Frequency measurement method and device based on Kalman filtering, electronic equipment and storage medium

Abstract

The invention relates to the technical field of frequency detection, in particular to a frequency measurement method and device based on Kalman filtering, electronic equipment and a storage medium, and the method comprises the steps: obtaining a posterior estimation value of frequency measurement data at a previous moment and an observation value at a current moment; calculating a prior estimation value at the current moment by using the posterior estimation value at the previous moment, calculating an observation error between the prior estimation value at the current moment and the observation value at the current moment, and judging whether the observation error is greater than a preset error threshold value or not; and if the observation error is greater than a preset error threshold value, updating the prior estimation value at the current moment by using a preset Kalman filter, and taking the updated prior estimation value at the current moment as an output value of the frequency measurement data at the current moment. Therefore, the problem that the frequency measurement requirements of complex signal processing, real-time monitoring and quick response to signal change are difficult to meet in the related technology is solved, accurate system state estimation can be provided, and the method has good real-time updating and signal processing capabilities.

Description

Technical Field

[0001] The present application relates to the technical field of frequency detection, and in particular to a frequency measurement method, device, electronic device and storage medium based on Kalman filtering. Background Art

[0002] Frequency measurement technology plays an important role in modern electronic and communication systems, especially in the fields of radar, communication and general testing. However, with the development of technology and the increase of application requirements, frequency measurement technology also faces more challenges and problems.

[0003] With the evolution of radar signals and electromagnetic countermeasure technology, signal forms have become more complex, such as broadband linear frequency modulation, agile frequency switching, frequency hopping, etc. These complex waveforms have put forward higher requirements on frequency measurement accuracy and speed. At the same time, in practical applications, such as wireless communications and radar systems, real-time monitoring and rapid response to changes in signal quality are necessary, which requires frequency measurement technology to have high-speed processing capabilities and real-time data analysis functions. However, the frequency measurement methods in related technologies are difficult to meet the requirements in these aspects. Summary of the invention

[0004] The present application provides a frequency measurement method, device, electronic device and storage medium based on Kalman filtering to solve the problem that related technologies are difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring and rapid response to signal changes. The present application can provide a relatively accurate system state estimation in the presence of noise and interference, has good real-time update and signal processing capabilities, and has few application restrictions.

[0005] The first aspect of the present application provides a frequency measurement method based on Kalman filtering, comprising the following steps:

[0006] Get the posterior estimate of the frequency measurement data at the previous moment and the observed value at the current moment;

[0007] Calculating the priori estimate at the current moment using the a posteriori estimate at the previous moment, calculating the observation error between the a priori estimate at the current moment and the observation value at the current moment, and determining whether the observation error is greater than a preset error threshold;

[0008] If the observation error is greater than a preset error threshold, the preset Kalman filter is used to update the prior estimate value at the current moment, and the updated prior estimate value at the current moment is used as the output value of the frequency measurement data at the current moment.

[0009] Optionally, in some embodiments, the updating the a priori estimate value at the current moment by using a preset Kalman filter includes:

[0010] Calculate the prediction variance at the current moment based on the prediction variance after filtering and updating at the previous moment;

[0011] Update the filter parameters based on the prediction variance at the current moment;

[0012] The updated prior estimation value of the filter is determined based on the updated filtering parameters, the prior estimation value at the previous moment, and the observation error.

[0013] Optionally, in some embodiments, before using a preset Kalman filter to update the a priori estimate value at the current moment, the method further includes:

[0014] Establishing a state space model of a preset Kalman filter, wherein the state space model includes a state equation and an observation equation;

[0015] The state space model is initialized to determine an initial state estimate and an initial state covariance matrix.

[0016] Optionally, in some embodiments, after determining whether the observation error is greater than the preset error threshold, the method further includes:

[0017] If the observation error is less than or equal to the preset error threshold, the prior estimate value at the current moment is used as the output value of the frequency measurement data at the current moment.

[0018] Optionally, in some embodiments, the calculating the a priori estimate value at the current moment by using the a posteriori estimate value at the previous moment includes:

[0019] Based on a preset state equation, the a priori estimate value at the current moment is calculated according to the a posteriori estimate value at the previous moment, wherein the preset state equation is:

[0020]

[0021] in, is the prior estimate of the frequency measurement data at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the model correction vector at the current moment.

[0022] A second aspect of the present application provides a frequency measurement device based on Kalman filtering, comprising:

[0023] The acquisition module is used to obtain the posterior estimation value of the frequency measurement data at the previous moment and the observation value at the current moment;

[0024] A calculation module, used to calculate the prior estimation value at the current moment using the posterior estimation value at the previous moment, calculate the observation error between the prior estimation value at the current moment and the observation value at the current moment, and determine whether the observation error is greater than a preset error threshold;

[0025] The frequency measurement module is used to update the prior estimation value at the current moment using a preset Kalman filter when the observation error is greater than a preset error threshold, and use the updated prior estimation value at the current moment as the output value of the frequency measurement data at the current moment.

[0026] Optionally, in some embodiments, the frequency measurement module includes:

[0027] A first calculation unit, used to calculate the prediction variance at the current moment according to the prediction variance after filtering and updating at the previous moment;

[0028] A first updating unit, used for updating the filtering parameters based on the prediction variance at the current moment;

[0029] A determination unit is used to determine the updated prior estimation value of the filter based on the updated filtering parameters, the prior estimation value at the previous moment, and the observation error.

[0030] Optionally, in some embodiments, before updating the a priori estimate value at the current moment by using a preset Kalman filter, the frequency measurement module includes:

[0031] An establishing unit, used for establishing a state space model of a preset Kalman filter, wherein the state space model includes a state equation and an observation equation;

[0032] The initialization unit is used to initialize the state space model and determine the initial state estimate and the initial state covariance matrix.

[0033] Optionally, in some embodiments, after determining whether the observation error is greater than the preset error threshold, the calculation module further includes:

[0034] A generating unit is used to use the prior estimation value at the current moment as the output value of the frequency measurement data at the current moment when the observation error is less than or equal to the preset error threshold.

[0035] Optionally, in some embodiments, the computing module includes:

[0036] The second calculation unit is used to calculate the prior estimation value at the current moment according to the posterior estimation value at the previous moment based on a preset state equation, wherein the preset state equation is:

[0037]

[0038] in, is the prior estimate of the frequency measurement data at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the model correction vector at the current moment.

[0039] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the frequency measurement method based on Kalman filtering as described in the above embodiment.

[0040] The fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the frequency measurement method based on Kalman filtering as described in the above embodiment.

[0041] Thus, by obtaining the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment, and using the a posteriori estimate of the previous moment to calculate the a priori estimate of the current moment, the observation error between the a priori estimate of the current moment and the observed value at the current moment is calculated, and it is determined whether the observation error is greater than the preset error threshold. If the observation error is greater than the preset error threshold, the a priori estimate of the current moment is updated using the preset Kalman filter, and the updated a priori estimate of the current moment is used as the output value of the frequency measurement data at the current moment. Thus, the problem that the related technology is difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring, and rapid response to signal changes is solved. The present application can provide a relatively accurate system state estimation in the presence of noise and interference, has good real-time update and signal processing capabilities, and has small application restrictions.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A flow chart of a frequency measurement method based on Kalman filtering provided according to an embodiment of the present application;

[0045] Figure 2 A block diagram of a frequency measurement device based on Kalman filtering according to an embodiment of the present application;

[0046] Figure 3It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0048] The following describes the frequency measurement method, device, electronic device and storage medium based on Kalman filtering of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the related technology is difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring and rapid response to signal changes, the present application provides a frequency measurement method based on Kalman filtering, in which the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment are obtained, and the a posteriori estimate of the previous moment is used to calculate the a priori estimate of the current moment, and the observation error between the a priori estimate of the current moment and the observed value at the current moment is calculated, and it is judged whether the observation error is greater than the preset error threshold. If the observation error is greater than the preset error threshold, the a priori estimate of the current moment is updated by using the preset Kalman filter, and the updated a priori estimate of the current moment is used as the output value of the frequency measurement data at the current moment. Thus, the problem that the related technology is difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring and rapid response to signal changes is solved. The present application can provide a more accurate system state estimation in the presence of noise and interference, has good real-time update and signal processing capabilities, and has small application restrictions.

[0049] Specifically, Figure 1 A flow chart of a frequency measurement method based on Kalman filtering provided in an embodiment of the present application.

[0050] like Figure 1 As shown, the frequency measurement method based on Kalman filtering includes the following steps:

[0051] In step S101, the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment are obtained.

[0052] Specifically, the embodiment of the present application first obtains two key data from the frequency measurement process at the previous moment: the a posteriori estimate and the observation at the current moment, wherein the a posteriori estimate is the most accurate estimate based on all available information at the previous moment, and the observation at the current moment is the data directly obtained from the sensor or measuring device.

[0053] In step S102, the a priori estimate at the previous moment is used to calculate the a priori estimate at the current moment, the observation error between the a priori estimate at the current moment and the observed value at the current moment is calculated, and it is determined whether the observation error is greater than a preset error threshold.

[0054] The preset error threshold may be pre-set by a user, obtained through a limited number of experiments, or obtained through a limited number of computer simulations, and is not specifically limited here.

[0055] In the actual execution process, the embodiment of the present application can use the a posteriori estimate value at the previous moment to calculate the a priori estimate value at the current moment, including: based on a preset state equation, calculating the a priori estimate value at the current moment according to the a posteriori estimate value at the previous moment, wherein the preset state equation is:

[0056]

[0057] in, is the prior estimate of the frequency measurement data at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the model correction vector at the current moment.

[0058] Specifically, the implementation of the present application can use the posterior estimate of the previous moment as a basis to predict the state of the current moment through the dynamic model of the system, that is, calculate the prior estimate, and compare the prior estimate of the current moment with the actual observation value of the current moment, calculate the observation error, and reflect the difference between the predicted value and the actual value through the observation error.

[0059] In step S103, if the observation error is greater than the preset error threshold, the preset Kalman filter is used to update the prior estimate value at the current moment, and the updated prior estimate value at the current moment is used as the output value of the frequency measurement data at the current moment.

[0060] It can be understood that the embodiments of the present application can reflect the difference between the predicted value and the actual value through the observation error. If the observation error exceeds the preset error threshold, it means that the current observation value is subject to large noise interference or the system state has changed significantly, and the prior estimate needs to be corrected to ensure the accuracy of the frequency measurement data.

[0061] In the embodiment of the present application, a Kalman filter algorithm can be used to adjust the prior estimate value according to the observation error and the Kalman gain to obtain an updated estimate value. This updated estimate value combines the prediction information and the observation information, and can more accurately reflect the system status at the current moment. This updated value is used as the output value of the frequency measurement data at the current moment for use in subsequent steps or systems.

[0062] Optionally, in some embodiments, a preset Kalman filter is used to update the prior estimate at the current moment, including: calculating the prediction variance at the current moment based on the prediction variance after filtering and updating at the previous moment; updating the filtering parameters based on the prediction variance at the current moment; and determining the prior estimate after filtering and updating based on the updated filtering parameters, the prior estimate at the previous moment, and the observation error.

[0063] Specifically, the embodiment of the present application can obtain the prediction variance after filtering and updating at the previous moment, and calculate the prediction variance at the current moment based on the prediction variance after filtering and updating at the previous moment according to formula (1):

[0064]

[0065] in, is the prediction variance at the current moment, A is the state transfer matrix, P k-1 is the prediction variance after filtering and updating at the last moment, Q k is the process noise.

[0066] After obtaining the prediction variance at the current moment, the filter parameters can be updated based on the prediction variance at the current moment according to formula (2):

[0067]

[0068] Among them, R k is the measurement noise, and H is the measurement matrix.

[0069] After obtaining the updated filter parameters, the updated prior estimation value of the filter is determined based on the updated filter parameters, the prior estimation value at the previous moment, and the observation error.

[0070]

[0071] in, is the prior estimate after filtering update, is the prior estimate of the previous moment, K is the updated filter parameter, e k For error.

[0072] Optionally, in some embodiments, after determining whether the observation error is greater than a preset error threshold, it also includes: if the observation error is less than or equal to the preset error threshold, using the prior estimate value at the current moment as the output value of the frequency measurement data at the current moment.

[0073] It can be understood that after determining whether the observation error is greater than the preset error threshold, if the observation error is found to be less than or equal to the preset error threshold, it indicates that the difference between the observation value at the current moment and the predicted observation value based on the prior estimate is small. In this case, it can be considered that the prior estimate at the current moment is accurate enough and does not require further correction.

[0074] Specifically, if the observation error is less than or equal to the preset error threshold, the prior estimate at the current moment can be directly used as the output value of the frequency measurement data at the current moment, without the need to adjust the prior estimate using the Kalman gain, because the observation data does not show significant deviations, and the prior estimate is sufficient to represent the current state.

[0075] Optionally, in some embodiments, before using a preset Kalman filter to update the prior estimate at the current moment, it includes: establishing a state space model of the preset Kalman filter, wherein the state space model includes a state equation and an observation equation; initializing the state space model to determine an initial state estimate and an initial state covariance matrix.

[0076] Specifically, before the embodiment of the present application uses the preset Kalman filter to update the prior estimation value at the current moment, it is necessary to establish a state space model of the preset Kalman filter, wherein the state space model includes a state equation and an observation equation, wherein the state equation can be:

[0077]

[0078] in, is the prior estimate at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the control vector.

[0079] The observation equation can be

[0080] z k =Hx k +v k ; (5)

[0081] Among them, z k is the observation vector at the current moment, H is the observation matrix, v k is the observation noise.

[0082] After the state space model is established through the state equation and the observation equation, the state space model is initialized, the initial state estimate and the initial state covariance matrix are determined, and the preset Kalman filter is obtained.

[0083] According to the frequency measurement method based on Kalman filtering proposed in the embodiment of the present application, by obtaining the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment, and using the a posteriori estimate of the previous moment to calculate the a priori estimate of the current moment, the observation error between the a priori estimate of the current moment and the observed value at the current moment is calculated, and it is determined whether the observation error is greater than the preset error threshold. If the observation error is greater than the preset error threshold, the a priori estimate of the current moment is updated using the preset Kalman filter, and the updated a priori estimate of the current moment is used as the output value of the frequency measurement data at the current moment. Thus, the problem that the related technology is difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring and rapid response to signal changes is solved. The present application can provide a relatively accurate system state estimation in the presence of noise and interference, has good real-time update and signal processing capabilities, and has small application restrictions.

[0084] Next, a frequency measuring device based on Kalman filtering proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0085] Figure 2 It is a block diagram of a frequency measurement device based on Kalman filtering according to an embodiment of the present application.

[0086] like Figure 2 As shown, the frequency measuring device 10 based on Kalman filtering includes: an acquisition module 100 , a calculation module 200 and a frequency measuring module 300 .

[0087] The acquisition module 100 is used to obtain the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment.

[0088] The calculation module 200 is used to calculate the prior estimation value at the current moment using the posterior estimation value at the previous moment, calculate the observation error between the prior estimation value at the current moment and the observation value at the current moment, and determine whether the observation error is greater than a preset error threshold.

[0089] The frequency measurement module 300 is used to update the prior estimation value at the current moment using a preset Kalman filter when the observation error is greater than a preset error threshold, and use the updated prior estimation value at the current moment as the output value of the frequency measurement data at the current moment.

[0090] Optionally, in some embodiments, the frequency measurement module 300 includes: a first calculation unit, a first updating unit and a determination unit.

[0091] The first calculation unit is used to calculate the prediction variance at the current moment according to the prediction variance after filtering and updating at the previous moment.

[0092] The first updating unit is used to update the filtering parameters based on the prediction variance at the current moment.

[0093] The determination unit is used to determine the updated prior estimation value of the filter based on the updated filtering parameters, the prior estimation value at the previous moment, and the observation error.

[0094] Optionally, in some embodiments, before using a preset Kalman filter to update the a priori estimate value at the current moment, the frequency measurement module 300 includes: an establishment unit and an initialization unit.

[0095] Wherein, the establishing unit is used to establish a state space model of a preset Kalman filter, wherein the state space model includes a state equation and an observation equation;

[0096] The initialization unit is used to initialize the state space model and determine the initial state estimate and the initial state covariance matrix.

[0097] Optionally, in some embodiments, after determining whether the observation error is greater than a preset error threshold, the calculation module 200 further includes: a generation unit.

[0098] Wherein, the generating unit is used to use the prior estimation value at the current moment as the output value of the frequency measurement data at the current moment when the observation error is less than or equal to the preset error threshold.

[0099] Optionally, in some embodiments, the computing module 200 includes: a second computing unit.

[0100] The second calculation unit is used to calculate the prior estimation value at the current moment according to the posterior estimation value at the previous moment based on the preset state equation, wherein the preset state equation is:

[0101]

[0102] in, is the prior estimate of the frequency measurement data at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the model correction vector at the current moment.

[0103] It should be noted that the above explanation of the embodiment of the frequency measurement method based on Kalman filtering is also applicable to the frequency measurement device based on Kalman filtering in this embodiment, and will not be repeated here.

[0104] According to the frequency measurement device based on Kalman filtering proposed in the embodiment of the present application, by obtaining the a posteriori estimate of the frequency measurement data at the previous moment and the observed value at the current moment, and using the a posteriori estimate of the previous moment to calculate the a priori estimate of the current moment, the observation error between the a priori estimate of the current moment and the observed value at the current moment is calculated, and it is determined whether the observation error is greater than the preset error threshold. If the observation error is greater than the preset error threshold, the a priori estimate of the current moment is updated using the preset Kalman filter, and the updated a priori estimate of the current moment is used as the output value of the frequency measurement data at the current moment. Thus, the problem that the related technology is difficult to meet the frequency measurement requirements of complex signal processing, real-time monitoring and rapid response to signal changes is solved. The present application can provide a relatively accurate system state estimation in the presence of noise and interference, has good real-time update and signal processing capabilities, and has small application restrictions.

[0105] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0106] A memory 301 , a processor 302 , and a computer program stored in the memory 301 and executable on the processor 302 .

[0107] When the processor 302 executes the program, the frequency measurement method based on Kalman filtering provided in the above embodiment is implemented.

[0108] Furthermore, the electronic device further comprises:

[0109] The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0110] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0111] The memory 301 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0112] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0113] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can communicate with each other through an internal interface.

[0114] The processor 302 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0115] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the frequency measurement method based on Kalman filtering as described above is implemented.

[0116] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0117] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0118] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0119] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0120] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0121] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A frequency measurement method based on Kalman filtering, characterized in that: The following steps are involved: Get the posterior estimate of the frequency measurement data at the previous moment and the observed value at the current moment; Calculating the priori estimate at the current moment using the a posteriori estimate at the previous moment, calculating the observation error between the a priori estimate at the current moment and the observation value at the current moment, and determining whether the observation error is greater than a preset error threshold; If the observation error is greater than a preset error threshold, the preset Kalman filter is used to update the prior estimate value at the current moment, and the updated prior estimate value at the current moment is used as the output value of the frequency measurement data at the current moment.

2. The method according to claim 1, characterized in that The updating of the a priori estimate value at the current moment by using a preset Kalman filter includes: Calculate the prediction variance at the current moment based on the prediction variance after filtering and updating at the previous moment; Update the filter parameters based on the prediction variance at the current moment; The updated prior estimation value of the filter is determined based on the updated filtering parameters, the prior estimation value at the previous moment, and the observation error.

3. The method according to claim 2, characterized in that Before updating the a priori estimate value at the current moment by using a preset Kalman filter, the method includes: Establishing a state space model of the preset Kalman filter, wherein the state space model includes a state equation and an observation equation; The state space model is initialized to determine an initial state estimate and an initial state covariance matrix.

4. The method according to claim 1, characterized in that: After determining whether the observation error is greater than the preset error threshold, the method further includes: If the observation error is less than or equal to the preset error threshold, the prior estimate value at the current moment is used as the output value of the frequency measurement data at the current moment.

5. The method according to claim 1, characterized in that The calculating the priori estimate value at the current moment by using the posterior estimate value at the previous moment comprises: Based on a preset state equation, the a priori estimate value at the current moment is calculated according to the a posteriori estimate value at the previous moment, wherein the preset state equation is: in, is the prior estimate of the frequency measurement data at the current moment, is the posterior estimate of the frequency measurement data at the previous moment, A is the state transfer matrix, B is the control matrix, and u k is the model correction vector at the current moment.

6. A frequency measurement device based on Kalman filtering, characterized in that: include: The acquisition module is used to obtain the posterior estimation value of the frequency measurement data at the previous moment and the observation value at the current moment; A calculation module, used to calculate the prior estimation value at the current moment using the posterior estimation value at the previous moment, calculate the observation error between the prior estimation value at the current moment and the observation value at the current moment, and determine whether the observation error is greater than a preset error threshold; The frequency measurement module is used to update the prior estimation value at the current moment using a preset Kalman filter when the observation error is greater than a preset error threshold, and use the updated prior estimation value at the current moment as the output value of the frequency measurement data at the current moment.

7. The device according to claim 6, characterized in that The frequency measurement module comprises: A first calculation unit, used to calculate the prediction variance at the current moment according to the prediction variance after filtering and updating at the previous moment; A first updating unit, used for updating the filtering parameters based on the prediction variance at the current moment; A determination unit is used to determine the updated prior estimation value of the filter based on the updated filtering parameters, the prior estimation value at the previous moment, and the observation error.

8. The device according to claim 7, characterized in that Before updating the a priori estimate value at the current moment by using a preset Kalman filter, the frequency measurement module includes: An establishing unit, used for establishing a state space model of a preset Kalman filter, wherein the state space model includes a state equation and an observation equation; The initialization unit is used to initialize the state space model and determine the initial state estimate and the initial state covariance matrix.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the frequency measurement method based on Kalman filtering as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the frequency measurement method based on Kalman filtering as described in any one of claims 1 to 5.

Images