Electrical signal parameter estimation method and apparatus, electronic device, and storage medium

Abstract

The application relates to an electrical signal parameter estimation method and device, electronic equipment and a storage medium, and relates to the technical field of electrical signal processing. The method comprises the following steps: adopting a variable step size least mean square algorithm, performing cyclic estimation on a fundamental wave signal based on a received signal until the fundamental wave signal meets a preset condition; wherein the fundamental wave signal is a difference value of an error signal and the received signal, the error signal is an output signal of the received signal after a notch filter; a step size factor of the variable step size least mean square algorithm is adjustable, and the step size factor is adjusted based on a square of a time average value of an autocorrelation function of error signals at two adjacent moments; and a signal parameter of the fundamental wave signal is acquired. When the frequency of the electrical signal rapidly changes, good response speed and estimation accuracy can be simultaneously obtained, and the method is more suitable for parameter estimation of a non-stationary signal.

Description

Technical Field

[0001] This application relates to the field of electrical signal processing technology, and in particular to an electrical signal parameter estimation method, apparatus, electronic device and storage medium. Background Technology

[0002] In related technologies, existing estimation methods for non-stationary, rapidly changing signals in power systems have the problem of simultaneously achieving a fast convergence speed and a small steady-state measurement error, which are contradictory relationships. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this application provides an electrical signal parameter estimation method, device, electronic device and storage medium that can simultaneously obtain good response speed and estimation accuracy, and solves the problem in related technologies that it is difficult to simultaneously obtain fast convergence speed and small steady-state measurement error.

[0004] According to a first aspect of the embodiments of this application, an electrical signal parameter estimation method is provided, comprising:

[0005] A variable step-size least mean square algorithm is used to iteratively estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition. The fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter. The step-size factor of the variable step-size least mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points.

[0006] Obtain the signal parameters of the fundamental wave signal.

[0007] In some implementations, the method employs a variable step size least mean square algorithm to cyclically estimate the fundamental signal based on the received signal; including:

[0008] Based on the error signals at time K-1 and time K-2, the time average value of the autocorrelation function at time K is obtained; where K is a positive integer greater than or equal to 3, and time K-1 and time K-2 are adjacent times;

[0009] The step size factor at time K is obtained by squared the time average of the autocorrelation function at time K.

[0010] Based on the step size factor at time K, the adaptive parameters at time K are obtained;

[0011] Based on the received signal and the adaptive parameters at time K, the error signal at time K is calculated;

[0012] Adjust K to K+1.

[0013] In some implementations, obtaining the time average of the autocorrelation function at time K based on the error signal at time K-1 and the error signal at time K-2 includes:

[0014] Using the first formula, based on the error signals at time K-1 and time K-2, the time average value of the autocorrelation function at time K is obtained; wherein, the first formula is expressed as follows:

[0015] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0016] Where φ(K) is the time average of the autocorrelation function at time K, λ is the time average coefficient of the autocorrelation function, y(K-1) is the error signal at time K-1, and y(K-2) is the error signal at time K-2.

[0017] In some implementations, the step size factor at time K is obtained based on the square of the time average of the autocorrelation function at time K, including:

[0018] The step size factor at time K is obtained using the second formula, based on the square of the time average of the autocorrelation function at time K; wherein the second formula is expressed as follows:

[0019] μ(K)=pμ(K-1)+βφ^2(K)

[0020] Where μ(K) is the step size factor at time K, p and β are both step size factor coefficients, and φ(K) is the time average of the autocorrelation function at time K.

[0021] In some implementations, the step size factor μ(K) at time K is greater than the lower limit threshold of the step size factor, and the step size factor μ(K) at time K is less than the upper limit threshold of the step size factor.

[0022] In some implementations, obtaining the adaptive parameters at time K based on the step size factor at time K includes:

[0023] The adaptive parameters at time K are obtained using the third formula, based on the step size factor at time K; wherein the third formula is expressed as follows:

[0024] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0025] Where α(k) is the adaptive parameter at time K, μ(K) is the step size factor at time K, y(K-1) is the error signal at time K-1, and x(k-2) is the received signal at time K-2.

[0026] In some implementations, calculating the error signal at time K based on the received signal and the adaptive parameters at time K includes:

[0027] The error signal at time K is calculated using the fourth formula, based on the received signal and the adaptive parameters at time K; wherein the fourth formula is expressed as follows:

[0028] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0029] Where y(K) is the error signal at time K, x(K) is the received signal at time K, and α(k) is the adaptive parameter at time K.

[0030] According to a second aspect of the embodiments of this application, an electrical signal parameter estimation apparatus is provided, comprising:

[0031] The signal estimation module is used to iteratively estimate the fundamental signal based on the received signal using a variable step size least mean square algorithm until the fundamental signal meets a preset condition; wherein, the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter; the step size factor of the variable step size least mean square algorithm is adjustable, and the step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points;

[0032] The parameter acquisition module is used to acquire the signal parameters of the fundamental wave signal.

[0033] In some implementations, the signal estimation module includes:

[0034] The autocorrelation parameter acquisition unit is used to obtain the time average value of the autocorrelation function at time K based on the error signal at time K-1 and the error signal at time K-2; wherein K is a positive integer greater than or equal to 3, and time K-1 and time K-2 are adjacent times;

[0035] The step size factor acquisition unit is used to obtain the step size factor at time K based on the square of the time average of the autocorrelation function at time K.

[0036] An adaptive parameter acquisition unit is used to obtain the adaptive parameters at time K based on the step size factor at time K.

[0037] An error signal acquisition unit is used to calculate the error signal at time K based on the received signal and the adaptive parameters at time K.

[0038] A parameter adjustment unit is used to adjust K to K+1.

[0039] In some implementations, the autocorrelation parameter acquisition unit is specifically used for:

[0040] Using the first formula, based on the error signals at time K-1 and time K-2, the time average value of the autocorrelation function at time K is obtained; wherein, the first formula is expressed as follows:

[0041] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0042] Where φ(K) is the time average of the autocorrelation function at time K, λ is the time average coefficient of the autocorrelation function, y(K-1) is the error signal at time K-1, and y(K-2) is the error signal at time K-2.

[0043] In some implementations, the step size factor acquisition unit is specifically used for:

[0044] The step size factor at time K is obtained using the second formula, based on the square of the time average of the autocorrelation function at time K; wherein the second formula is expressed as follows:

[0045] μ(K)=pμ(K-1)+βφ^2(K)

[0046] Where μ(K) is the step size factor at time K, p and β are both step size factor coefficients, and φ(K) is the time average of the autocorrelation function at time K.

[0047] In some implementations, the step size factor μ(K) at time K is greater than the lower limit threshold of the step size factor, and the step size factor μ(K) at time K is less than the upper limit threshold of the step size factor.

[0048] In some implementations, the adaptive parameter acquisition unit is specifically used for:

[0049] The adaptive parameters at time K are obtained using the third formula, based on the step size factor at time K; wherein the third formula is expressed as follows:

[0050] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0051] Where α(k) is the adaptive parameter at time K, μ(K) is the step size factor at time K, y(K-1) is the error signal at time K-1, and x(k-2) is the received signal at time K-2.

[0052] In some implementations, the error signal acquisition unit is specifically used for:

[0053] The error signal at time K is calculated using the fourth formula, based on the received signal and the adaptive parameters at time K; wherein the fourth formula is expressed as follows:

[0054] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0055] Where y(K) is the error signal at time K, x(K) is the received signal at time K, and α(k) is the adaptive parameter at time K.

[0056] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising:

[0057] At least one processor; and

[0058] A memory communicatively connected to the at least one processor; wherein,

[0059] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the electrical signal parameter estimation method described in the first aspect above.

[0060] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, comprising:

[0061] The storage medium stores a computer program, which, when executed by a processor, implements the electrical signal parameter estimation method described in the first aspect above.

[0062] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0063] When the frequency of an electrical signal changes rapidly, it can simultaneously achieve good response speed and estimation accuracy, making it more suitable for parameter estimation of non-stationary signals. Furthermore, it can be achieved with the assistance of existing load equipment without incurring additional costs.

[0064] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0066] Figure 1 This is a flowchart illustrating an electrical signal parameter estimation method according to an exemplary embodiment.

[0067] Figure 2 This is an application scenario diagram illustrating an electrical signal parameter estimation method according to an exemplary embodiment.

[0068] Figure 3 This is a flowchart illustrating an electrical signal parameter estimation method according to another exemplary embodiment.

[0069] Figure 4 This is a block diagram illustrating an electrical signal parameter estimation device according to an exemplary embodiment.

[0070] Figure 5 This is a block diagram illustrating an apparatus according to an exemplary embodiment. Detailed Implementation

[0071] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0072] In related technologies, in fields such as power system and electrical equipment operation, monitoring, control, and relay protection, for example, for parameter estimation of non-stationary rapidly changing signals in power systems, adaptive filtering algorithms, such as the LMS (Least Mean Square) adaptive algorithm, are commonly used. However, when using the LMS adaptive algorithm with a fixed step size to estimate parameters of non-stationary rapidly changing signals, it is difficult to simultaneously achieve a fast convergence speed and a small steady-state measurement error.

[0073] To address the aforementioned problems, embodiments of this application provide an electrical signal parameter estimation method, apparatus, electronic device, and storage medium. The method employs a variable step-size factor LMS adaptive algorithm to calculate the notch filter frequency parameters, thereby estimating the frequency parameters of non-stationary signals in a power system. Compared to existing methods, this method is suitable for parameter estimation of non-stationary signals, and when the electrical signal frequency changes rapidly, it can simultaneously achieve good response speed and frequency measurement accuracy. Therefore, this method is more suitable for parameter estimation of non-stationary, rapidly changing signals.

[0074] Figure 1 This is a flowchart illustrating an electrical signal parameter estimation method according to an exemplary embodiment, such as... Figure 1 As shown, this electrical signal parameter estimation method can be used in electronic devices such as terminals and may include the following steps.

[0075] In step S101, a variable step size minimum mean square algorithm is used to iteratively estimate the fundamental signal based on the received signal until the fundamental signal meets the preset conditions; wherein, the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through the notch filter; the step size factor of the variable step size minimum mean square algorithm is adjustable, and the step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points.

[0076] like Figure 2 The diagram illustrates an application scenario of the electrical signal parameter estimation method according to an embodiment of this application. x(k) is the input signal of the notch filter H(z), i.e., the received signal. As an example, x(k) = Asin(ω0k + b0) + v(K), where v(K) represents higher harmonics, and y(K) is the error signal output after the input signal x(k) passes through the notch filter H(z). s(K) is the fundamental signal s(K) obtained by subtracting the original input signal x(K) from x(K) after passing through the notch filter H(z), i.e., s(K) = y(k) - x(k). The function of the notch filter is to filter out unwanted frequency signals. In this embodiment, its function is to filter out higher harmonics, which can be understood as noise signals. Filtering out the noise signals yields the pure fundamental signal.

[0077] The variable step size least mean square algorithm is used to iteratively estimate the fundamental signal based on the received signal until the fundamental signal meets the preset conditions. Meeting the preset conditions means reaching a stable value. After reaching the stable value, the obtained fundamental signal is the estimated electrical signal.

[0078] The step size factor of the variable step size least mean square algorithm in this embodiment is adjustable. The step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points. That is, the step size factor is dynamically adjusted according to the time average of the autocorrelation function of the error signal at two adjacent time points. The smaller the time average of the autocorrelation function, the smaller the step size; the larger the time average of the autocorrelation function, the larger the step size, thereby improving the convergence speed and stability of the algorithm.

[0079] It can be understood that when the minimum mean square algorithm with a fixed step size updates the coefficients of the filter, there is a performance degradation problem in a non-stationary environment. However, the variable step size minimum mean square algorithm of this application improves performance by dynamically adjusting the step size factor to adapt to the changes in the non-stationary environment.

[0080] In step S102, the signal parameters of the fundamental wave signal are obtained.

[0081] Once the estimated electrical signal is obtained, for example, Asin(ω0k+b0), the signal parameters of the electrical signal, including the frequency parameter, can be obtained.

[0082] The electrical signal parameter estimation method in this application employs a variable-step-size least mean square algorithm to cyclically estimate the fundamental signal based on the received signal. Simultaneously, the step-size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points. When the electrical signal frequency changes rapidly, it can simultaneously achieve good response speed and estimation accuracy, solving the problem in related technologies where it is difficult to simultaneously achieve fast convergence speed and small steady-state measurement error. This method is more suitable for parameter estimation of non-stationary signals. Furthermore, it can be implemented with the assistance of existing load equipment without incurring additional costs.

[0083] In step S101 of the above embodiment, it is mentioned that the fundamental signal is cyclically estimated based on the received signal using a minimum mean square algorithm with a variable step size. The specific implementation of this step will be described in detail below.

[0084] Figure 3 This is a flowchart illustrating an electrical signal parameter estimation method according to another exemplary embodiment, such as... Figure 3 As shown, the electrical signal parameter estimation method may include the following steps.

[0085] In step S201, the parameters related to the cyclic estimation are initialized.

[0086] As an example, we set a(0) = 0, φ(0) = 0, p = 0.99, β = 0.001, λ = 0.95, μmin = 0.0001, μmax = 0.005, where μmin is the lower threshold of the step factor, μmax is the upper threshold of the step factor, and y(0) = 0.

[0087] Where α(0) is the adaptive parameter at time 0, i.e. the initial time, φ(0) is the time average of the autocorrelation function at time 0, i.e. the initial time, p and β are both step size factor coefficients, λ is the time average coefficient of the autocorrelation function, and y(0) is the error signal at time 0, i.e. the initial time.

[0088] In step S202, the time average value of the autocorrelation function at time K is obtained based on the error signal at time K-1 and the error signal at time K-2; where K is a positive integer greater than or equal to 3, and time K-1 and time K-2 are adjacent times.

[0089] As one implementation method, the time average value of the autocorrelation function at time K is obtained based on the error signals at time K-1 and time K-2 using the first formula; wherein, the first formula is expressed as follows:

[0090] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0091] Where φ(K) is the time average of the autocorrelation function at time K, λ is the time average coefficient of the autocorrelation function, y(K-1) is the error signal at time K-1, and y(K-2) is the error signal at time K-2.

[0092] Obtain the time average of the autocorrelation function at time K so that the step size factor can be dynamically adjusted in subsequent steps by using the square of the time average of the autocorrelation function.

[0093] In step S203, the step size factor at time K is obtained based on the square of the time average of the autocorrelation function at time K.

[0094] As one implementation method, the step size factor at time K is obtained by using the second formula, based on the square of the time average of the autocorrelation function at time K; where the second formula is expressed as follows:

[0095] μ(K)=pμ(K-1)+βφ^2(K), (μmin<μ(K)<μmax);

[0096] Where μ(K) is the step size factor at time K, μ(K-1) is the step size factor at time K-1, p and β are both step size factor coefficients, and φ(K) is the time average of the autocorrelation function at time K.

[0097] In this embodiment, the step size factor is dynamically adjusted by taking the square of the time average of the autocorrelation function of the error signal at time K-1 and the error signal at time K-2, so as to better adapt to changes in non-stationary environments.

[0098] In step S204, the adaptive parameters at time K are obtained based on the step size factor at time K.

[0099] As one implementation method, the adaptive parameters at time K are obtained based on the step size factor at time K using the third formula; where the third formula is expressed as follows:

[0100] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0101] Where α(k) is the adaptive parameter at time K, μ(K) is the step size factor at time K, y(K-1) is the error signal at time K-1, and x(k-2) is the received signal at time K-2.

[0102] After obtaining the step size factor, the adaptive parameters can be obtained based on the step size factor, that is, the coefficients of the notch filter.

[0103] In step S205, the error signal at time K is calculated based on the received signal and the adaptive parameters at time K.

[0104] As one implementation method, the error signal at time K is calculated using the fourth formula, based on the received signal and the adaptive parameters at time K; where the fourth formula is expressed as follows:

[0105] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0106] Where y(K) is the error signal at time K, x(K) is the received signal at time K, and α(k) is the adaptive parameter at time K.

[0107] In step S206, K is adjusted to K+1, and the process returns to step S202 until the fundamental signal at time K meets the preset conditions, and then proceeds to step S207. The fundamental signal at time K is the difference between the error signal at time K and the received signal at time K.

[0108] In other words, after obtaining the error signal at time K, K is adjusted to K+1, and the process returns to step S202 to execute the next iteration, estimating the error signal at the next time until the fundamental signal s(K)=y(k)-x(k) reaches a stable value, at which point the iteration stops.

[0109] In step S207, the signal parameters of the fundamental wave signal are obtained.

[0110] It should be noted that, in the embodiments of this application, the implementation process of step S207 can be referred to the description of the implementation process of step S102 above, and will not be repeated here.

[0111] The electrical signal parameter estimation method of this application dynamically adjusts the step size factor based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points, and obtains adaptive parameters based on the dynamically adjusted step size factor. Then, the fundamental signal is estimated based on the adaptive parameters. When the electrical signal frequency changes rapidly, it can simultaneously obtain good response speed and estimation accuracy.

[0112] Figure 4This is a block diagram illustrating an electrical signal parameter estimation device according to an exemplary embodiment. (Refer to...) Figure 4 The device includes a signal estimation module 301 and a parameter acquisition module 302.

[0113] The signal estimation module 301 is used to perform cyclic estimation of the fundamental signal based on the received signal using a variable step size least mean square algorithm until the fundamental signal meets the preset conditions. The fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through the notch filter. The step size factor of the variable step size least mean square algorithm is adjustable, and the step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points.

[0114] The parameter acquisition module 302 is used to acquire the signal parameters of the fundamental wave signal.

[0115] In some implementations, the signal estimation module 301 includes:

[0116] The autocorrelation parameter acquisition unit 3011 is used to obtain the time average value of the autocorrelation function at time K based on the error signal at time K-1 and the error signal at time K-2; where K is a positive integer greater than or equal to 3, and time K and time K-1 are adjacent times;

[0117] The step size factor acquisition unit 3012 is used to obtain the step size factor at time K based on the square of the time average of the autocorrelation function at time K.

[0118] The adaptive parameter acquisition unit 3013 is used to obtain the adaptive parameters at time K based on the step size factor at time K.

[0119] The error signal acquisition unit 3014 is used to calculate the error signal at time K based on the received signal and the adaptive parameters at time K.

[0120] The parameter adjustment unit 3015 is used to adjust K to K+1.

[0121] In some implementations, the autocorrelation parameter acquisition unit 3011 is specifically used for:

[0122] Using the first formula, based on the error signals at time K-1 and time K-2, the time average of the autocorrelation function at time K is obtained; where the first formula is expressed as follows:

[0123] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0124] Where φ(K) is the time average of the autocorrelation function at time K, λ is the time average coefficient of the autocorrelation function, y(K-1) is the error signal at time K-1, and y(K-2) is the error signal at time K-2.

[0125] In some implementations, the step size factor acquisition unit 3012 is specifically used for:

[0126] The step size factor at time K is obtained using the second formula, which is based on the square of the time average of the autocorrelation function at time K. The second formula is expressed as follows:

[0127] μ(K)=pμ(K-1)+βφ^2(K)

[0128] Where μ(K) is the step size factor at time K, p and β are both step size factor coefficients, and φ(K) is the time average of the autocorrelation function at time K.

[0129] In some implementations, the step size factor μ(K) at time K is greater than the lower limit threshold of the step size factor, and the step size factor μ(K) at time K is less than the upper limit threshold of the step size factor.

[0130] In some implementations, the adaptive parameter acquisition unit 3013 is specifically used for:

[0131] The adaptive parameters at time K are obtained using the third formula, based on the step size factor at time K; the third formula is expressed as follows:

[0132] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0133] Where α(k) is the adaptive parameter at time K, μ(K) is the step size factor at time K, y(K-1) is the error signal at time K-1, and x(k-2) is the received signal at time K-2.

[0134] In some implementations, the error signal acquisition unit 3014 is specifically used for:

[0135] The error signal at time K is calculated using the fourth formula, based on the received signal and the adaptive parameters at time K; the fourth formula is expressed as follows:

[0136] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0137] Where y(K) is the error signal at time K, x(K) is the received signal at time K, and α(k) is the adaptive parameter at time K.

[0138] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0139] The electrical signal parameter estimation device of this application embodiment uses a variable-step-size least mean square algorithm to cyclically estimate the fundamental signal based on the received signal. Specifically, it dynamically adjusts the step-size factor based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points, and obtains adaptive parameters based on the dynamically adjusted step-size factor. The fundamental signal is then estimated based on the adaptive parameters. When the electrical signal frequency changes rapidly, it can simultaneously achieve good response speed and estimation accuracy, solving the problem in related technologies that it is difficult to simultaneously achieve fast convergence speed and small steady-state measurement error. It is more suitable for parameter estimation of non-stationary signals. Moreover, it can be implemented with the assistance of existing load equipment without adding additional costs.

[0140] Figure 5 This is a block diagram illustrating an apparatus 1800 for estimating electrical signal parameters according to an exemplary embodiment. For example, apparatus 1800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0141] Reference Figure 5 The device 1800 may include one or more of the following components: a processing component 1802, a memory 1804, a power component 1806, a multimedia component 1808, an audio component 1810, an input / output (I / O) interface 1812, a sensor component 1814, and a communication component 1816.

[0142] Processing component 1802 typically controls the overall operation of device 1800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 1802 may include one or more processors 1820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 1802 may include one or more modules to facilitate interaction between processing component 1802 and other components. For example, processing component 1802 may include a multimedia module to facilitate interaction between multimedia component 1808 and processing component 1802.

[0143] Memory 1804 is configured to store various types of data to support the operation of device 1800. Examples of this data include instructions for any application or method operating on device 1800, contact data, phonebook data, messages, pictures, videos, etc. Memory 1804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0144] The power supply component 1806 provides power to the various components of the device 1800. The power supply component 1806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 1800.

[0145] Multimedia component 1808 includes a screen that provides an output interface between the device 1800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 1808 includes a front-facing camera and / or a rear-facing camera. When the device 1800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0146] Audio component 1810 is configured to output and / or input audio signals. For example, audio component 1810 includes a microphone (MIC) configured to receive external audio signals when device 1800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 1804 or transmitted via communication component 1816. In some embodiments, audio component 1810 also includes a speaker for outputting audio signals.

[0147] I / O interface 1812 provides an interface between processing component 1802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0148] Sensor assembly 1814 includes one or more sensors for providing status assessments of various aspects of device 1800. For example, sensor assembly 1814 may detect the on / off state of device 1800, the relative positioning of components such as the display and keypad of device 1800, changes in the position of device 1800 or a component of device 1800, the presence or absence of user contact with device 1800, the orientation or acceleration / deceleration of device 1800, and temperature changes of device 1800. Sensor assembly 1814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 1814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 1814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0149] Communication component 1816 is configured to facilitate wired or wireless communication between device 1800 and other devices. Device 1800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 1816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 1816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0150] In an exemplary embodiment, the apparatus 1800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0151] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1804 including instructions, which can be executed by a processor 1820 of the device 1800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0152] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0153] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for estimating electrical signal parameters, characterized in that, include: A variable-step minimum mean square algorithm is employed to iteratively estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition. The fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter. The step size factor of the variable-step minimum mean square algorithm is adjustable, and the step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points. The received signal is a non-stationary signal, and the time average of the autocorrelation function at time point K is obtained based on the error signal at time point K-1 and time point K-2. Here, K is a positive integer greater than or equal to 3, and time points K-1 and K-2 are adjacent time points. Obtain the signal parameters of the fundamental wave signal.

2. The method according to claim 1, characterized in that, The method employs a variable step size least mean square algorithm to cyclically estimate the fundamental signal based on the received signal; including: The step size factor at time K is obtained by squared the time average of the autocorrelation function at time K. Based on the step size factor at time K, the adaptive parameters at time K are obtained; Based on the received signal and the adaptive parameters at time K, the error signal at time K is calculated; Adjust K to K+1.

3. The method according to claim 2, characterized in that, The step of obtaining the time average of the autocorrelation function at time K based on the error signal at time K-1 and the error signal at time K-2 includes: Using the first formula, based on the error signals at time K-1 and time K-2, the time average value of the autocorrelation function at time K is obtained; wherein, the first formula is expressed as follows: (K) =λ (K-1) + (1-λ) y(K-1) y(K-2) in, (K) is the time average of the autocorrelation function at time K, λ is the coefficient of the time average of the autocorrelation function, y(K-1) is the error signal at time K-1, and y(K-2) is the error signal at time K-2.

4. The method according to claim 2, characterized in that, The step size factor at time K is obtained by squaring the time average of the autocorrelation function at time K, including: The step size factor at time K is obtained using the second formula, based on the square of the time average of the autocorrelation function at time K; wherein the second formula is expressed as follows: μ(K) = pμ(K-1) +β ^2 (K) Where μ(K) is the step size factor at time K, and p and β are both step size factor coefficients. (K) is the time average of the autocorrelation function at time K.

5. The method according to claim 4, characterized in that, The step size factor μ(K) at time K is greater than the lower limit threshold of the step size factor, and the step size factor μ(K) at time K is less than the upper limit threshold of the step size factor.

6. The method according to claim 2, characterized in that, The process of obtaining the adaptive parameters at time K based on the step size factor at time K includes: The adaptive parameters at time K are obtained using the third formula, based on the step size factor at time K; wherein the third formula is expressed as follows: α(k) =α(k-1) -2μ(K) α(k-1)y(K-1) (x(k-2) - y(k-2) ) Where α(k) is the adaptive parameter at time K, μ(K) is the step size factor at time K, y(K-1) is the error signal at time K-1, and x(k-2) is the received signal at time K-2.

7. The method according to claim 2, characterized in that, The step of calculating the error signal at time K based on the received signal and the adaptive parameters at time K includes: The error signal at time K is calculated using the fourth formula, based on the received signal and the adaptive parameters at time K; wherein the fourth formula is expressed as follows: y(K) = x(K) +α(k)x(K-1) + x(K-2) α(k)y(K-1) y(K-2) Where y(K) is the error signal at time K, x(K) is the received signal at time K, and α(k) is the adaptive parameter at time K.

8. An electrical signal parameter estimation device, characterized in that, include: The signal estimation module is used to iteratively estimate the fundamental signal based on the received signal using a variable step size least mean square algorithm until the fundamental signal meets a preset condition. The fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter. The step size factor of the variable step size least mean square algorithm is adjustable, and the step size factor is adjusted based on the square of the time average of the autocorrelation function of the error signal at two adjacent time points. The received signal is a non-stationary signal, and the time average of the autocorrelation function at time point K is obtained based on the error signal at time point K-1 and time point K-2. K is a positive integer greater than or equal to 3, and time points K-1 and K-2 are adjacent time points. The parameter acquisition module is used to acquire the signal parameters of the fundamental wave signal.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, include: The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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