CN-122017899-A - Measurement and control signal fine capturing method and device based on fuzzy control

CN122017899ACN 122017899 ACN122017899 ACN 122017899ACN-122017899-A

Abstract

The application provides a measurement and control signal fine capturing method and device based on fuzzy control, belonging to the field of signal processing of satellite-borne measurement and control answering machines, wherein the method comprises the steps of respectively obtaining a pseudo code delay coarse estimation value and a Doppler frequency coarse estimation value; the method comprises the steps of determining a value range corresponding to an input variable of a pseudo code delay dimension and a value range corresponding to an input variable of a Doppler frequency dimension, determining an input fuzzy set of the dimension and an input membership function corresponding to the input fuzzy set according to the input variable of the pseudo code delay dimension, determining the input fuzzy set of the dimension and the input membership function corresponding to the input fuzzy set according to the input variable of the Doppler frequency dimension, executing a deviation value estimation operation in the fuzzy set corresponding to the input variable of the dimension aiming at each dimension to obtain a deviation estimated value corresponding to the dimension, and determining a fine capture value of the dimension according to a rough estimated value and the deviation estimated value corresponding to the input variable of the dimension. The application can improve the estimation precision of the measurement and control signal.

Inventors

WANG YONGQING
XU YING
LI ZIHAN
BIAN GUOHUI
SHEN YUYAO
ZHANG QIANG
Feng Jining
QU JIAJIAN
LIU YIMING
YU QUANZHOU

Assignees

河北东森电子科技有限公司
北京理工大学
河北师范大学

Dates

Publication Date: 20260512
Application Date: 20260409

Claims (10)

1. A measurement and control signal fine capturing method based on fuzzy control is characterized by comprising the following steps: In a two-dimensional discrete search grid of pseudo code delay and Doppler frequency, acquiring a target input variable according to a baseband receiving signal, wherein the target input variable comprises an input variable of pseudo code delay dimension and an input variable of Doppler frequency dimension; Acquiring a pseudo code delay coarse estimation value corresponding to the input variable of the pseudo code delay dimension and a Doppler frequency coarse estimation value corresponding to the input variable of the Doppler frequency dimension; determining a value range corresponding to an input variable of the pseudo code delay dimension and a value range corresponding to an input variable of the Doppler frequency dimension; Determining an input fuzzy set and a corresponding input membership function of the dimension according to the input variable of the pseudo code delay dimension, and determining the input fuzzy set and the corresponding input membership function of the dimension according to the input variable of the Doppler frequency dimension; For each dimension, in a fuzzy set corresponding to an input variable of the dimension, performing a deviation value estimation operation to obtain a deviation estimation value corresponding to the dimension; and determining a fine acquisition value of the dimension according to the rough estimation value and the deviation estimation value corresponding to the input variable of the dimension, wherein the fine acquisition value comprises a pseudo code delay fine acquisition value and a Doppler frequency fine acquisition value.
2. The method of claim 1, wherein the bias estimate is determined by: Aiming at each dimension, obtaining a membership value of each input variable in a corresponding sub-fuzzy set according to the input variable of the dimension and through each membership function corresponding to each fuzzy set in the fuzzy set; according to each membership value and an output membership function corresponding to the input variable of the dimension, determining each output fuzzy set of the dimension through a preset reasoning rule, and performing a maximization operation on each output fuzzy set to obtain a reasoning result of the dimension; And performing a deblurring operation on the reasoning result to determine a deviation estimation value of the dimension input variable.
3. The method of claim 2, wherein the input variables for each dimension comprise a first input variable and a second input variable, and the fuzzy sets for each input dimension comprise a first input fuzzy set and a second input fuzzy set; According to the input variable of each dimension, and through each membership function corresponding to each fuzzy set in the fuzzy set, obtaining the membership value of each input variable in the corresponding sub-fuzzy set, including: Inputting a first input variable of the dimension into each first membership function in the first input fuzzy set to obtain a first membership set of the first input variable of the dimension in each sub fuzzy set, wherein the first input fuzzy set is a preset fuzzy set of a pseudo code delay dimension or a fuzzy set of a Doppler frequency dimension, and comprises a plurality of sub fuzzy sets, and each sub fuzzy set corresponds to one first membership function; Inputting a second input variable of the dimension into each second membership function in the second input fuzzy set to obtain a second membership set of the second input variable of the dimension in each sub fuzzy set, wherein the second input fuzzy set is a preset fuzzy set of a pseudo code delay dimension or a fuzzy set of a Doppler frequency dimension, the first input fuzzy set comprises a plurality of sub fuzzy sets, and each sub fuzzy set corresponds to one second membership function.
4. The method of claim 3, wherein the preset inference rules comprise a plurality of fuzzy rules, wherein each fuzzy rule comprises a premise and a conclusion, the premise comprising a sub-fuzzy set corresponding to a first input variable and a sub-fuzzy set corresponding to a second input variable, the conclusion comprising an output sub-fuzzy set corresponding to an output quantity; determining each output fuzzy set of the dimension through a preset reasoning rule, and performing a big operation on each output fuzzy set to obtain a reasoning result of the dimension, wherein the method comprises the following steps: aiming at the input variable of each dimension, based on each fuzzy rule, acquiring a corresponding first membership value from the first membership set according to a sub-fuzzy set corresponding to the first input variable in the precondition of the rule; Obtaining a corresponding second membership value from the second membership set according to a sub-fuzzy set corresponding to a second input variable in the rule premise, and taking the minimum value of the first membership value and the second membership value as the activation strength of the rule; Determining an output fuzzy set of the rule according to the activation intensity of the rule and the membership function of the corresponding output sub-fuzzy set; And performing a big operation on all the output fuzzy sets to obtain the output fuzzy set of the dimension, and taking the output fuzzy set of the dimension as an output reasoning result of the dimension.
5. The method of claim 4, wherein the determining the output membership function comprises: for each dimension, determining a membership function of the fuzzy relation of the dimension; determining an output membership function according to the first membership value, the second membership value and the membership function of the fuzzy relation; The output membership function is: , wherein, The membership value of the output quantity representing the dimension in the output fuzzy set, U represents the output quantity of the dimension, A first membership value representing the dimension, A first input variable representing the dimension, A second membership value representing the dimension, A second input variable representing the dimension, A membership function representing the fuzzy relationship of the dimension, E representing the fuzzy relationship of the dimension, The representation is a fetch-and-big operation, Representing a fetch operation.
6. The method of claim 5, wherein said performing a deblurring operation on said inference results to determine an estimate of the deviation of the dimension input variable comprises: Aiming at an input variable of a pseudo code delay dimension, determining a deviation estimation value of the pseudo code delay dimension according to an inference result, an output quantity and a membership degree of the output quantity corresponding to the input variable and through a first defuzzification calculation formula; The first solution ambiguity calculation formula is as follows: , wherein, A bias estimate representing the pseudo-code delay dimension, Representing the output fuzzy set corresponding to the input variable, Representing the degree of membership of the output quantity, Representing the output quantity; aiming at an input variable of the Doppler frequency dimension, determining a deviation estimation value of the Doppler frequency dimension through a second solution fuzzy calculation formula according to an inference result, an output quantity and a membership degree of the output quantity corresponding to the input variable; the second solution ambiguity calculation formula is as follows: , wherein, A bias estimate representing the doppler frequency dimension, Representing the output fuzzy set corresponding to the input variable, Indicating the time of the coherent accumulation.
7. A method as recited in claim 3, further comprising: For each dimension, determining a corresponding membership function according to the boundary of each sub-fuzzy set corresponding to the dimension, wherein the membership function comprises a triangular membership function and a shoulder membership function, and the shoulder membership function comprises a left shoulder membership function and a right shoulder membership function.
8. The utility model provides a measurement and control signal fine capture device based on fuzzy control which characterized in that includes: The data acquisition module is used for acquiring target input variables in a two-dimensional discrete search grid of pseudo code delay and Doppler frequency according to baseband receiving signals, wherein the target input variables comprise input variables of pseudo code delay dimension and input variables of Doppler frequency dimension; The coarse estimation value acquisition module is used for acquiring a pseudo code delay coarse estimation value corresponding to the input variable of the pseudo code delay dimension and a Doppler frequency coarse estimation value corresponding to the input variable of the Doppler frequency dimension; the value range determining module is used for determining a value range corresponding to the input variable of the pseudo code delay dimension and a value range corresponding to the input variable of the Doppler frequency dimension; The membership function determining module is used for determining an input fuzzy set of the dimension and a corresponding input membership function according to the input variable of the pseudo code delay dimension, and determining the input fuzzy set of the dimension and the corresponding input membership function according to the input variable of the Doppler frequency dimension; The deviation estimation value determining module is used for executing deviation value estimation operation in the fuzzy set corresponding to the input variable of each dimension to obtain a deviation estimation value corresponding to the dimension; and the fine acquisition value determining module is used for determining the fine acquisition value of the dimension according to the rough estimation value and the deviation estimation value corresponding to the input variable of the dimension, wherein the fine acquisition value comprises a pseudo code delay fine acquisition value and a Doppler frequency fine acquisition value.
9. The apparatus of claim 8, wherein the bias estimate determination module is configured to, in determining a bias estimate: Aiming at each dimension, according to the input variable of the dimension, and through each membership function corresponding to each fuzzy set in the fuzzy set, obtaining the membership value of each input variable in the corresponding sub-fuzzy set; According to each membership value and an output membership function corresponding to the input variable of the dimension, determining each output fuzzy set of the dimension through a preset reasoning rule, and performing a maximization operation on each output fuzzy set to obtain a reasoning result of the dimension; And performing a deblurring operation on the reasoning result to determine a deviation estimation value of the dimension input variable.
10. The apparatus of claim 9, wherein the input variables for each dimension comprise a first input variable and a second input variable, and the fuzzy sets for each input dimension comprise a first input fuzzy set and a second input fuzzy set; when obtaining the membership value of each input variable in the corresponding sub-fuzzy set according to the input variable of the dimension and through each membership function corresponding to each fuzzy set in the fuzzy set, the deviation estimation value determining module is specifically configured to: Inputting a first input variable of the dimension into each first membership function in the first input fuzzy set to obtain a first membership set of the first input variable of the dimension in each sub fuzzy set, wherein the first input fuzzy set is a preset fuzzy set of a pseudo code delay dimension or a fuzzy set of a Doppler frequency dimension, and comprises a plurality of sub fuzzy sets, and each sub fuzzy set corresponds to one first membership function; Inputting a second input variable of the dimension into each second membership function in the second input fuzzy set to obtain a second membership set of the second input variable of the dimension in each sub fuzzy set, wherein the second input fuzzy set is a preset fuzzy set of a pseudo code delay dimension or a fuzzy set of a Doppler frequency dimension, the first input fuzzy set comprises a plurality of sub fuzzy sets, and each sub fuzzy set corresponds to one second membership function.

Description

Measurement and control signal fine capturing method and device based on fuzzy control Technical Field The application belongs to the technical field of signal processing of satellite-borne measurement and control transponders, and particularly relates to a measurement and control signal fine capturing method and device based on fuzzy control. Background In satellite measurement and control systems, direct-spread signals are widely used because of their good anti-interference capability. However, the prior art faces two major core challenges, namely, on one hand, the conventional signal capturing method performs a search operation in a pseudo code delay and Doppler frequency two-dimensional discrete search grid, and takes parameters corresponding to grid points of the best matching grid as estimated values. Because the parameter value of the actual received signal has a continuity characteristic, the probability that the parameter true value just falls on the grid point of the discretization grid is extremely low, and the grid discretization characteristic causes inherent deviation between the estimated value and the true value, thereby directly affecting the performance precision of the follow-up tracking loop. On the other hand, in order to improve the estimation precision, the conventional method needs to refine the search grid to approach the true value, but the grid refinement can cause the exponential increase of the calculation complexity, and under the scene that the calculation resources of the satellite-borne measurement and control transponder are strictly limited, the rapid increase of the calculation cost directly threatens the real-time performance of the system, so as to form a contradiction dilemma between precision improvement and calculation efficiency. The defects are particularly prominent under the background of high-precision and real-time requirements of a satellite measurement and control system, and a technical scheme capable of eliminating estimation deviation caused by grid discretization and effectively controlling calculation complexity to ensure the real-time performance of the system is needed. Disclosure of Invention The method and the device for precisely capturing the measurement and control signals based on the fuzzy control provided by the embodiment of the application are used for solving the technical problems that in the prior art, inherent deviation exists between an estimated value and a true value caused by discrete grid search, and the calculation complexity is increased rapidly and the real-time performance of a system is influenced due to grid refinement, so that the purposes of precisely capturing the measurement and control signals with high precision and low complexity and improving the tracking performance and the real-time performance are realized. In order to achieve the above object, the technical solution provided by the embodiments of the present application is as follows: in a first aspect, a method for capturing measurement and control signals precisely based on fuzzy control is provided, including: In a two-dimensional discrete search grid of pseudo code delay and Doppler frequency, acquiring a target input variable according to a baseband receiving signal, wherein the target input variable comprises an input variable of pseudo code delay dimension and an input variable of Doppler frequency dimension; Acquiring a pseudo code delay coarse estimation value corresponding to an input variable of a pseudo code delay dimension and a Doppler frequency coarse estimation value corresponding to an input variable of a Doppler frequency dimension; determining a value range corresponding to an input variable of the pseudo code delay dimension and a value range corresponding to an input variable of the Doppler frequency dimension; determining an input fuzzy set and a corresponding input membership function of the dimension according to the input variable of the pseudo code delay dimension, and determining the input fuzzy set and the corresponding input membership function of the dimension according to the input variable of the Doppler frequency dimension; For each dimension, in a fuzzy set corresponding to an input variable of the dimension, performing a deviation value estimation operation to obtain a deviation estimation value corresponding to the dimension; and determining a fine acquisition value of the dimension according to the rough estimation value and the deviation estimation value corresponding to the input variable of the dimension, wherein the fine acquisition value comprises a pseudo code delay fine acquisition value and a Doppler frequency fine acquisition value. In a second aspect, a measurement and control signal fine capturing device based on fuzzy control is provided, including: The data acquisition module is used for acquiring a target input variable according to a baseband receiving signal in a two-dimensional discrete search grid of pseudo code