Method and device for fine acquisition of a telemetry signal based on fuzzy control

Abstract

The application provides a fuzzy control-based TT&C signal fine acquisition method and device, and belongs to the field of satellite-borne TT&C transponder signal processing. The method comprises the following steps: acquiring a pseudo-code delay coarse estimation value and a Doppler frequency coarse estimation value respectively; determining the value range corresponding to the input variable of the pseudo-code delay dimension and the value range corresponding to the input variable of the Doppler frequency dimension; determining the input fuzzy set of the pseudo-code delay dimension and the corresponding input membership function according to the input variable of the dimension, and determining the input fuzzy set of the Doppler frequency dimension and the corresponding input membership function according to the input variable of the dimension; for each dimension, performing a bias value estimation operation in the fuzzy set corresponding to the input variable of the dimension to obtain the bias estimation value corresponding to the dimension; and determining the fine acquisition value of the dimension according to the coarse estimation value and the bias estimation value corresponding to the input variable of the dimension. The application can improve the estimation accuracy of TT&C signals.

Description

Technical Field

[0001] This application belongs to the field of spaceborne telemetry, tracking and command transponder signal processing technology, and more specifically, it relates to a method and device for precise acquisition of telemetry, tracking and command signals based on fuzzy control. Background Technology

[0002] Direct-sequence spread spectrum (DSSS) signals are widely used in satellite telemetry, tracking, and command (TT&C) systems due to their excellent anti-interference capabilities.

[0003] However, existing technologies face two major challenges: First, conventional signal acquisition methods perform a search operation within a two-dimensional discrete search grid of pseudocode delay and Doppler frequency, using the parameters corresponding to the best-matching grid point as the estimated value. Since the parameters of the actual received signal have continuous characteristics, the probability that the true parameter value falls exactly on a discrete grid point is extremely low. This grid discretization characteristic leads to an inherent deviation between the estimated and true values, directly affecting the performance and accuracy of subsequent tracking loops. Second, to improve estimation accuracy, conventional methods need to refine the search grid to approximate the true value. However, grid refinement leads to an exponential increase in computational complexity. In scenarios where the computational resources of spaceborne telemetry and control transponders are strictly limited, this surge in computational overhead directly threatens the system's real-time performance, creating a contradictory dilemma between accuracy improvement and computational efficiency.

[0004] The aforementioned defects are particularly prominent in the context of the high precision and real-time requirements of satellite telemetry and control systems. There is an urgent need for a technical solution that can both eliminate the estimation bias caused by grid discretization and effectively control the computational complexity to ensure the real-time performance of the system. Summary of the Invention

[0005] This application provides a method and apparatus for precise acquisition of measurement and control signals based on fuzzy control. This addresses the technical problems in existing technologies where discrete grid search leads to inherent deviations between estimated and true values, and where refining the grid results in a surge in computational complexity and impacts system real-time performance. The aim is to achieve high-precision, low-complexity precise acquisition of measurement and control signals, thereby improving tracking performance and real-time capabilities. To achieve the above objectives, the technical solution provided by this application is as follows:

[0006] Firstly, a method for precise acquisition of measurement and control signals based on fuzzy control is provided, including:

[0007] In a two-dimensional discrete search grid of pseudocode delay and Doppler frequency, the target input variables are obtained based on the baseband received signal. The target input variables include input variables in the pseudocode delay dimension and input variables in the Doppler frequency dimension.

[0008] Obtain the coarse estimate of the pseudocode delay corresponding to the input variable in the pseudocode delay dimension and the coarse estimate of the Doppler frequency corresponding to the input variable in the Doppler frequency dimension;

[0009] Determine the value range of the input variable corresponding to the pseudocode delay dimension and the value range of the input variable corresponding to the Doppler frequency dimension;

[0010] The input fuzzy set and its corresponding input membership function are determined based on the input variables of the pseudocode delay dimension, and the input fuzzy set and its corresponding input membership function are determined based on the input variables of the Doppler frequency dimension.

[0011] For each dimension, a deviation estimation operation is performed in the fuzzy set corresponding to the input variable of that dimension to obtain the deviation estimate for that dimension;

[0012] Based on the coarse and bias estimates of the input variables for this dimension, the fine capture value for this dimension is determined. The fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value.

[0013] Secondly, a device for precise acquisition of measurement and control signals based on fuzzy control is provided, comprising:

[0014] The data acquisition module is used to acquire target input variables based on the baseband received signal in a two-dimensional discrete search grid of pseudocode delay and Doppler frequency. The target input variables include input variables in the pseudocode delay dimension and input variables in the Doppler frequency dimension.

[0015] The coarse estimation value acquisition module is used to acquire the coarse estimation value of the pseudocode delay corresponding to the input variable in the pseudocode delay dimension and the coarse estimation value of the Doppler frequency corresponding to the input variable in the Doppler frequency dimension.

[0016] The value range determination module is used to determine the value range corresponding to the input variable in the pseudocode delay dimension and the value range corresponding to the input variable in the Doppler frequency dimension.

[0017] The input membership function determination module is used to determine the input fuzzy set and its corresponding input membership function based on the input variables of the pseudocode delay dimension, and to determine the input fuzzy set and its corresponding input membership function based on the input variables of the Doppler frequency dimension.

[0018] The deviation estimate determination module is used to perform deviation estimate operation for each dimension in the fuzzy set corresponding to the input variable of that dimension, and obtain the deviation estimate corresponding to that dimension.

[0019] The fine capture value determination module is used to determine the fine capture value of the dimension based on the coarse estimate and bias estimate corresponding to the input variable of the dimension. The fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value.

[0020] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the fuzzy control-based precision acquisition method for measurement and control signals provided in any possible implementation of the first aspect.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the fuzzy control-based precision acquisition method for measurement and control signals provided by any possible implementation of the first aspect.

[0022] The beneficial effects of the technical solution provided in this application are as follows:

[0023] The fuzzy control-based precision acquisition method and apparatus for telemetry and control signals provided in this application, compared with related technologies, introduces fuzzy control on the basis of a two-dimensional discrete search grid. By utilizing the input variables of each dimension (e.g., error and error rate of change), and through fuzzification, fuzzy inference, and defuzzification operations, it can accurately estimate and compensate for the pseudo-code delay and Doppler frequency estimation deviation caused by grid discretization, effectively eliminating the quantization error inherent in traditional coarse acquisition methods and significantly improving the parameter estimation accuracy of telemetry and control signals. This embodiment achieves precision acquisition without refining the search grid, avoiding the exponential increase in computational complexity caused by grid refinement, and still ensuring the real-time performance of the system under the condition of strictly limited computing resources of the spaceborne telemetry and control transponder. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0025] Figure 1 A flowchart illustrating the precise acquisition method for measurement and control signals based on fuzzy control provided in this application embodiment;

[0026] Figure 2 A schematic diagram of the membership function corresponding to the first fuzzy input variable provided in the embodiments of this application;

[0027] Figure 3 A schematic diagram of the membership function corresponding to the second fuzzy input variable provided in the embodiments of this application;

[0028] Figure 4 A schematic diagram of the output membership functions corresponding to the output quantities of each dimension provided in the embodiments of this application;

[0029] Figure 5 A structural block diagram of the measurement and control signal fine acquisition device based on fuzzy control provided in the embodiments of this application;

[0030] Figure 6 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0032] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” can be implemented as “A,” or as “B,” or as “A and B.” When describing multiple (two or more) items, if the relationship between the multiple items is not explicitly defined, the multiple items can refer to one, several or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" can be implemented as parameter A includes A1 or A2 or A3, or it can be implemented as parameter A includes at least two of the three items A1, A2 and A3.

[0033] This application provides a method for precise acquisition of measurement and control signals based on fuzzy control. This method can be executed by electronic devices, such as... Figure 1 As shown, the method may include:

[0034] S101: In a two-dimensional discrete search grid of pseudocode delay and Doppler frequency, the target input variable is obtained based on the baseband received signal.

[0035] In this embodiment, the target input variables include input variables in the pseudocode delay dimension and input variables in the Doppler frequency dimension, and the input variables in each dimension include a first input variable and a second input variable.

[0036] In this embodiment, in a two-dimensional discrete search grid of pseudocode delay and Doppler frequency, with The expression for the baseband received signal obtained for the sampling period is:

[0037] ,

[0038] in, Indicates the baseband received signal. Indicates the sampling index. Indicates the amplitude of the received signal. Data representing modulation, Indicates the spread spectrum pseudocode, Indicates the pseudocode delay of the received signal. This indicates the Doppler frequency of the received signal. Indicates the initial phase of the received signal. This indicates that the mean is 0 and the variance is 0. Additive white Gaussian noise.

[0039] In this embodiment, when estimating the pseudocode delay deviation, two input variables of the fuzzy controller in the pseudocode delay dimension are obtained. The calculation method for the first input variable (e.g., the error in the pseudocode delay dimension) and the second input variable (e.g., the rate of change of the error in the pseudocode delay dimension) in this dimension is as follows:

[0040] ,

[0041] in, The first input variable represents the pseudocode delay dimension. Let the two-input variable represent the pseudocode delay dimension. and Let represent the real and imaginary parts of the correlation result of the baseband received signal, respectively. and The baseband received signal correlation results are related to The real and imaginary parts of the first-order partial derivative, and The received signal correlation results are related to The real and imaginary parts of the second-order partial derivative.

[0042] In this embodiment, when estimating the Doppler frequency deviation, two input variables of the fuzzy controller in the Doppler frequency dimension are obtained. The calculation method for the first input variable (e.g., the error in the Doppler frequency dimension) and the second input variable (e.g., the rate of change of the error in the Doppler frequency dimension) in this dimension is as follows:

[0043] ,

[0044] in, The first input variable represents the Doppler frequency dimension. The second input variable represents the Doppler frequency dimension. and The baseband received signal correlation results are related to The real and imaginary parts of the first-order partial derivative, and The baseband received signal correlation results are related to The real and imaginary parts of the second-order partial derivative.

[0045] For example, the intermediate variables in the calculation process of the first and second input variables of the pseudocode delay dimension, and the first and second input variables of the Doppler frequency dimension can be calculated as follows:

[0046] ,

[0047] in, and They are respectively Regarding the rough estimate of pseudocode delay The first-order difference and the second-order difference.

[0048] In this embodiment, the fuzzy controller can be a Mamdani-type fuzzy controller with two-dimensional input and one-dimensional output, where the pseudocode delay and Doppler frequency are set independently for the two dimensions.

[0049] This embodiment, after obtaining the input variables for each dimension, also includes variable fuzzification processing for each input variable. Specifically, the pseudocode is delayed by the first input variable of the dimension. The corresponding first quantization factor Setting it to 1 will change the second input variable of the pseudocode delay dimension. The corresponding second quantization factor Set to 1; set the first input variable of the Doppler frequency dimension. The corresponding third quantization factor Setting it to 2500 will change the second input variable of the Doppler frequency dimension. The corresponding fourth quantization factor The value is set to 5. In this embodiment, the quantization factor corresponding to each input variable is fixed, resulting in low storage resource consumption.

[0050] As can be seen from the above, this embodiment constructs errors (first input variables of each dimension) and error change rates (second input variables of each dimension) that can characterize the magnitude and trend of deviations by calculating the partial derivatives of the signal correlation results. These are used as inputs to the fuzzy controller, providing rich information for accurate estimation. At the same time, by setting each quantization factor, the actual physical quantity is mapped to the standard fuzzy domain, making the fuzzy controller design more universal and portable.

[0051] S102: Obtain the coarse estimate of the pseudocode delay corresponding to the input variable in the pseudocode delay dimension and the coarse estimate of the Doppler frequency corresponding to the input variable in the Doppler frequency dimension.

[0052] In this embodiment, a coarse estimate of the pseudocode delay dimension is obtained in a two-dimensional discrete grid of pseudocode delay and Doppler frequency. The rough estimate of the Doppler frequency dimension is .

[0053] S103: Determine the range of values ​​for the input variables in the pseudocode delay dimension and the range of values ​​for the input variables in the Doppler frequency dimension.

[0054] In this embodiment, after determining the input variables for the pseudocode delay dimension and the Doppler frequency dimension, the value range corresponding to each dimension of the input variables is determined based on the normalized difference between the coarse estimate of the pseudocode delay and the coarse estimate of the Doppler frequency.

[0055] For the input variable of the pseudocode delay dimension, when estimating the pseudocode delay bias, the first and second input variables of this dimension are respectively input into the fuzzy controller to obtain the first fuzzy value of the first input variable in the fuzzy universe and the second fuzzy value of the second input variable in the fuzzy universe. Specifically, the first input variable and the first quantization factor are... The product of and is used as the first fuzzy input variable The second input variable and the second quantization factor The product of these two variables is used as the second fuzzy input variable. .

[0056] For the input variable in the Doppler frequency dimension, when estimating the Doppler frequency bias, the first and second input variables of this dimension are respectively input into the fuzzy controller to obtain the third fuzzy value of the first input variable in the fuzzy universe and the fourth fuzzy value of the second input variable in the fuzzy universe. Specifically, the first input variable and the third quantization factor are... The product of these variables is used as the third fuzzy input variable. The second input variable is combined with the fourth quantization factor. The product of these variables is used as the fourth fuzzy input variable. .

[0057] In this embodiment, the first fuzzy input variable after amplitude limiting is calculated based on the first fuzzy input variable and a preset first range, using a first amplitude limiting formula. The first amplitude limiting formula can be:

[0058] ,in, This represents the first fuzzy input variable after amplitude limiting.

[0059] In this embodiment, based on the second fuzzy input variable and a preset second range, and using a second limiting formula, the second fuzzy input variable after limiting is calculated. The second limiting formula can be:

[0060] ,in, This represents the second fuzzy input variable after amplitude limiting.

[0061] In this embodiment, the third fuzzy input variable after limiting is calculated based on the third fuzzy input variable and a preset third range, using a third limiting formula. The third limiting formula can be:

[0062] ,in, This represents the third fuzzy input variable after amplitude limiting.

[0063] In this embodiment, the fourth fuzzy input variable and a preset fourth range are used as the basis for calculating the fourth fuzzy input variable after the limiting process, and the fourth limiting formula is used. The fourth limiting formula can be:

[0064] ,in, This represents the fourth fuzzy input variable after amplitude limiting.

[0065] In this embodiment, the amplitude limiting boundaries corresponding to each fuzzy input variable (e.g., 1.2, -1.2, -0.8, and -2.4) can be determined by debugging based on actual working conditions and experience.

[0066] As can be seen from the above, this embodiment maps the actual input variables to the fuzzy universe of discourse through a quantization factor and uses a limiting formula to process input values ​​that exceed the preset range, ensuring that the input of the fuzzy controller is always within the designed universe of discourse. This effectively prevents controller output saturation or runaway due to singular values ​​or large noise interference, and improves the stability and reliability of the system.

[0067] S104: Determine the input fuzzy set and its corresponding input membership function based on the input variables of the pseudocode delay dimension, and determine the input fuzzy set and its corresponding input membership function based on the input variables of the Doppler frequency dimension.

[0068] In one specific implementation, for each dimension, the corresponding input membership function is determined based on the boundaries of each sub-fuzzy set corresponding to that dimension.

[0069] In this embodiment, the input membership functions include triangular membership functions and shoulder-shaped membership functions. The shoulder-shaped membership functions include left shoulder-shaped membership functions and right shoulder-shaped membership functions.

[0070] In this embodiment, for the input variable of the pseudocode delay dimension, the first input fuzzy set is determined based on the first fuzzy input variable after amplitude limiting. This first input fuzzy set may include seven sub-fuzzy sets: a first negative large (NL) fuzzy set, a first negative medium (NM) fuzzy set, a first negative small (NS) fuzzy set, a first zero (ZE) fuzzy set, a first positive small (PS) fuzzy set, a first positive medium (PM) fuzzy set, and a first positive large (PL) fuzzy set. The second sub-fuzzy set may include four fuzzy sets: a second negative large fuzzy set, a second negative medium fuzzy set, a second negative small fuzzy set, and a second zero fuzzy set.

[0071] In this embodiment, the first negative large fuzzy set and the first positive large fuzzy set are configured with shoulder-shaped membership functions, while the first negative medium fuzzy set, the first negative small fuzzy set, the first zero fuzzy set, the first positive small fuzzy set, and the first positive medium fuzzy set are configured with triangular membership functions. In the second sub-fuzzy set, the second negative large fuzzy set and the second zero fuzzy set are configured with shoulder-shaped membership functions, while the second negative medium fuzzy set and the second negative small fuzzy set are configured with triangular membership functions.

[0072] For the input variable in the Doppler frequency dimension, this embodiment determines the second input fuzzy set based on the second fuzzy input variable after amplitude limiting. The second input fuzzy set may include a third sub-fuzzy set and a fourth sub-fuzzy set. The third sub-fuzzy set may include seven fuzzy sets: a third negative large (NL) fuzzy set, a third negative medium (NM) fuzzy set, a third negative small (NS) fuzzy set, a third zero (ZE) fuzzy set, a third positive small (PS) fuzzy set, a third positive medium (PM) fuzzy set, and a third positive large (PL) fuzzy set. The fourth sub-fuzzy set may include four fuzzy sets: a fourth negative large fuzzy set, a fourth negative medium (NM) fuzzy set, a fourth negative small (NS) fuzzy set, and a fourth zero (ZE) fuzzy set.

[0073] In this embodiment, the third negative large fuzzy set and the positive large fuzzy set in the third sub-fuzzy set adopt a shoulder-shaped membership function, while the third negative medium fuzzy set, the third negative small fuzzy set, the third zero fuzzy set, the third positive small fuzzy set, and the third positive medium fuzzy set adopt a triangular membership function. In the fourth sub-fuzzy set, the fourth negative large fuzzy set and the fourth zero fuzzy set adopt a shoulder-shaped membership function, while the fourth negative medium fuzzy set and the fourth negative small fuzzy set adopt a triangular membership function. In this embodiment, the triangular membership function has a narrower range near its peak, higher resolution, and stronger sensitivity, providing precise control in the middle part of the fuzzy set. The shoulder-shaped membership function has saturation characteristics (flat-top region) in the extreme region, avoiding a rapid decrease in membership degree due to deviation.

[0074] In this embodiment, the triangle membership function The general parsing expression is:

[0075]

[0076] in, The left vertex of the triangle membership function is represented by the left vertex. This represents the upper vertex of the triangle's membership function. This represents the right vertex of the triangle's membership function. This represents the input variables, namely, the first fuzzy input variable after amplitude limiting, the second fuzzy input variable after amplitude limiting, the third fuzzy input variable after amplitude limiting, or the fourth fuzzy input variable after amplitude limiting. In this embodiment, the values ​​of the left vertex, the top vertex, and the right vertex of the triangle membership function can be obtained experimentally.

[0077] Shoulder-shaped membership functions include left shoulder-shaped membership functions. Membership function of right shoulder shape The membership function of the left shoulder shape can be expressed as:

[0078]

[0079] The membership function of the right shoulder shape can be expressed as:

[0080]

[0081] in, This represents the left inflection point of the shoulder-shaped membership function. This represents the right inflection point of the shoulder-shaped membership function. In this embodiment, the values ​​of the left and right inflection points of the shoulder-shaped membership function can be obtained experimentally.

[0082] For the input membership function of the pseudocode delay dimension, in this embodiment, the first fuzzy input variable after amplitude limiting and the second fuzzy input variable after amplitude limiting determine the input membership function. This input membership function includes the membership function corresponding to the first fuzzy input variable and the membership function corresponding to the second fuzzy input variable. The membership function corresponding to the first fuzzy input variable is shown in Table 1 below, and the membership function corresponding to the second fuzzy input variable is shown in Table 2 below.

[0083] Table 1 Membership functions corresponding to the first fuzzy input variable

[0084]

[0085] Table 2 Membership functions corresponding to the second fuzzy input variable

[0086]

[0087] Similarly, in this embodiment, the input membership functions corresponding to the first and second input variables of the Doppler frequency dimension have the same fuzzy set partitioning and function type settings as shown in Tables 1 and 2, and will not be repeated here.

[0088] In this embodiment, based on the fuzzy sets of the membership functions corresponding to the first fuzzy input variable and the corresponding membership functions, a schematic diagram of the membership functions corresponding to the first fuzzy input variable is determined, as shown below. Figure 2 As shown. Based on the various fuzzy results and corresponding membership functions of the membership functions corresponding to the second fuzzy input variable, a schematic diagram is drawn to determine the membership functions corresponding to the second fuzzy input variable, as shown below. Figure 3 As shown.

[0089] In this embodiment, the method for determining the input membership function of the Doppler frequency dimension is similar to the method for determining the input membership function of the pseudocode delay dimension, and will not be described in detail here.

[0090] As can be seen from the above, this embodiment designs an asymmetric fuzzy partition with different granularities (e.g., 7 levels for error and 4 levels for error change rate) for the two-dimensional input variables (error and error change rate). This ensures both the adjustment accuracy of the core variable (error) and the computational efficiency of the secondary variable (error change rate). Furthermore, the combination of shoulder-shaped functions at the edges of the universe of discourse and triangular functions in the middle ensures stable coverage of extreme cases while achieving a smooth transition in the intermediate region, making the characteristics of the fuzzy controller more aligned with practical engineering needs.

[0091] S105: For each dimension, perform a deviation value estimation operation in the fuzzy set corresponding to the input variable of that dimension to obtain the deviation estimate value corresponding to that dimension.

[0092] In one embodiment of this application, the deviation estimate is determined in the following manner:

[0093] For each dimension, based on the input variables of that dimension, and through the membership function corresponding to each fuzzy set in the fuzzy set, the membership value of each input variable in the corresponding subfuzzy set is obtained;

[0094] Based on each membership value and the output membership function corresponding to the input variable of that dimension, and through the preset inference rules, determine each output fuzzy set of that dimension, perform the maximum operation on each output fuzzy set, and obtain the inference result of that dimension.

[0095] The inference results are defuzzified to determine the bias estimate of the input variable for that dimension.

[0096] In one embodiment of this application, for each dimension, based on the input variables of that dimension and through the membership functions corresponding to each fuzzy set, the membership value of each input variable in the corresponding sub-fuzzy set is obtained, including:

[0097] The first input variable of this dimension is input into each of the first membership functions in the first input fuzzy set to obtain the first membership set of the first input variable of this dimension in each sub-fuzzy set; wherein, the first input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a first membership function;

[0098] The second input variable of this dimension is input into each of the second membership functions in the second input fuzzy set to obtain the second membership set of the second input variable of this dimension in each sub-fuzzy set; wherein, the second input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a second membership function.

[0099] In this embodiment, the input variables for each dimension include a first input variable and a second input variable, and the fuzzy set for each input dimension includes a first input fuzzy set and a second input fuzzy set.

[0100] In this embodiment, when performing the bias estimation operation for each dimension's input variable, it is first necessary to obtain the membership degree value of each input variable in each sub-fuzzy set of the corresponding fuzzy set. This embodiment takes the pseudocode delay dimension as an example. The input variables of this dimension include a first input variable and a second input variable (for example, the first input variable is the error value of the pseudocode delay, and the second input variable is the error change rate of the pseudocode delay).

[0101] Specifically, the current value of the first input variable is substituted into the first membership function (i.e., the function corresponding to each sub-fuzzy set) of each sub-fuzzy set to calculate the membership value of the first input variable in the corresponding sub-fuzzy set. These membership values ​​constitute the first membership set. Similarly, the current value of the second input variable is substituted into the second membership function of each sub-fuzzy set to obtain the membership value of the second input variable in the corresponding sub-fuzzy set, thus forming the second membership set. The first and second membership sets can completely describe the membership status of the two input variables at the current moment under the pseudocode delay dimension fuzzy partitioning, providing input fuzzification results for subsequent fuzzy inference.

[0102] For the Doppler frequency dimension, the processing method is exactly the same as that for the pseudocode delay dimension. The first input variable (e.g., the error value of the Doppler frequency) and the second input variable (e.g., the error rate of change of the Doppler frequency) of this dimension are input into the fuzzy set corresponding to this dimension, and the corresponding third and fourth membership sets are obtained through the same membership function calculation process.

[0103] In this embodiment, based on each membership value and the input membership function corresponding to the input variable of that dimension, and through a preset inference rule, an output fuzzy set of the input variable of that dimension is determined as the inference result (the specific implementation steps for determining the inference result are detailed in the following embodiment); the inference result is defuzzified to determine the deviation estimate of the input variable of that dimension (the specific implementation steps for determining the deviation estimate are detailed in the following embodiment).

[0104] As can be seen from the above, this embodiment constructs a complete and highly interpretable nonlinear mapping system by fuzzifying the precise input values ​​and using a rule base established based on expert experience for fuzzy reasoning. This system can effectively approximate the complex nonlinear relationship between the input deviation and the deviation value to be estimated, thereby obtaining better estimation accuracy than traditional interpolation or fitting methods.

[0105] In one embodiment of this application, each output fuzzy set of a given dimension is determined by a preset inference rule, and the inference result of that dimension is obtained by taking the largest value from each output fuzzy set. This includes:

[0106] For each dimension of input variable, based on each fuzzy rule, according to the subfuzzy set corresponding to the first input variable in the premise of the rule, the corresponding first membership value is obtained from the first membership set;

[0107] Based on the subfuzzy set corresponding to the second input variable in the premise of the rule, the corresponding second membership value is obtained from the second membership set, and the minimum value between the first membership value and the second membership value is taken as the activation strength of the rule.

[0108] The output fuzzy set of the rule is determined based on the activation strength of the rule and the membership function of the corresponding output subfuzzy set.

[0109] The maximum value of all output fuzzy sets is taken to obtain the output fuzzy set of that dimension, which is used as the output inference result of that dimension.

[0110] In this embodiment, the preset inference rules include multiple fuzzy rules. Each fuzzy rule includes a premise and a conclusion. The premise includes a subfuzzy set corresponding to the first input variable and a subfuzzy set corresponding to the second input variable. The conclusion includes the output subfuzzy set corresponding to the output quantity.

[0111] This embodiment first constructs a Mamdani-type fuzzy inference rule base. For the pseudocode delay dimension, each fuzzy rule in this rule base is represented in IF-THEN form, and its structure can be expressed as follows:

[0112] ,in, This represents the fuzzy set of the first fuzzy input variables after amplitude limiting. This represents the fuzzy set of the second fuzzy input variable after amplitude limiting. The fuzzy set representing the output quantity U.

[0113] The fuzzy control rule base in this embodiment is shown in Table 3 below.

[0114] Table 3 Fuzzy Control Rule Base

[0115]

[0116] In this embodiment, the fuzzy control rule base can be determined based on expert experience or simulation optimization.

[0117] This embodiment employs the dual-input, single-output Mamdani fuzzy inference method. For any fuzzy rule in the fuzzy control rule base, its inference model includes a major premise, a minor premise, and a conclusion. The major premise can be: If... belong , belong ,but belong The minor premise can be known. belong , belong The corresponding conclusion is belong .

[0118] in, Indicates input variables Fuzzy sets under minor premises Indicates input fuzzy variables Fuzzy sets under minor premises; Indicates output fuzzy variables A fuzzy set. , as well as The ranges are respectively compared to , as well as The scope is small. In this embodiment, the major premise is a static empirical rule, and the three membership degrees in the major premise are determined according to their respective membership functions.

[0119] The minor premise of this embodiment is that the specific value of the input variable at a certain moment is substituted into the corresponding membership function to obtain the corresponding specific value.

[0120] For each fuzzy rule in the rule base described above, this embodiment performs the following operations:

[0121] Extract the membership values ​​corresponding to the subfuzzy sets in the rule premise (major premise) from the first membership set; extract the membership values ​​corresponding to the second subfuzzy sets in the rule premise (major premise) from the second membership set.

[0122] The activation strength (trigger strength) of the rule is calculated using the minimum operation in Mamdani inference. This activation strength represents the degree to which the current input conditions satisfy the premise of the rule.

[0123] This embodiment is under a minor premise when Pick , Pick When, output variable Corresponding fuzzy set for:

[0124] ,

[0125] in, The Cartesian product operation represents a fuzzy set. This represents the composition operation of fuzzy sets. It represents a fuzzy relationship consisting of a major premise.

[0126] The fuzzy controller designed in this embodiment has a total of 28 fuzzy rules. Based on the above reasoning method, the output fuzzy set under the corresponding fuzzy rules can be obtained. .Will Taking the union of the sets yields the final fuzzy inference result, i.e., the output fuzzy set. ,in, .

[0127] In one embodiment of this application, the method for determining the output membership function includes:

[0128] For each dimension, determine the membership function of the fuzzy relationship in that dimension;

[0129] The output membership function is determined based on the first membership value, the second membership value, and the membership function of the fuzzy relation;

[0130] The output membership function is: ,in, This represents the membership value of the output quantity in the output fuzzy set for that dimension, where U represents the output quantity in that dimension. This represents the first membership value of this dimension. This represents the first input variable for this dimension. This represents the second membership value of this dimension. This represents the second input variable for this dimension. The membership function represents the fuzzy relation in this dimension, and E represents the fuzzy relation in this dimension. This indicates the larger operation. This indicates the operation of taking the smaller value.

[0131] In this embodiment, the ranges of the pseudocode delay coarse estimate and the Doppler frequency coarse estimate are obtained, and the range of the output quantity U is determined based on the first and second ranges (e.g., According to the range of values ​​of the output quantity U, the fuzzy universe corresponding to the output quantity U is divided into output fuzzy sets. In this embodiment, the output fuzzy sets include seven fuzzy sets, including the output negative large (NL) fuzzy set, the output negative medium (NM) fuzzy set, the output negative small (NS) fuzzy set, the output zero (ZE) fuzzy set, the output positive small (PS) fuzzy set, the output positive medium (PM) fuzzy set, and the output positive large (PL) fuzzy set.

[0132] The membership function of the output quantity U in this embodiment is shown in Table 4.

[0133] Table 4 Membership function of output quantity U

[0134]

[0135] In this embodiment, the fuzzy relationship is constituted by the first fuzzy input variable after amplitude limiting, the second fuzzy input variable, and the output quantity. The membership function of this fuzzy relationship is determined as follows:

[0136] ,in, Membership function representing fuzzy relations This represents the membership degree of the first input fuzzy variable under the major premise. This represents the membership degree of the second input fuzzy variable under the major premise. This represents the membership degree of the output quantity in the output fuzzy set. This indicates the operation of taking the smaller value.

[0137] The output membership function in this embodiment It can be represented as:

[0138] ,in, and These represent the first membership value and the second membership value, respectively. This indicates the larger operation.

[0139] Based on the fuzzy sets of the membership functions of the output quantities and the membership functions corresponding to each fuzzy set, the output membership function corresponding to the output quantity is determined, such as... Figure 4 As shown.

[0140] As can be seen from the above, this embodiment adopts Mamdani-type fuzzy inference, whose output membership function clearly defines the combined effect of multiple rules through the large-small operation. This inference method is intuitive, conforms to human thinking habits, and is simple to calculate, making it suitable for real-time applications in resource-constrained environments such as spaceborne computers.

[0141] In one embodiment of this application, a defuzzification operation is performed on the inference result to determine the bias estimate of the input variable for that dimension, including:

[0142] For the input variable of the pseudocode delay dimension, the first deviation estimate of the pseudocode delay dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable and through the first unfuzzy calculation formula.

[0143] The formula for calculating the first fuzzy solution is as follows: ,in, The deviation estimate of the input variable representing the pseudocode delay dimension. This represents the output fuzzy set corresponding to the input variable. Indicates the membership degree of the output quantity. Indicates the output quantity;

[0144] For the input variable in the Doppler frequency dimension, the bias estimate of the Doppler frequency dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable, and through the second unfuzzy calculation formula.

[0145] The second fuzzy solution calculation formula is as follows: ,in, This represents the bias estimate of the input variable in the Doppler frequency dimension. This represents the output fuzzy set corresponding to the input variable. This indicates the time for coherent accumulation.

[0146] In this embodiment, if the input variables of the fuzzy controller are the first and second input variables of the pseudocode delay dimension, after defuzzification by the first defuzzification calculation formula, the estimated deviation value of the pseudocode delay dimension of the input variables of the pseudocode delay dimension is:

[0147] ,in, This represents the estimated deviation of the pseudocode delay dimension.

[0148] If the input variables of the fuzzy controller are the first and second input variables of the Doppler frequency dimension, after defuzzification using the second defuzzification calculation formula, the obtained deviation estimate of the Doppler frequency dimension is:

[0149] ,in, This represents the bias estimate of the Doppler frequency dimension. This represents the output fuzzy set corresponding to the input variable. This indicates the time for coherent accumulation.

[0150] As can be seen from the above, in this embodiment, after the fuzzy inference engine performs inference based on the set rule base, its output result is a fuzzy set, which contains multiple possible states distributed on the output domain and their corresponding membership degrees. In this embodiment, the output fuzzy set obtained by inference is mapped to a single precise value that best represents the fuzzy distribution characteristics through a defuzzification operation (a specific mathematical mapping algorithm).

[0151] S106: Determine the precision capture value of the input variable in this dimension based on the estimated value of the input variable in this dimension and the corresponding deviation estimate.

[0152] In this embodiment, the fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value.

[0153] In this embodiment, after the acquisition and fine estimation of the pseudocode delay dimension and Doppler frequency dimension, the fine acquisition value of the pseudocode delay is determined based on the coarse estimate of the pseudocode delay dimension and the corresponding deviation estimate. ;

[0154] Based on the coarse estimate and the corresponding bias estimate of the Doppler frequency dimension, the fine acquisition value of the Doppler frequency is determined. .

[0155] As shown above, by algebraically summing the estimated value obtained from coarse acquisition (which contains grid quantization error) with the deviation estimate from fuzzy control output (precise correction), the precise acquisition values ​​for pseudocode delay and Doppler frequency are obtained respectively. This effectively compensates for the original estimation error, eliminates the inherent estimation bias caused by the two-dimensional discrete search grid, and allows the precise acquisition value to overcome the limitations of grid resolution and approximate the true values ​​of signal parameters. This provides a more accurate initial state for subsequent high-precision tracking loops, significantly improving the overall telemetry and control performance of the spaceborne telemetry and control transponder.

[0156] Based on the same principle as the fuzzy control-based precision acquisition method for measurement and control signals provided in the embodiments of this application, the embodiments of this application also provide a fuzzy control-based precision acquisition device for measurement and control signals, such as... Figure 5 As shown, the fuzzy control-based measurement and control signal fine acquisition device 20 may specifically include: a data acquisition module 21, a coarse estimate acquisition module 22, a value range determination module 23, a membership function determination module 24, a deviation estimate determination module 25, and a fine acquisition value determination module 26.

[0157] Among them, the data acquisition module 21 is used to acquire target input variables based on the baseband received signal in a two-dimensional discrete search grid of pseudocode delay and Doppler frequency. The target input variables include input variables in the pseudocode delay dimension and input variables in the Doppler frequency dimension.

[0158] The coarse estimation value acquisition module 22 is used to acquire the coarse estimation value of the pseudocode delay corresponding to the input variable in the pseudocode delay dimension and the coarse estimation value of the Doppler frequency corresponding to the input variable in the Doppler frequency dimension.

[0159] The value range determination module 23 is used to determine the value range corresponding to the input variable in the pseudocode delay dimension and the value range corresponding to the input variable in the Doppler frequency dimension.

[0160] The membership function determination module 24 is used to determine the input fuzzy set and its corresponding input membership function based on the input variables of the pseudocode delay dimension, and to determine the input fuzzy set and its corresponding input membership function based on the input variables of the Doppler frequency dimension.

[0161] The deviation estimation module 25 is used to perform a deviation estimation operation on each dimension in the fuzzy set corresponding to the input variable of that dimension, and obtain the deviation estimation value corresponding to that dimension.

[0162] The fine capture value determination module 26 is used to determine the fine capture value of the dimension based on the coarse estimate and bias estimate corresponding to the input variable of the dimension. The fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value.

[0163] In one embodiment of this application, when determining the deviation estimate, the deviation estimate determination module 25 is specifically used for:

[0164] For each dimension, based on the input variables of that dimension, and through the membership function corresponding to each fuzzy set in the fuzzy set, the membership value of each input variable in the corresponding subfuzzy set is obtained;

[0165] Based on each membership value and the output membership function corresponding to the input variable of that dimension, and through the preset inference rules, determine each output fuzzy set of that dimension, perform the maximum operation on each output fuzzy set, and obtain the inference result of that dimension.

[0166] The inference results are defuzzified to determine the bias estimate of the input variable for that dimension.

[0167] In one embodiment of this application, the input variables for each dimension include a first input variable and a second input variable, and the fuzzy set for each input dimension includes a first input fuzzy set and a second input fuzzy set;

[0168] When determining the membership value of each input variable in the corresponding sub-fuzzy set for each dimension, based on the input variable of that dimension and through the membership function corresponding to each fuzzy set, the deviation estimation module 25 is specifically used for:

[0169] The first input variable of this dimension is input into each of the first membership functions in the first input fuzzy set to obtain the first membership set of the first input variable of this dimension in each sub-fuzzy set; wherein, the first input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a first membership function;

[0170] The second input variable of this dimension is input into each of the second membership functions in the second input fuzzy set to obtain the second membership set of the second input variable of this dimension in each sub-fuzzy set; wherein, the second input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a second membership function.

[0171] In one embodiment of this application, the preset inference rule includes multiple fuzzy rules, wherein each fuzzy rule includes a premise and a conclusion. The premise includes a subfuzzy set corresponding to the first input variable and a subfuzzy set corresponding to the second input variable, and the conclusion includes an output subfuzzy set corresponding to the output quantity.

[0172] When determining the output fuzzy sets for this dimension using preset inference rules, and performing a maximum operation on each output fuzzy set to obtain the inference result for this dimension, the deviation value determination module 25 is specifically used for:

[0173] For each dimension of input variable, based on each fuzzy rule, according to the subfuzzy set corresponding to the first input variable in the premise of the rule, the corresponding first membership value is obtained from the first membership set;

[0174] Based on the subfuzzy set corresponding to the second input variable in the premise of the rule, the corresponding second membership value is obtained from the second membership set, and the minimum value between the first membership value and the second membership value is taken as the activation strength of the rule.

[0175] The output fuzzy set of the rule is determined based on the activation strength of the rule and the membership function of the corresponding output subfuzzy set.

[0176] The maximum value of all output fuzzy sets is taken to obtain the output fuzzy set of that dimension, which is used as the output inference result of that dimension.

[0177] In one embodiment of this application, when determining the output membership function, the deviation estimate determination module 25 is specifically used for:

[0178] For each dimension, determine the membership function of the fuzzy relationship in that dimension;

[0179] The output membership function is determined based on the first membership value, the second membership value, and the membership function of the fuzzy relation;

[0180] The output membership function is: ,in, This represents the membership value of the output quantity in the output fuzzy set for that dimension, where U represents the output quantity in that dimension. This represents the first membership value of this dimension. This represents the first input variable for this dimension. This represents the second membership value of this dimension. This represents the second input variable for this dimension. The membership function represents the fuzzy relation in this dimension, and E represents the fuzzy relation in this dimension. This indicates the larger operation. This indicates the operation of taking the smaller value.

[0181] In one embodiment of this application, when performing a defuzzification operation on the inference result to determine the bias estimate of the input variable for that dimension, the bias estimate determination module 25 is specifically used for:

[0182] For the input variable of the pseudocode delay dimension, the deviation estimate of the pseudocode delay dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable and through the first unfuzzy calculation formula.

[0183] The formula for calculating the first fuzzy solution is as follows: ,in, This represents the estimated bias value for the pseudocode delay dimension. This represents the output fuzzy set corresponding to the input variable. Indicates the membership degree of the output quantity. Indicates the output quantity;

[0184] For the input variable in the Doppler frequency dimension, the bias estimate of the Doppler frequency dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable, and through the second unfuzzy calculation formula.

[0185] The second fuzzy solution calculation formula is as follows: ,in, This represents the bias estimate in the Doppler frequency dimension. This represents the output fuzzy set corresponding to the input variable. This indicates the time for coherent accumulation.

[0186] In one embodiment of this application, the device further includes a membership function determination module, which is specifically used for:

[0187] For each dimension, the corresponding membership function is determined based on the boundary of each sub-fuzzy set corresponding to that dimension. The membership functions include triangular membership functions and shoulder-shaped membership functions. The shoulder-shaped membership functions include left shoulder-shaped membership functions and right shoulder-shaped membership functions.

[0188] The apparatus in this application embodiment can execute the method provided in this application embodiment, and the implementation principle is similar. The actions performed by each module in the apparatus of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0189] Figure 6 A schematic diagram of the structure of an electronic device to which this application embodiment applies is shown, such as... Figure 6 As shown, the electronic device can be used to implement the methods provided in any embodiment of this application.

[0190] like Figure 6 As shown, the electronic device 300 may primarily include at least one processor 301. Figure 6 The diagram shows components such as a memory 302, a communication module 303, and an input / output interface 304. Optionally, these components can be connected and communicate with each other via a bus 305. It should be noted that... Figure 6 The structure of the electronic device 300 shown is merely illustrative and does not constitute a limitation on the electronic devices to which the methods provided in the embodiments of this application are applicable.

[0191] The memory 302 can be used to store operating systems and applications, etc. The applications can include computer programs that implement the methods shown in the embodiments of this application when invoked by the processor 301, and can also include programs for implementing other functions or services. The memory 302 can be ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices that can store information and computer programs, or it can be EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0192] Processor 301 is connected to memory 302 via bus 305 and implements corresponding functions by calling the application programs stored in memory 302. Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0193] Electronic device 300 can connect to a network via communication module 303 (which may include, but is not limited to, components such as a network interface) to communicate with other devices (such as user terminals or servers) through the network and achieve data interaction, such as sending data to or receiving data from other devices. Communication module 303 may include wired network interfaces and / or wireless network interfaces, meaning the communication module may include at least one of wired or wireless communication modules.

[0194] The electronic device 300 can connect to necessary input / output devices, such as a keyboard or display device, via the input / output interface 304. The electronic device 300 itself may have a display device, and other display devices can also be connected externally via the interface 304. Optionally, a storage device, such as a hard drive, can also be connected via the interface 304, allowing data from the electronic device 300 to be stored, read, or transferred to the memory 302. It is understood that the input / output interface 304 can be a wired or wireless interface. Depending on the specific application scenario, the device connected to the input / output interface 304 can be an integral part of the electronic device 300 or an external device connected to the electronic device 300 when needed.

[0195] The bus 305 used to connect the components may include a path for transmitting information between the components. The bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Depending on its function, the bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0196] Optionally, for the solution provided in the embodiments of this application, the memory 302 can be used to store a computer program that executes the solution of this application, and the processor 301 runs the computer program. When the processor 301 runs the computer program, it implements the operation of the method or apparatus provided in the embodiments of this application.

[0197] Based on the same principle as the method provided in the embodiments of this application, the embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the corresponding content of the aforementioned method embodiments.

[0198] It should be noted that the terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0199] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0200] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0201] The above are only optional implementation methods for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A method for precise acquisition of measurement and control signals based on fuzzy control, characterized in that, include: In a two-dimensional discrete search grid of pseudocode delay and Doppler frequency, the target input variable is obtained based on the baseband received signal. The target input variable includes the input variable in the pseudocode delay dimension and the input variable in the Doppler frequency dimension. Obtain the coarse estimate of the pseudocode delay corresponding to the input variable of the pseudocode delay dimension and the coarse estimate of the Doppler frequency corresponding to the input variable of the Doppler frequency dimension; Determine the value range of the input variable corresponding to the pseudocode delay dimension and the value range of the input variable corresponding to the Doppler frequency dimension; The input fuzzy set and its corresponding input membership function are determined based on the input variables of the pseudocode delay dimension, and the input fuzzy set and its corresponding input membership function are determined based on the input variables of the Doppler frequency dimension. For each dimension, a deviation estimation operation is performed in the fuzzy set corresponding to the input variable of that dimension to obtain the deviation estimate for that dimension; The coarse estimate and the bias estimate corresponding to the input variable of this dimension are algebraically summed to obtain the fine capture value of this dimension. The fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value. The deviation estimate is determined in the following manner: For each dimension, based on the input variables of that dimension, and through the membership functions corresponding to each fuzzy set in the fuzzy set, the membership values ​​of each input variable in the corresponding subfuzzy set are obtained; Based on each membership value and the output membership function corresponding to the input variable of that dimension, and through preset inference rules, determine each output fuzzy set of that dimension, perform a maximum operation on each output fuzzy set, and obtain the inference result of that dimension. The inference result is defuzzified to determine the bias estimate of the input variable for that dimension; Each dimension's input variables include a first input variable and a second input variable, and each input dimension's fuzzy set includes a first input fuzzy set and a second input fuzzy set; wherein, the first input variable of the pseudocode delay dimension is the error of the pseudocode delay dimension, the second input variable of the pseudocode delay dimension is the error change rate of the pseudocode delay dimension, the first input variable of the Doppler frequency dimension is the error of the Doppler frequency dimension, and the second input variable of the Doppler frequency dimension is the error change rate of the Doppler frequency dimension; For each dimension, based on the input variable of that dimension and through the membership function corresponding to each fuzzy set in the fuzzy set, the membership value of each input variable in the corresponding sub-fuzzy set is obtained, including: The first input variable of this dimension is input into each of the first membership functions in the first input fuzzy set to obtain the first membership set of the first input variable of this dimension in each sub-fuzzy set; wherein, the first input fuzzy set is a preset fuzzy set of pseudocode delay dimension or a fuzzy set of Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a first membership function; The second input variable of this dimension is input into each of the second membership functions in the second input fuzzy set to obtain the second membership set of the second input variable of this dimension in each sub-fuzzy set; wherein, the second input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the second input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a second membership function; The preset reasoning rules include multiple fuzzy rules, each of which includes a premise and a conclusion. The premise includes a subfuzzy set corresponding to the first input variable and a subfuzzy set corresponding to the second input variable. The conclusion includes an output subfuzzy set corresponding to the output quantity. The step of determining each output fuzzy set of the dimension through preset inference rules, and performing a maximum operation on each output fuzzy set to obtain the inference result of the dimension includes: For each dimension of input variable, based on each fuzzy rule, according to the subfuzzy set corresponding to the first input variable in the premise of the rule, the corresponding first membership value is obtained from the first membership set; Based on the subfuzzy set corresponding to the second input variable in the premise of the rule, the corresponding second membership value is obtained from the second membership set, and the minimum value between the first membership value and the second membership value is taken as the activation strength of the rule. The output fuzzy set of the rule is determined based on the activation strength of the rule and the membership function of the corresponding output subfuzzy set. Take the largest value from all the output fuzzy sets to obtain the output fuzzy set for that dimension, and use it as the output inference result for that dimension. The method for determining the output membership function includes: For each dimension, determine the membership function of the fuzzy relationship in that dimension; The output membership function is determined based on the first membership value, the second membership value, and the membership function of the fuzzy relationship; The output membership function is: ,in, U represents the membership value of the output quantity in the output fuzzy set for that dimension. This represents the first membership value of this dimension. This represents the first input variable for this dimension. This represents the second membership value of this dimension. This represents the second input variable for this dimension. The membership function represents the fuzzy relation in this dimension, and E represents the fuzzy relation in this dimension. This indicates the larger operation. This indicates the operation of taking the smaller value.

2. The method as described in claim 1, characterized in that, The step of defuzzifying the inference result to determine the bias estimate of the input variable for that dimension includes: For the input variable of the pseudocode delay dimension, the deviation estimate of the pseudocode delay dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable and through the first unfuzzy calculation formula. The first fuzzy solution calculation formula is as follows: ,in, This represents the estimated bias value for the pseudocode delay dimension. This represents the output fuzzy set corresponding to the input variable. Indicates the membership degree of the output quantity. Indicates the output quantity; For the input variable of the Doppler frequency dimension, the bias estimate of the Doppler frequency dimension is determined based on the inference result, output quantity and membership degree of the output quantity corresponding to the input variable and through the second unfuzzy calculation formula. The second fuzzy solution calculation formula is as follows: ,in, This represents the bias estimate in the Doppler frequency dimension. This represents the output fuzzy set corresponding to the input variable. This indicates the time for coherent accumulation.

3. The method as described in claim 1, characterized in that, Also includes: For each dimension, the corresponding membership function is determined based on the boundary of each sub-fuzzy set corresponding to that dimension. The membership function includes a triangular membership function and a shoulder-shaped membership function. The shoulder-shaped membership function includes a left shoulder-shaped membership function and a right shoulder-shaped membership function.

4. A device for precise acquisition of measurement and control signals based on fuzzy control, characterized in that, include: The data acquisition module is used to acquire target input variables based on the baseband received signal in a two-dimensional discrete search grid of pseudocode delay and Doppler frequency. The target input variables include input variables in the pseudocode delay dimension and input variables in the Doppler frequency dimension. The coarse estimation value acquisition module is used to acquire the coarse estimation value of the pseudocode delay corresponding to the input variable of the pseudocode delay dimension and the coarse estimation value of the Doppler frequency corresponding to the input variable of the Doppler frequency dimension. The value range determination module is used to determine the value range corresponding to the input variable in the pseudocode delay dimension and the value range corresponding to the input variable in the Doppler frequency dimension. The membership function determination module is used to determine the input fuzzy set and its corresponding input membership function of the pseudocode delay dimension based on the input variables of the pseudocode delay dimension, and to determine the input fuzzy set and its corresponding input membership function of the Doppler frequency dimension based on the input variables of the Doppler frequency dimension. The deviation estimate determination module is used to perform deviation estimate operation for each dimension in the fuzzy set corresponding to the input variable of that dimension, and obtain the deviation estimate corresponding to that dimension. The fine capture value determination module is used to algebraically sum the coarse estimate and the bias estimate corresponding to the input variable of this dimension to obtain the fine capture value of this dimension. The fine capture value includes the pseudocode delay fine capture value and the Doppler frequency fine capture value. In determining the deviation estimate, the deviation estimate determination module is specifically used for: For each dimension, based on the input variables of that dimension, and through the membership function corresponding to each fuzzy set in the fuzzy set, the membership value of each input variable in the corresponding subfuzzy set is obtained; Based on each membership value and the output membership function corresponding to the input variable of that dimension, and through the preset inference rules, determine each output fuzzy set of that dimension, perform the maximum operation on each output fuzzy set, and obtain the inference result of that dimension. Perform a defuzzing operation on the inference results to determine the bias estimate of the input variable for that dimension; Each dimension's input variables include a first input variable and a second input variable, and each input dimension's fuzzy set includes a first input fuzzy set and a second input fuzzy set; wherein, the first input variable of the pseudocode delay dimension is the error of the pseudocode delay dimension, the second input variable of the pseudocode delay dimension is the error change rate of the pseudocode delay dimension, the first input variable of the Doppler frequency dimension is the error of the Doppler frequency dimension, and the second input variable of the Doppler frequency dimension is the error change rate of the Doppler frequency dimension; When determining the membership value of each input variable in the corresponding sub-fuzzy set for each dimension, based on the input variable of that dimension and through the membership function corresponding to each fuzzy set in the fuzzy set, the deviation estimate determination module is specifically used for: The first input variable of this dimension is input into each of the first membership functions in the first input fuzzy set to obtain the first membership set of the first input variable of this dimension in each sub-fuzzy set; wherein, the first input fuzzy set is a preset fuzzy set of pseudocode delay dimension or a fuzzy set of Doppler frequency dimension, and the first input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a first membership function; The second input variable of this dimension is input into each of the second membership functions in the second input fuzzy set to obtain the second membership set of the second input variable of this dimension in each sub-fuzzy set; wherein, the second input fuzzy set is a preset fuzzy set of the pseudocode delay dimension or a fuzzy set of the Doppler frequency dimension, and the second input fuzzy set includes multiple sub-fuzzy sets, each sub-fuzzy set corresponding to a second membership function; The preset inference rules include multiple fuzzy rules. Each fuzzy rule includes a premise and a conclusion. The premise includes the subfuzzy set corresponding to the first input variable and the subfuzzy set corresponding to the second input variable. The conclusion includes the output subfuzzy set corresponding to the output quantity. When determining the output fuzzy sets for a given dimension using preset inference rules, and performing a maximum operation on each output fuzzy set to obtain the inference result for that dimension, the deviation value determination module is specifically used for: For each dimension of input variable, based on each fuzzy rule, according to the subfuzzy set corresponding to the first input variable in the premise of the rule, the corresponding first membership value is obtained from the first membership set; Based on the subfuzzy set corresponding to the second input variable in the premise of the rule, the corresponding second membership value is obtained from the second membership set, and the minimum value between the first membership value and the second membership value is taken as the activation strength of the rule. The output fuzzy set of the rule is determined based on the activation strength of the rule and the membership function of the corresponding output subfuzzy set. Take the largest value from all the output fuzzy sets to obtain the output fuzzy set for that dimension, and use it as the output inference result for that dimension. The bias estimate determination module, used in determining the output membership function, is specifically used for: For each dimension, determine the membership function of the fuzzy relationship in that dimension; The output membership function is determined based on the first membership value, the second membership value, and the membership function of the fuzzy relation; The output membership function is: ,in, U represents the membership value of the output quantity in the output fuzzy set for that dimension. This represents the first membership value of this dimension. This represents the first input variable for this dimension. This represents the second membership value of this dimension. This represents the second input variable for this dimension. The membership function represents the fuzzy relation in this dimension, and E represents the fuzzy relation in this dimension. This indicates the larger operation. This indicates the operation of taking the smaller value.

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