment according to the first prior estimation covariance, and filter the first prior estimation data according to the Kalman filter matrix to obtain the optimal estimate at the current moment.

In some optional implementations, the generating unit includes:

A generating subunit, used for acquiring a target state dynamic model corresponding to the target vehicle, wherein the target state dynamic model is used for predicting the motion state data of the target vehicle at each moment;

A prediction subunit, configured to predict the motion state data at the first moment according to the target state dynamic model to obtain a prediction result;

The adjustment subunit is used to adjust the prediction result based on the posterior state data to obtain the first a priori estimation data at the current moment.

In some optional implementations, the filtering unit includes:

A calculation subunit, used for obtaining the angle information of the target vehicle at the current moment, and calculating the residual between the angle information and the first a priori estimation data;

A filtering subunit is used to filter the first prior estimation data based on the Kalman filter matrix and the residual to obtain an optimal estimate of the motion state data at a current moment.

In some optional implementations, the optimal estimate includes: estimated values of angular velocity information and angle information of the target vehicle at the current moment.

The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

The data filtering device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and/or other devices that can provide the above functions.

An embodiment of the present invention further provides a computer device having the data filtering device shown in FIG. 5 .

Please refer to Figure 6, which is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention. As shown in Figure 6, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input/output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and/or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). In Figure 6, a processor 10 is taken as an example.

The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means, and FIG6 takes the connection via a bus as an example.

The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and/or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims (11)

1. A method of filtering data applied to a kalman filter, the method comprising:

acquiring motion state data of a target vehicle in running, wherein the motion state data comprise motion state data of a first moment and a second moment;

Determining a first priori estimated covariance of a current moment based on the motion state data, wherein the current moment is a moment after the first moment and the second moment, and the first priori estimated covariance is used for indicating covariance of errors when the motion state data of the current moment is estimated a priori;

And filtering the first priori estimation data of the current moment based on the first priori estimation covariance to obtain optimal estimation of the motion state data of the current moment, wherein the first priori estimation data is used for indicating the prior estimation of the motion state data of the current moment.

2. The method of claim 1, wherein the determining a first a priori estimated covariance of a current time instant based on the motion state data comprises:

determining a priori adjustment amount corresponding to a first moment based on the motion state data, wherein the priori adjustment amount is used for indicating an adjustment amount when correcting an error of a second priori estimated covariance of the first moment;

and determining a first prior estimation covariance of the current moment according to the sum of the prior adjustment quantity and the second prior estimation covariance.

3. The method of claim 2, wherein the motion state data at the first time comprises posterior state data corresponding to the first time, wherein the posterior state data is used to indicate an optimal estimate corresponding to the first time;

The determining, based on the motion state data, a priori adjustment corresponding to a first time includes:

acquiring a second prior estimated covariance of the first moment, which is determined in advance based on the motion state data of the second moment;

Determining a posterior residual corresponding to the first moment based on the difference value between the posterior state data and the second prior estimation data;

and correcting the second priori estimated covariance based on the posterior residual error to obtain the priori adjustment corresponding to the first moment.

4. A method according to any one of claims 1 to 3, wherein filtering the first prior estimate data at the current time based on the first prior estimate covariance to obtain an optimal estimate at the current time comprises:

acquiring posterior information of a target vehicle, and determining posterior state data corresponding to the motion state data at the first moment according to the posterior information;

Generating first priori estimated data of the current moment according to the posterior state data;

and determining a Kalman filter matrix corresponding to the current moment according to the first priori estimation covariance, and filtering the first priori estimation data according to the Kalman filter matrix to obtain the optimal estimation of the current moment.

5. The method of claim 4, wherein the generating the first prior estimate data for the current time from the posterior state data comprises:

Acquiring a target state dynamic model corresponding to the target vehicle, wherein the target state dynamic model is used for predicting motion state data of the target vehicle at each moment;

predicting the motion state data at the first moment according to the target state dynamic model to obtain a prediction result;

And adjusting the prediction result based on the posterior state data to obtain first priori estimated data of the current moment.

6. The method of claim 4, wherein filtering the first a priori estimate data based on the kalman filter matrix to obtain an optimal estimate of motion state data at a current time comprises:

Acquiring angle information of the target vehicle at the current moment, and calculating residual errors of the angle information and the first priori estimated data;

and filtering the first priori estimation data based on the Kalman filtering matrix and the residual error to obtain the optimal estimation of the motion state data at the current moment.

7. The method of claim 1, wherein the optimal estimation comprises: and estimating the angular velocity information and the angle information of the target vehicle at the current moment.

8. A data filtering apparatus, the apparatus comprising:

The system comprises an acquisition module, a control module and a control module, wherein the acquisition module acquires motion state data of a target vehicle in running, and the motion state data comprises motion state data of a first moment and a second moment;

The determining module is used for determining a first priori estimated covariance of the current moment based on the motion state data, wherein the current moment is a moment after the first moment and the second moment, and the first priori estimated covariance is used for indicating covariance of errors when the motion state data of the current moment is estimated a priori;

The filtering module is used for filtering the first priori estimation data of the current moment based on the first priori estimation covariance to obtain the optimal estimation of the motion state data of the current moment, wherein the first priori estimation data is used for indicating the prior estimation of the motion state data of the current moment.

9. A computer device, comprising:

a memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the data filtering method of any of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the data filtering method of any of claims 1 to 7.

11. A computer program product comprising computer instructions for causing a computer to perform the data filtering method of any one of claims 1 to 7.