CN118114498A - Data filtering method, device, computer equipment, storage medium and program product - Google Patents
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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
Technical Field
The invention relates to the technical field of permanent magnet synchronous motors, in particular to a data filtering method, a data filtering device, computer equipment, a storage medium and a program product.
Background
In the development process of new energy automobiles, the adopted motor is usually a permanent magnet synchronous motor, so that the energy consumption of the automobile is reduced, and meanwhile, the endurance mileage of the battery is prolonged. However, it is considered that the magnetic field directional vector control method of the permanent magnet synchronous motor driver generates positive and negative sixth harmonic currents in the synchronous reference frame, resulting in torque ripple and deterioration of control performance.
Based on this, a kalman filter is generally used in the new energy automobile field to filter harmonic current, and generally speaking, the filtering effect of the kalman filter depends on the prediction of the process noise Q, however, during the running process of the automobile, since the running road condition cannot be predicted, the accurate process noise Q cannot be obtained, so that the prediction accuracy of the kalman filter is affected.
Disclosure of Invention
In view of the above, the present invention provides a data filtering method, apparatus, computer device, storage medium and program product, so as to solve the problem that the accurate process noise Q cannot be determined during the driving process of the new energy automobile, 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:
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 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;
Filtering first priori estimation data of the current moment based on first priori estimation covariance to obtain optimal estimation of motion state data of the current moment, wherein the first priori estimation data are used for indicating prior estimation of the motion state data of the current moment.
In an alternative embodiment, determining a first a priori estimated covariance of the current moment based on the motion state data comprises:
Determining a priori adjustment amount corresponding to the 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 the first prior estimation covariance of the current moment according to the sum of the prior adjustment quantity and the second prior estimation covariance.
In the embodiment of the disclosure, the first priori estimated covariance corresponding to the current moment k needs to be calculated through the noise Q in the related kalman filtering algorithm, and in the disclosure, the noise Q is abandoned when the first priori estimated covariance is calculated, so that the accuracy of the determined first priori estimated covariance is improved, and the accuracy of optimal estimation of the motion state data at the current moment is further improved.
In an alternative embodiment, the motion state data at the first moment includes posterior state data corresponding to the first moment, where the posterior state data is used to indicate an optimal estimate corresponding to the first moment;
based on the motion state data, determining a priori adjustment corresponding to a first time, including:
Acquiring a second priori 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.
In the embodiment of the disclosure, the prior adjustment amount corresponding to the first time can be calculated based on the operation data corresponding to the first time and the second time, so that the first prior estimation covariance corresponding to the current time k is calculated based on the prior adjustment amount, and dependence on noise Q in the calculation process is eliminated.
In an alternative embodiment, filtering the first prior estimation data at the current time based on the first prior estimation covariance to obtain an optimal estimation at the current time includes:
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 posterior state data;
And determining a Kalman filter matrix corresponding to the current moment according to the first priori estimated covariance, and filtering the first priori estimated data according to the Kalman filter matrix to obtain the optimal estimation of the current moment.
In the embodiment of the disclosure, the first priori estimated data at the current moment can be generated according to the posterior state data corresponding to the first moment, so that the first priori estimated data is filtered according to the Kalman filtering matrix corresponding to the current moment, and the influence of noise on the first priori estimated data is filtered as much as possible.
In an alternative embodiment, generating the first a priori estimate data of the current time from the posterior state data includes:
Acquiring a target state dynamic model corresponding to a 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 a 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 at the current moment.
In the embodiment of the 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 priori estimated data at the current moment, so that a technical basis is provided for the subsequent calculation of the optimal estimation at the current moment based on the first priori estimated data.
In an optional implementation manner, filtering the first a priori estimation data according to the kalman filter matrix to obtain an optimal estimation of motion state data at the current moment includes:
Acquiring angle information of a 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.
In an alternative embodiment, the optimal estimate comprises: and estimating the angular velocity information and the angle information of the target vehicle at the current moment.
In the embodiment of the disclosure, the residual error of the angle information at the current moment and the first priori estimated data can be calculated, so that the first priori estimated data is corrected based on the residual error to obtain the optimal estimation corresponding to the current moment, and the accuracy of the optimal estimation is improved.
In a second aspect, the present invention provides 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 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, wherein the first priori estimation data are used for indicating the prior estimation of the motion state data at the current moment.
In a third aspect, the present invention provides a computer device comprising: the data filtering device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the data filtering method of the first aspect or any corresponding implementation mode of the first aspect is executed.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the data filtering method of the first aspect or any of its corresponding embodiments.
In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to perform the data filtering method of the first aspect or any of its corresponding embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data filtering method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another data filtering method according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another data filtering method according to an embodiment of the present invention;
FIG. 4 is a computational block diagram of a Kalman filter of yet another data filtering method according to an embodiment of the invention;
fig. 5 is a block diagram of a data filtering apparatus according to an embodiment of the present invention;
Fig. 6 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The application scenario is described herein in connection with an application scenario on which execution of a data filtering method depends.
In the development process of new energy automobiles, the adopted motor is usually a permanent magnet synchronous motor, so that the energy consumption of the automobile is reduced, and meanwhile, the endurance mileage of the battery is prolonged. However, it is considered that the magnetic field directional vector control method of the permanent magnet synchronous motor driver generates positive and negative sixth harmonic currents in the synchronous reference frame, resulting in torque ripple and deterioration of control performance.
Based on this, a kalman filter is generally used in the new energy automobile field to filter harmonic current, and generally speaking, the filtering effect of the kalman filter depends on the prediction of the process noise Q, however, during the running process of the automobile, since the running road condition cannot be predicted, the accurate process noise Q cannot be obtained, so that the prediction accuracy of the kalman filter is affected.
For example, the algorithm in the associated kalman filter is as follows:
the time update equation for the kalman filter is as follows:
The state update equation for the Kalman filter is as follows:
wherein, And/>The a posteriori state estimation values, which represent the k-1 time and the k time, respectively, are one of the results of the filtering, i.e., the updated result, also called the optimal estimation (estimated state, the exact result of the state per time cannot be theoretically measured in real time, so-called estimation).
The a priori state estimate representing time k is the result of an intermediate calculation of the filtering, i.e. the result of time k predicted from the optimal estimate of the previous time (time k-1), is the result of the prediction equation.
P K represents the a posteriori estimated covariance at time k (i.eThe covariance of (c) representing the uncertainty of the state), is one of the results of the filtering.
H represents a conversion matrix from a state variable to measurement (observation), represents a relation connecting the state and the observation, is a linear relation in Kalman filtering, is responsible for converting a measured value in m dimension into n dimension so as to conform to the mathematical form of the state variable, and is one of preconditions of filtering.
Z k represents the measured value (observed value), which is the input of the filtering.
K k represents a filter gain matrix, which is an intermediate calculation of the filter, the kalman gain, or the kalman coefficient.
A represents a state transition matrix, which is actually a guess model for the state transition of the object. For example, in maneuvering target tracking, a state transition matrix is often used to model the motion of the target, which may be uniform linear motion or uniform acceleration motion. When the state transition matrix does not conform to the state transition model of the target, the filtering diverges very rapidly.
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. Is the state variable used by the kalman filter to estimate the discrete time process, also known as noise from the prediction model itself.
R represents the measurement noise covariance. When the filter is actually implemented, the measurement noise covariance R is generally observed and is a known condition of the filter.
B represents a matrix that converts an input into a state.
The residual errors representing the actual observation and the predicted observation are corrected a priori (predicted) together with the kalman gain to obtain a posterior.
The embodiment of the invention provides a data filtering method, which comprises the steps of firstly 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, and then 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 an error when the motion state data of the current moment are estimated in a priori. Next, the first prior estimation data at the current time may be filtered based on the first prior estimation covariance to obtain an optimal estimate of the motion state data at the current time, where the first prior estimation data is used to indicate a prior estimate of the motion state data at the current time. 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.
According to an embodiment of the present invention, there is provided a data filtering method embodiment, it being noted that the steps shown in the flowchart of the figures may be performed 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 may be performed in an order other than that shown or described herein.
In this embodiment, a data filtering method is provided, which may be used in the driving control system of the new energy automobile, and in particular, may be applied to the kalman filter, and fig. 1 is a flowchart of a data filtering method according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, acquiring motion state data of a target vehicle during running, wherein the motion state data includes motion state data of a first moment and a second moment.
In the embodiment of the disclosure, the motion state data may include an angular velocity ω e and an angle θ of the target vehicle during operation, and the motion state data x may be represented as a state vectorIn determining the angular velocity ω e, the travel speed v of the target vehicle and the turning radius r can be acquired, wherein ω e =v/r. Meanwhile, the angle θ may be acquired based on an angle sensor mounted on the wheel position of the target vehicle.
The motion state data of the first time and the second time may be acquired when the motion state data is acquired, and it should be understood that the data filtering process in the present disclosure is performed in real time during the running of the target vehicle, and the first time k-1 is a time previous to k with respect to the current time k, and the second time k-2 is a time previous to k-1. Specifically, the interval between the moments may be seconds, milliseconds, etc., which is specifically based on the actual use requirement, and the disclosure is not limited thereto.
Step S102, 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.
In the embodiments of the present disclosure, it should be understood that the kalman filtering algorithm is divided into two steps: the state prediction is used for predicting a first priori estimated covariance P k|k-1 of the current moment k, and the state update is used for predicting the motion state of the current moment according to the motion state of the target vehicle at the previous moment, and specifically comprises the following steps: 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 k. The specific manner of determining the first a priori estimated covariance is as follows and will not be described in detail herein.
Step S103, 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, wherein the first priori estimation data are used for indicating the prior estimation of the motion state data at the current moment.
In the embodiments of the present disclosure, as known from the above, the state prediction and the state update are included in the kalman filtering algorithm, where the state update is used to predict the motion state at the current time according to the motion state of the target vehicle at the previous time. The method comprises the following steps of: 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 k.
Specifically, when the above state update is performed, first, the first a priori estimated data corresponding to the current time k may be determinedIt should be understood that there is often a certain deviation in the first prior estimated data, so the first prior estimated data may be filtered by the first prior estimated covariance P k|k-1 at the current time, so as to obtain the optimal estimate/>, of the motion state data at the current timeIt should be understood that the/>The method can be used as the optimal estimation of the current moment output by the Kalman filter, and can also be used as posterior state data of the current moment to be input into the Kalman filter to participate in the calculation process of the optimal estimation of the next moment k+1.
As can be seen from the foregoing description, in the embodiments of the present disclosure, first, motion state data of a target vehicle during running may be acquired, where the motion state data includes motion state data of a first time and a second time, and then, based on the motion state data, a first a priori estimated covariance of a current time may be determined, where the current time is a time after the first time and the second time, and the first a priori estimated covariance is used to indicate a covariance of an a priori estimated error of the motion state data of the current time. Next, the first prior estimation data at the current time may be filtered based on the first prior estimation covariance to obtain an optimal estimate of the motion state data at the current time, where the first prior estimation data is used to indicate a prior estimate of the motion state data at the current time. 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 accurate Q.
In this embodiment, another data filtering method is provided, which may be used in the driving control system of the new energy automobile, and in particular, may be applied to the kalman filter, and fig. 2 is a flowchart of the data filtering method according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
In step S201, motion state data of the target vehicle during running is acquired, where the motion state data includes motion state data of a first time and a second time. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, 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 a covariance of an error when the motion state data of the current moment is estimated a priori.
Specifically, the step S202 includes:
In step S2021, based on the motion state data of the first time and the second time, a priori adjustment amount corresponding to the first time is determined, where the a priori adjustment amount is used to indicate an adjustment amount for correcting an error of the second a priori estimated covariance of the first time.
In step S2022, the first prior estimated covariance at the current time is determined according to the sum of the prior adjustment amount and the second prior estimated covariance.
In the embodiment of the present disclosure, the above-mentioned a priori adjustment may be denoted as Δp k-1, and the first a priori estimated covariance at the current time may be denoted as P k|k-1. In particular, the method comprises the steps of,
Pk|k-1＝Pk-1|k-2+ΔPk-1
Wherein P k-1|k-2 is the second prior estimated covariance corresponding to the first time k-1.
Here, in determining the above-described second prior estimated covariance P k-1|k-2, it may be determined based on a prior adjustment amount corresponding to the second time, which may be based on posterior state data corresponding to the motion state data at the second timeThird prior estimate data/>The manner of determining the a priori adjustment amount at the second time is described in the following embodiments for determining the a priori adjustment amount corresponding to the first time, which is not described herein.
Step S203, filtering the first prior estimation data at the current time based on the first prior estimation covariance to obtain an optimal estimation of the motion state data at the current time, where the first prior estimation data is used to indicate prior estimation of the motion state data at the current time. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
In the embodiment of the present disclosure, it is known from the above that, in the related kalman filtering algorithm, the first prior estimated covariance corresponding to the current time k needs to be calculated by the noise Q, and in the present disclosure, the noise Q is omitted when the first prior estimated covariance is calculated, so that the accuracy of the determined first prior estimated covariance is improved, and further, the accuracy of the optimal estimation of the motion state data at the current time is improved.
In some optional embodiments, the motion state data at the first moment includes posterior state data corresponding to the first moment, where the posterior state data is used to indicate the optimal estimate corresponding to the first moment, and step S2021 includes:
Step a1, obtaining a second prior estimated covariance of the first moment, which is determined in advance based on the motion state data of the second moment.
And a step a2 of 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 a step a3, correcting the second priori estimated covariance based on the posterior residual error to obtain the priori adjustment corresponding to the first moment.
In an embodiment of the present disclosure, posterior state data in the motion state data at the first time may be recorded asThe second a priori estimate data may be denoted/>Based on the above, the post-test residue corresponding to the first moment
Next, the second prior estimated covariance may be corrected according to the posterior parameter Δx k-1 to obtain a prior adjustment Δp k-1 corresponding to the first time, specifically:
wherein, The transposed matrix, K k-1, is a Kalman filter matrix corresponding to the moment K-1, P k-1|k-2 is a priori estimated covariance corresponding to the moment K-1, H is a state variable to measurement conversion matrix, represents a relation connecting states and observations, is responsible for converting m-dimensional measurement values into n-dimensions so as to be in line with the mathematical form of the state variable, and is one of known filtering preconditions in the Kalman filter.
Specifically, the manner of determining the K k-1 is described in the following embodiment of determining the kalman filter matrix corresponding to the current time K, which is not described herein. It should be appreciated that P k-1|k-2 is posterior state data corresponding to the motion state data at the second time k-2Third prior estimate data/>And (3) determining. Thus, the/>, is calculated in the preamble step at the current momentAnd/>After that, the/>And/>Pre-stored into the buffer space for direct reading in subsequent operations.
In the embodiment of the disclosure, the prior adjustment amount corresponding to the first time can be calculated based on the operation data corresponding to the first time and the second time, so that the first prior estimation covariance corresponding to the current time k is calculated based on the prior adjustment amount, and dependence on noise Q in the calculation process is eliminated.
In this embodiment, a further data filtering method is provided, which may be used in the driving control system of the new energy automobile, and in particular, may be applied to the kalman filter, and fig. 3 is a flowchart of the data filtering method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
In step S301, motion state data of the target vehicle during running is acquired, where the motion state data includes motion state data of a first time and a second time. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, determining a first prior estimated covariance of the current time based on the motion state data, where the current time is a time after the first time and the second time, and the first prior estimated covariance is used to indicate a covariance of an error when the prior estimated motion state data of the current time is performed. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, filtering the first priori estimated data at the current moment based on the first priori estimated covariance to obtain the optimal estimation at the current moment.
Specifically, the step S303 includes:
Step S3031, posterior information of the target vehicle is acquired, and posterior state data corresponding to the motion state data at the first moment is determined according to the posterior information.
Step S3032, first a priori estimated data at the current time is generated according to the posterior state data.
Step S3033, determining a Kalman filter matrix corresponding to the current moment according to the first priori estimated covariance, and filtering the first priori estimated data according to the Kalman filter matrix to obtain the optimal estimation of the current moment.
In the embodiment of the present disclosure, the posterior information may be an optimal estimate output by the kalman filter when predicting each time before the current time, where the optimal estimate at each time may be used as posterior state data of the optimal estimate at the time after calculation.
In determining the first a priori estimate dataThereafter, it can be based on the/>Determining the Kalman filtering matrix K k at the current moment, specifically:
Kk＝Pk|k-1H[Pk|k-1HT+R]-1
wherein, H is described above, and is not described herein, R is a measurement noise covariance, and when the kalman filter is actually implemented, the measurement noise covariance R is generally observed and is a known condition of the filter.
In the embodiment of the disclosure, the first priori estimated data at the current moment can be generated according to the posterior state data corresponding to the first moment, so that the first priori estimated data is filtered according to the Kalman filtering matrix corresponding to the current moment, and the influence of noise on the first priori estimated data is filtered as much as possible.
In some optional embodiments, step S3032 includes:
And b1, 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.
And b2, predicting the motion state data at the first moment according to the target state dynamic model to obtain a prediction result.
And b3, adjusting the prediction result based on the posterior state data to obtain first priori estimated data of the current moment.
In the embodiment of the present disclosure, the above-mentioned target state dynamic model may be denoted as f (x), and the target state dynamic model is used for predicting the motion state data at the first moment k-1 to obtain a prediction resultThen, the pair/>, can be based on the posterior state data Δx k-1 pair at the first time instantPerforming adjustment to obtain first priori estimated data/>Specific:
where T is the transposed matrix and B is the matrix used to transform the input u k-1 into states.
In the embodiment of the 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 priori estimated data at the current moment, so that a technical basis is provided for the subsequent calculation of the optimal estimation at the current moment based on the first priori estimated data.
In some optional embodiments, step S3033 includes:
and c1, 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 c2, 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.
In the embodiment of the present disclosure, the angle information y may be used to indicate the angle of the target vehicle during running, where y=θ, from which the angle information θ may be acquired based on an angle sensor installed at the wheel position of the target vehicle. The procedure for specifically calculating the residual error between the angle information and the first a priori estimated data is as follows:
Wherein the residual may be used to represent the residual of the actual data observation and the predicted data observation.
The first a priori estimate data may then be modified based on the parameters along with the Kalman filter matrixTo obtain the optimal estimation/>, corresponding to the current moment kThe specific correction process is as follows:
in some alternative embodiments, the optimal estimate described above Comprising the following steps: the current time of day is an estimate of the angular velocity information and angle information of the target vehicle, which may be expressed as a vector/>
In the embodiment of the disclosure, the residual error of the angle information at the current moment and the first priori estimated data can be calculated, so that the first priori estimated data is corrected based on the residual error to obtain the optimal estimation corresponding to the current moment, and the accuracy of the optimal estimation is improved.
In this embodiment, a further data filtering method is provided, which may be used in the driving control system of the new energy automobile, and in particular, may be applied to the kalman filter, where the algorithm in the kalman filter is as follows:
Kk＝Pk|k-1H[Pk|k-1HT+R]-1 (16)
Specifically, a block diagram of a kalman filter of another data filtering method corresponding to the algorithm is shown in fig. 4, where input data of the kalman filter is posterior state data of the first time k-1 The angle information y of the target vehicle and the second priori estimated covariance P k-1|k-2 corresponding to the first moment are output as the optimal estimation/>, of the current moment k
In summary, in the embodiments of the present disclosure, first, motion state data of a target vehicle during running may be acquired, where the motion state data includes motion state data of a first time and a second time, and then, based on the motion state data, a first prior estimated covariance of a current time may be determined, where the current time is a time after the first time and the second time, and the first prior estimated covariance is used to indicate a covariance of an error when prior estimation is performed on the motion state data of the current time. Next, the first prior estimation data at the current time may be filtered based on the first prior estimation covariance to obtain an optimal estimate of the motion state data at the current time, where the first prior estimation data is used to indicate a prior estimate of the motion state data at the current time. 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 accurate Q.
The embodiment also provides a data filtering device, which is used for implementing the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a data filtering apparatus, as shown in fig. 5, including:
An obtaining module 501, configured to obtain motion state data of a target vehicle during running, where the motion state data includes motion state data of a first moment and a second moment;
a determining module 502, configured to determine a first prior estimated covariance of the current time based on the motion state data, where the current time is a time after the first time and the second time, and the first prior estimated covariance is used to indicate a covariance of an error when performing prior estimation on the motion state data of the current time;
The filtering module 503 is configured to filter first prior estimation data at the current time based on a first prior estimation covariance, to obtain an optimal estimation of motion state data at the current time, where the first prior estimation data is used to indicate prior estimation of motion state data at the current time.
In some alternative embodiments, the determining module 502 includes:
The first determining unit is used for determining a priori adjustment amount corresponding to the first moment based on the motion state data, wherein the a priori adjustment amount is used for indicating an adjustment amount for correcting an error of a second a priori estimated covariance of the first moment;
And the second determining unit is used for determining the first prior estimation covariance of the current moment according to the sum of the prior adjustment quantity and the second prior estimation covariance.
In some optional embodiments, the motion state data at the first moment includes posterior state data corresponding to the first moment, where the posterior state data is used to indicate the optimal estimate corresponding to the first moment, and the first determining unit includes:
an acquisition subunit, configured to acquire a second prior estimated covariance of the first time determined in advance based on motion state data of the second time;
A first determining subunit, configured to determine a posterior residual error corresponding to the first time based on a difference between the posterior state data and the second prior estimation data;
And the correction subunit is used for correcting the second priori estimated covariance based on the post-test residual difference to obtain the priori adjustment corresponding to the first moment.
In some alternative embodiments, the filtering module 503 includes:
the acquisition unit is used for acquiring posterior information of the target vehicle and determining posterior state data corresponding to the motion state data at the first moment according to the posterior information;
the generation unit is used for generating first priori estimation data at the current moment according to the posterior state data;
and the filtering unit is used for determining a Kalman filtering matrix corresponding to the current moment according to the first priori estimated covariance, and filtering the first priori estimated data according to the Kalman filtering matrix to obtain the optimal estimation of the current moment.
In some alternative embodiments, the generating unit includes:
The generation subunit is used for 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;
The prediction subunit is used for predicting the motion state data at the first moment according to the target state dynamic model to obtain a prediction result;
And the adjustment subunit is used for adjusting the prediction result based on the posterior state data to obtain first priori estimated data of the current moment.
In some alternative embodiments, the filtering unit includes:
The calculating subunit is used for acquiring the angle information of the target vehicle at the current moment and calculating the residual error of the angle information and the first priori estimated data;
and the filtering subunit is used for 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.
In some alternative embodiments, the optimal estimate comprises: and estimating the angular velocity information and the angle information of the target vehicle at the current moment.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not 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 a memory that execute one or more software or firmware programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the data filtering device shown in the figure 5.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 6, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on 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 alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific 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.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device further comprises input means 30 and output means 40. The processor 10, memory 20, input device 30, and output device 40 may be connected by a bus or other means, for example in fig. 6.
The input device 30 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointer stick, one or more mouse buttons, a trackball, a joystick, and the like. The output means 40 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, displays and plasma displays. In some alternative implementations, the display device may be a touch screen.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Portions of the present invention may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or aspects in accordance with the present invention by way of operation of the computer. Those skilled in the art will appreciate that the form of computer program instructions present in a computer readable medium includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the manner in which the 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. Herein, a computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by a computer.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as 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.
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