阅读说明：本技术 一种基于智能多普勒搜索的直扩信号捕获方法 (Direct sequence spread spectrum signal capturing method based on intelligent Doppler search ) 是由 史学森 何宜根 王永庆 申宇瑶 沈人豪 于 2021-08-25 设计创作，主要内容包括：本发明提供了一种基于智能多普勒搜索的直扩信号捕获方法,根据真实多普勒邻近区域和非邻近区域积累能量的变化特点,采用两种不同控制规则的模糊控制器进行多普勒频率步进的自适应调节,在非邻近区域采用大搜索步进,有效降低了多普勒搜索规模,在邻近区域采用小搜索步进,有效提高了多普勒捕获精度；同时,本发明在设计模糊控制器参数时考虑了非邻近区域和邻近区域可能产生的误捕,有效降低了信号捕获的误捕概率,即使在复杂条件下,也能够更好的兼顾直扩信号捕获的搜索规模和捕获精度。(The invention provides a direct sequence spread spectrum signal capturing method based on intelligent Doppler search, which adopts fuzzy controllers with two different control rules to perform adaptive adjustment of Doppler frequency stepping according to the change characteristics of accumulated energy of a real Doppler adjacent region and a non-adjacent region, adopts large search stepping in the non-adjacent region, effectively reduces the Doppler search scale, and adopts small search stepping in the adjacent region, thereby effectively improving the Doppler capturing precision; meanwhile, the method considers the error capturing possibly generated by a non-adjacent area and an adjacent area when designing the parameters of the fuzzy controller, effectively reduces the error capturing probability of signal capturing, and can better give consideration to the search scale and the capturing precision of direct sequence spread spectrum signal capturing even under the complex condition.)

1. A direct sequence spread spectrum signal acquisition method based on intelligent Doppler search is characterized by comprising the following steps:

s1: setting the Doppler frequency search range to [ -f_max,f_max]Acquiring energy accumulation results of the received direct spread signal r (t) at two set frequency points by adopting a non-coherent accumulation method, and respectively taking the two energy accumulation results as first detection variablesAnd a second detected variableWherein f is_iHas an initial frequency point of-f_max，f_i+1Has an initial frequency point of-f_max+1/2T_coh，f_maxIs a set value, T_cohFor coherent integration time, N is the number of points of FFT performed on the pseudo code of the direct-spread signal r (t),andrespectively the energy value at each point;

s2: and constructing initial values of an input variable x and an input variable y of the first fuzzy controller according to the first detection variable and the second detection variable as follows:

wherein the content of the first and second substances, is composed ofThe corresponding pseudo-code phase value is,is composed ofThe corresponding pseudo-code phase value is,

s3: inputting an input variable x and an input variable y into a first fuzzy controller to obtain a Doppler search step, and judging whether the Doppler search step is smaller than a set threshold gamma₁If it is smaller than the above range, the process proceeds to step S4, and if it is not smaller than the above range,the frequency point f of the iteration_i+1Frequency point f as next iteration_iAnd will use the frequency point f of this iteration_i+1The frequency point obtained by taking the currently obtained Doppler search step as the step length as the frequency point f of the next iteration as the starting point_i+1Then based on updated f_iAnd f_i+1Re-obtaining the first detection variable and the second detection variable, and repeating the steps S2-S3 until the Doppler search step is smaller than the set threshold gamma₁；

S4: stepping the Doppler search to be less than a set threshold gamma₁Frequency point f corresponding to time_i+1Is marked as frequency point f_MRespectively convert the frequency points f_MThe frequency points corresponding to the maximum values of the energy accumulation results in the left neighborhood and the right neighborhood are respectively recorded as f_L、f_RAnd according to the frequency point f_L、f_M、f_RCorresponding detected variable obtains input variable of second fuzzy controllerAnd

s5: will input variableAndinputting the output variable delta u to a second fuzzy controller_tJudgment of Δ u_tWhether is less than a set threshold gamma₂Wherein γ is₂＜γ₁If the frequency point is smaller than the preset frequency point, the frequency point f of the iteration is determined_MAs the Doppler frequency value of the direct spread spectrum signal r (t), and obtaining the frequency point f of the iteration_MThe corresponding pseudo code phase value completes the capture of the direct sequence spread spectrum signal; if not, go to step S6;

s6: using the output variable Deltau u_tUpdating the frequency point f in the iteration as Doppler search stepping_MObtaining the updated frequency pointUsing frequency pointsReplacing the frequency point f according to the set rule_L、f_M、f_RGet updated f_L、f_M、f_RAnd obtaining updated f_L、f_M、f_RCorresponding input variableAnd

s7: input variables to be updatedAndstep S5 is repeated until the output variable Δ u_tLess than a set threshold gamma₂。

2. The method as claimed in claim 1, wherein the step S3 of inputting the input variable x and the input variable y into the first fuzzy controller, the step of obtaining doppler search specifically comprises:

the input variable x is blurred into fuzzy quantities according to a first set fuzzy rule, and all possible fuzzy quantities of the input variable x comprise Positive Small (PS), Positive Medium (PM) and Positive Large (PL);

blurring the input variable y into fuzzy quantities according to a second set blurring rule, wherein all possible fuzzy quantities of the input variable y comprise Negative Large (NL), Negative Small (NS), Zero (ZE), Positive Small (PS) and Positive Large (PL);

based on a set fuzzy control rule, obtaining fuzzy quantities of an output variable z of the first fuzzy controller according to current possible fuzzy quantities of an input variable x and a current possible fuzzy quantity of an input variable y, wherein all the possible fuzzy quantities of the output variable z comprise Zero (ZE), Positive Small (PS), Positive Medium (PM), positive large (PL1) and maximum large (PL 2);

and resolving the fuzzy of the current possible fuzzy quantity of the output variable z by adopting an area center method to obtain the Doppler search stepping.

3. The method as claimed in claim 2, wherein the input variable x has both physical and fuzzy domains [95,106], and the first set fuzzy rule is as follows:

wherein f is₁(x) Membership function corresponding to positive and small fuzzy quantity (PS);

wherein f is₂(x) Is a membership function corresponding to the median (PM) of the fuzzy quantity;

wherein f is₃(x) Is the membership function corresponding to the positive fuzzy quantity (PL).

4. The method as claimed in claim 2, wherein the input variable y has a physical argument and an ambiguity range of [ -4,4], and the second fuzzy rule is as follows:

wherein f is₁(y) is a membership function corresponding to the negative large fuzzy quantity (NL);

wherein f is₂(y) is a membership function corresponding to the negative minimum (NS) of the fuzzy quantity;

wherein f is₃(y) is a membership function corresponding to the fuzzy quantity Zero (ZE);

wherein f is₄(y) is a membership function corresponding to the positive and negative fuzzy quantity (PS);

wherein f is₅And (y) is a membership function corresponding to the positive fuzzy quantity (PL).

5. The method as claimed in claim 2, wherein the fuzzy control rule is as follows:

6. the method as claimed in claim 5, wherein the output variable z has a physical universe of arguments [200,1200], a universe of ambiguities [0,10], and all possible ambiguities of the output variable z have the following membership functions:

wherein f is₁(z) is a membership function corresponding to the fuzzy quantity Zero (ZE);

wherein f is₂(z) is a membership function corresponding to the positive and negative fuzzy quantity (PS);

wherein f is₃(z) is a membership function corresponding to the median (PM) of the fuzzy quantity;

wherein f is₄(z) is a membership function corresponding to the positive fuzzy quantity (PL 1);

wherein f is₅(z) is a membership function corresponding to the maximum blur magnitude (PL 2).

Technical Field

The invention belongs to the field of aerospace measurement and control communication, and particularly relates to a direct sequence spread spectrum signal acquisition method based on intelligent Doppler search.

Background

Acquisition of a direct-spread signal requires a rough estimation of the doppler frequency and the pseudocode phase value of the signal before tracking at the receiver to assist the receiver in initializing the tracking loop and tracking the signal. The performance of signal acquisition is therefore critical to the success of the tracking loop in pulling, settling, and properly tracking the received signal. In the transmission process of the space measurement and control signal, the strength of the signal is greatly changed due to the influence of the environment and a transmission path, and the equivalent noise power is greatly changed due to non-ideal factors such as interference, and the adverse factors can seriously influence the signal acquisition performance. Firstly, when the doppler dynamic change is large, the change range of the doppler frequency is increased, and in signal acquisition, the search range of the doppler frequency needs to be increased to ensure a high acquisition probability, which inevitably causes a large increase in the search scale, thereby prolonging the acquisition time. Secondly, when the power of the signal changes greatly, the signal capture is easy to be mistakenly captured at the abnormal point of the power change, so that the tracking loop of the receiver is initialized under the wrong parameters, and the loop cannot be normally locked. In addition, when the doppler search is performed, if the true doppler falls in the middle of the frequency search interval, the doppler acquisition error is large, thereby increasing the locking time of the tracking loop and even making it difficult to lock. In order to improve the direct sequence spread spectrum signal capture performance, a great deal of research is carried out by scholars at home and abroad. The acquisition search strategy is used as an important factor for determining the quality of signal acquisition performance, and not only influences the signal acquisition time, but also influences the Doppler acquisition error and the false acquisition probability. Therefore, the method for improving the signal acquisition performance by optimizing the acquisition search strategy has important research significance.

The conventional capture search strategy is classified from the search mode, and can be divided into three modes, namely serial search, parallel search and mixed search. The serial search mode firstly determines the search range of the frequency and the code phase, and then searches each frequency point and the code phase in the range one by one according to a certain sequence to obtain the detection variable. Serial search, although simple to implement, is slow because it searches only one grid cell at a time. The parallel search refers to the simultaneous parallel search of frequency dimension and code phase dimension to obtain the detection variable, and the search mode has the advantages of higher acquisition speed but higher requirement on hardware resources. The hybrid search comprises two modes of frequency parallel-code phase serial search and frequency serial-code phase parallel search. The potential code phase delay is traversed in a frequency parallel-code phase serial search mode, and the Doppler frequency search is completed by performing FFT (fast Fourier transform) on each code phase, so that the calculation amount can be reduced to a certain extent, and the search speed is increased. However, due to the influence of the FFT frequency resolution, the attenuation of the detection amount is severe at a frequency point far from the correct doppler, resulting in a deterioration in the signal acquisition sensitivity. In addition, the frequency parallel algorithm needs to perform one FFT on each code phase, and the search speed is slow for the case of a long pseudo code. The frequency serial-code phase parallel search traverses potential Doppler frequency points in a linear search mode, and completes code phase search on each frequency point through one FFT conversion. The strategy can balance the relation between the resource consumption of the acquisition algorithm and the search speed, and is a search mode widely applied to a direct sequence spread spectrum signal acquisition system. In order to improve the direct sequence spread spectrum signal acquisition performance, a large number of improved search strategies are proposed successively on the basis of the conventional search method, such as a multi-round search strategy for improving the acquisition accuracy and a local search strategy for reducing the search scale. However, the existing search strategy is difficult to consider both the search scale and the capture precision under complex conditions, and further research is necessary.

Disclosure of Invention

In order to solve the above problems, the present invention provides a method for capturing a direct sequence spread spectrum signal based on an intelligent doppler search, which effectively improves the accuracy of capturing doppler.

A direct sequence spread spectrum signal acquisition method based on intelligent Doppler search comprises the following steps:

s1: setting the Doppler frequency search range to [ -f_max,f_max]Acquiring energy accumulation results of the received direct spread signal r (t) at two set frequency points by adopting a non-coherent accumulation method, and respectively taking the two energy accumulation results as first detection variablesAnd a second detected variableWherein f is_iHas an initial frequency point of-f_max，f_i+1Has an initial frequency point of-f_max+1/2T_coh，f_maxIs a set value, T_cohFor coherent integration time, N is the number of points of FFT performed on the pseudo code of the direct-spread signal r (t),andrespectively the energy value at each point;

s2: and constructing initial values of an input variable x and an input variable y of the first fuzzy controller according to the first detection variable and the second detection variable as follows:

wherein the content of the first and second substances, is composed ofThe corresponding pseudo-code phase value is,is composed ofThe corresponding pseudo-code phase value is,

s3: inputting the input variable x and the input variable y into a first fuzzy controller to obtain Doppler search stepping and judgingWhether the Doppler search step is smaller than a set threshold gamma or not₁If the frequency is less than the preset frequency, the step S4 is entered, and if the frequency is not less than the preset frequency, the frequency f of the iteration is added_i+1Frequency point f as next iteration_iAnd will use the frequency point f of this iteration_i+1The frequency point obtained by taking the currently obtained Doppler search step as the step length as the frequency point f of the next iteration as the starting point_i+1Then based on updated f_iAnd f_i+1Re-obtaining the first detection variable and the second detection variable, and repeating the steps S2-S3 until the Doppler search step is smaller than the set threshold gamma₁；

S4: stepping the Doppler search to be less than a set threshold gamma₁Frequency point f corresponding to time_i+1Is marked as frequency point f_MRespectively convert the frequency points f_MThe frequency points corresponding to the maximum values of the energy accumulation results in the left neighborhood and the right neighborhood are respectively recorded as f_L、f_RAnd according to the frequency point f_L、f_M、f_RCorresponding detected variable obtains input variable of second fuzzy controller And

s5: will input variableAndinputting the output variable delta u to a second fuzzy controller_tJudgment of Δ u_tWhether is less than a set threshold gamma₂Wherein γ is₂＜γ₁If the frequency point is smaller than the preset frequency point, the frequency point f of the iteration is determined_MAs the Doppler frequency value of the direct spread spectrum signal r (t), and obtaining the frequency point f of the iteration_MCorresponding pseudo code phaseCompleting the capture of the direct sequence spread spectrum signal; if not, go to step S6;

s6: using the output variable Deltau u_tUpdating the frequency point f in the iteration as Doppler search stepping_MObtaining the updated frequency pointUsing frequency pointsReplacing the frequency point f according to the set rule_L、f_M、f_RGet updated f_L、f_M、f_RAnd obtaining updated f_L、f_M、f_RCorresponding input variableAnd

s7: input variables to be updatedAndstep S5 is repeated until the output variable Δ u_tLess than a set threshold gamma₂。

Further, in step S3, the inputting the input variable x and the input variable y into the first fuzzy controller to obtain the doppler search step specifically includes:

the input variable x is blurred into fuzzy quantities according to a first set fuzzy rule, and all possible fuzzy quantities of the input variable x comprise Positive Small (PS), Positive Medium (PM) and Positive Large (PL);

blurring the input variable y into fuzzy quantities according to a second set blurring rule, wherein all possible fuzzy quantities of the input variable y comprise Negative Large (NL), Negative Small (NS), Zero (ZE), Positive Small (PS) and Positive Large (PL);

based on a set fuzzy control rule, obtaining fuzzy quantities of an output variable z of the first fuzzy controller according to current possible fuzzy quantities of an input variable x and a current possible fuzzy quantity of an input variable y, wherein all the possible fuzzy quantities of the output variable z comprise Zero (ZE), Positive Small (PS), Positive Medium (PM), positive large (PL1) and maximum large (PL 2);

and resolving the fuzzy of the current possible fuzzy quantity of the output variable z by adopting an area center method to obtain the Doppler search stepping.

Further, the input variable x has both physics and ambiguity fields [95,106], and the first set ambiguity rule is as follows:

wherein f is₁(x) Membership function corresponding to positive and small fuzzy quantity (PS);

wherein f is₂(x) Is a membership function corresponding to the median (PM) of the fuzzy quantity;

wherein f is₃(x) Is the membership function corresponding to the positive fuzzy quantity (PL).

Further, the physics and ambiguity domains of the input variable y are both [ -4,4], and the second set ambiguity rule is as follows:

wherein f is₁(y) is a membership function corresponding to the negative large fuzzy quantity (NL);

wherein f is₂(y) is a membership function corresponding to the negative minimum (NS) of the fuzzy quantity;

wherein f is₃(y) is a membership function corresponding to the fuzzy quantity Zero (ZE);

wherein f is₄(y) is a membership function corresponding to the positive and negative fuzzy quantity (PS);

wherein f is₅And (y) is a membership function corresponding to the positive fuzzy quantity (PL).

Further, the fuzzy control rule is set as follows:

further, the output variable z has a physical universe of discourse [200,1200], a universe of ambiguity [0,10], and all possible ambiguities of the output variable z are associated with the following membership functions:

wherein f is₁(z) is a membership function corresponding to the fuzzy quantity Zero (ZE);

wherein f is₂(z) is a membership function corresponding to the positive and negative fuzzy quantity (PS);

wherein f is₃(z) is a membership function corresponding to the median (PM) of the fuzzy quantity;

wherein f is₄(z) is a membership function corresponding to the positive fuzzy quantity (PL 1);

wherein f is₅(z) is a membership function corresponding to the maximum blur magnitude (PL 2).

Has the advantages that:

1. the invention provides a direct sequence spread spectrum signal capturing method based on intelligent Doppler search, which adopts fuzzy controllers with two different control rules to perform adaptive adjustment of Doppler frequency stepping according to the change characteristics of accumulated energy of a real Doppler adjacent region and a non-adjacent region, adopts large search stepping in the non-adjacent region, effectively reduces the Doppler search scale, and adopts small search stepping in the adjacent region, thereby effectively improving the Doppler capturing precision; meanwhile, the method considers the error capturing possibly generated by a non-adjacent area and an adjacent area when designing the parameters of the fuzzy controller, effectively reduces the error capturing probability of signal capturing, and can better give consideration to the search scale and the capturing precision of direct sequence spread spectrum signal capturing even under the complex condition.

2. The invention provides a direct sequence spread spectrum signal capturing method based on intelligent Doppler search, which sets an initial value of a second fuzzy controller FL _2 through traversal and point selection, not only can effectively avoid the problem of sidelobe miscapturing, but also can solve the problem that the searching stepping direction deviates from the actual frequency point due to the fact that the signal capturing starting time is earlier than the signal arrival time.

Drawings

Fig. 1 is a direct sequence spread spectrum signal acquisition system model based on intelligent doppler search provided by the present invention;

FIG. 2 is a membership function of an input variable x provided by the present invention;

FIG. 3 is a membership function of an input variable y provided by the present invention;

fig. 4 is a membership function of the output variable z provided by the present invention.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.

The invention provides a direct sequence spread spectrum signal capturing method based on an intelligent Doppler search strategy, which adopts two fuzzy controllers with different control rules to adaptively adjust Doppler search stepping according to the change characteristics of signal accumulation energy of a real Doppler adjacent region and a non-adjacent region. Parameters of the fuzzy controller are designed in non-adjacent areas with the aim of reducing search scale and reducing false capture. The parameters of the fuzzy controller are designed in the neighboring area with the aim of improving the Doppler acquisition accuracy.

Specifically, a direct sequence spread spectrum signal acquisition method based on intelligent Doppler search comprises the following steps:

s1: setting the Doppler frequency search range to [ -f_max,f_max]Acquiring energy accumulation results of the received direct spread signal r (t) at two set frequency points by adopting a non-coherent accumulation method, and respectively taking the two energy accumulation results as first detection variablesAnd a second detected variableWherein f is_iHas an initial frequency point of-f_max，f_i+1Has an initial frequency point of-f_max+1/2T_coh，f_maxIs a set value, T_cohFor coherent integration time, N is the number of points of FFT performed on the pseudo code of the direct-spread signal r (t),andrespectively, the energy values at each point.

It should be noted that a model of a direct sequence spread spectrum signal acquisition system based on intelligent doppler search is shown in fig. 1, wherein the portion inside the dotted line is the intelligent doppler search method provided by the present invention. The direct sequence spread spectrum signal acquisition needs to estimate the Doppler frequency and the pseudo code phase, and can be regarded as a two-dimensional search process of the Doppler frequency and the pseudo code phase. The pseudo code phase searching method adopts a parallel searching mode based on FFT, and the Doppler searching adopts the provided intelligent Doppler searching method.

Further, the step of acquiring the energy accumulation result of the received direct sequence spread spectrum signal at each frequency point by using a non-coherent accumulation method specifically includes the following steps:

the received signal r (t) of the direct-sequence spread-spectrum signal of the signal type is an intermediate frequency signal output by the rf front end, and can be represented as:

where A is the frequency spectrum of the signal, τ is the pseudo-code delay, f_IAt an intermediate frequency, f_dIs the frequency of the doppler frequency and is,is unknown carrier phase of the input signal, d (t) is information data, c (t) is Pseudo-random noise (PRN), and n (t) is gaussian noise. For ease of discussion, we assume that no information data is modulated on the pseudo code, i.e., d (t) is 1. Local regeneration is required in response to the received signalThe in-phase and quadrature components of the line-related processed IF signal are represented as

Wherein the content of the first and second substances,anddelay tau by pseudo code and Doppler frequency f, respectively_dAn estimate of (d). After correlation processing, I, Q two paths of correlation results are generated, which are respectively expressed as:

wherein n is_IIs path I noise, n_QFor Q-path noise, the correlation result can be written as

Where R (-) is a cross-correlation function, Δ τ is the time delay between the local code and the received code, Δ f_dIs the actual Doppler f_dAnd estimating DopplerThe frequency difference between them, T is the correlation time. When the energy accumulation is carried out by adopting the incoherent accumulation method, the detection variable is expressed as

Wherein L is the number of incoherent accumulations, Z_kIs the correlation value over the kth code period.

S2: and constructing initial values of an input variable x and an input variable y of the first fuzzy controller according to the first detection variable and the second detection variable as follows:

wherein the content of the first and second substances, is composed ofThe corresponding pseudo-code phase value is,is composed ofThe corresponding pseudo-code phase value is,

s3: inputting an input variable x and an input variable y into a first fuzzy controller to obtain a Doppler search step, and judging whether the Doppler search step is smaller than a set threshold gamma₁If the frequency is less than the preset frequency, the step S4 is entered, and if the frequency is not less than the preset frequency, the frequency f of the iteration is added_i+1Frequency point f as next iteration_iAnd will use the frequency point f of this iteration_i+1The frequency point obtained by taking the currently obtained Doppler search step as the step length as the frequency point f of the next iteration as the starting point_i+1Then toAt updated f_iAnd f_i+1Re-obtaining the first detection variable and the second detection variable, and repeating the steps S2-S3 until the Doppler search step is smaller than the set threshold gamma₁。

S4: stepping the Doppler search to be less than a set threshold gamma₁Frequency point f corresponding to time_i+1Is marked as frequency point f_MRespectively convert the frequency points f_MThe frequency points corresponding to the maximum values of the energy accumulation results in the left neighborhood and the right neighborhood are respectively recorded as f_L、f_RAnd according to the frequency point f_L、f_M、f_RCorresponding detected variable obtains input variable of second fuzzy controller And

s5: will input variableAndinputting the output variable delta u to a second fuzzy controller_tJudgment of Δ u_tWhether is less than a set threshold gamma₂Wherein γ is₂＜γ₁If the frequency point is smaller than the preset frequency point, the frequency point f of the iteration is determined_MAs the Doppler frequency value of the direct spread spectrum signal r (t), and obtaining the frequency point f of the iteration_MThe corresponding pseudo code phase value completes the capture of the direct sequence spread spectrum signal; if not, the process proceeds to step S6.

S6: using the output variable Deltau u_tUpdating the frequency point f in the iteration as Doppler search stepping_MObtaining the updated frequency pointBy usingFrequency pointReplacing the frequency point f according to the set rule_L、f_M、f_RGet updated f_L、f_M、f_RAnd obtaining updated f_L、f_M、f_RCorresponding input variableAnd

it should be noted how to obtain the frequency point after each updateSubstitution f_L、f_M、f_RFor one of them, the specific alternative rules are described in the article (Fuzzy logic control for Doppler search in DSSS systems, IEEE transactions on Fuzzy systems, 2020), and are not described herein again.

S7: input variables to be updatedAndstep S5 is repeated until the output variable Δ u_tLess than a set threshold gamma₂。

The following describes the intelligent doppler search method according to the present invention in detail.

The search for the doppler frequency is divided into three stages, defined as state S1, state S2, and state S3, respectively. The initial stage of the system is state S1, and assuming that the search frequency is located in a non-adjacent area at this stage, the first fuzzy controller FL _1 is used to perform the search, resulting in the doppler search step at the initial stage. When the system decides that the search frequency is already in the true doppler neighborhood, the system state transitions to the S2 state. The system traverses the search frequency of the real Doppler neighboring region in the S2 state, obtains the input variable of the second fuzzy controller FL _2 after selecting points, and the system is converted into the S3 state. When the system is in the state of S3, a search is performed using the FL _2 fuzzy controller until the system decides to successfully acquire the signal.

In the intelligent Doppler search method provided by the present invention, the parameters of the first Fuzzy controller FL _1 are designed in detail, and the parameters of the second Fuzzy controller FL _2 are the parameters of the Fuzzy controller proposed in the article published by Xuesen Shi et al (Fuzzy logic control for Doppler search in DSSS systems, IEEE transactions on Fuzzy systems) in 2020. The parameter design of the first fuzzy controller FL _1 is explained in detail below.

The first fuzzy controller FL _1 adopts a double-input-single-output Mamdani type fuzzy controller, which is mainly divided into three parts: fuzzification, fuzzy reasoning and deblurring.

(1) Fuzzification

Fuzzification refers to finding the degree of membership that a real number value belongs to each relevant fuzzy subset after the input and output variables are mapped to the real number value on the fuzzy subset.

The input variable x is fuzzified, the fuzzy subset number of x is set to be 3, namely Positive Small (PS), Positive Medium (PM) and Positive Large (PL).

As shown in FIG. 2, the membership function of PS is

Membership function of PM of

Membership function of PL of

Both the physics and ambiguity domains of the input variable y are [ -4,4 ]. The input variable y is fuzzified, and the fuzzy subset number of y is set to be 5, namely Negative Large (NL), Negative Small (NS), Zero (ZE), PS and PL.

As shown in FIG. 3, the membership function of NL is

Membership function of NS of

Membership function of ZE as

Membership function of PS of

Membership function of PL of

The output variable z is the Doppler search step output by the fuzzy controller, and the physical domain is [200,1200], and the fuzzy domain is [0,10 ]. The output variable z is blurred, and the number of fuzzy subsets of z is set to 5, namely ZE, PS, PM, PL1 and PL 2.

As shown in FIG. 4, the membership function of ZE is

Membership function of PS of

Membership function of PM of

Membership function of PL1 of

Membership function of PL2 of

(2) Fuzzy inference

Fuzzy inference is the theoretical basis of fuzzy controller design, and refers to a process of deducing a possibly inaccurate conclusion from an imprecise premise according to a fuzzy control rule, that is, the fuzzy inference refers to a process of deducing a fuzzy output variable from a fuzzy input variable through a certain inference method according to the fuzzy control rule. The design of the fuzzy rule mainly depends on expert experience knowledge, and the more abundant the experience is, the more accurate the fuzzy control is. The fuzzy rule design of the invention summarizes and summarizes the fuzzy control rule for fuzzy reasoning by analyzing experimental test data, as shown in table 1.

TABLE 1 fuzzy control rules

For example, when the input variable x is PS and the input variable y is NL, the output variable z is PL 2.

(3) Deblurring

Deblurring is the process of equating a fuzzy set output through fuzzy inference to a distinct value, also called as sharpening. And performing deblurring processing by adopting an area center method. The area-centric method is to find the fuzzy set membership function curve and the center of the area surrounded by the abscissa, and then to take the abscissa of the center as the output value. The calculation principle of the area center method is

Wherein u is an output variable, and U (u) is a fuzzy domain N_uA membership function of the fuzzy set U. For example, when the input variable x is PS and the input variable y is NL and the output variable z is PL2, the ambiguity is resolved for the output variable z by finding the center of the area of the membership function curve corresponding to PL2 and the area enclosed by the abscissa, and then taking the abscissa of the center as the output value, that is, the doppler search step of the initial stage S1 state is obtained.

For the parameter design of the FL _2 Fuzzy controller, see the article (Fuzzy logic control for Doppler search in DSSS systems, IEEE transactions on Fuzzy systems, 2020), which is not described herein again.

Therefore, the direct sequence spread spectrum signal acquisition process based on the intelligent doppler search provided by the invention can be summarized as follows:

(1) initial stage

The initial stage of the system is state S1, and a doppler search is performed using the first fuzzy controller FL _ 1. Let Doppler frequency search range be [ -f_max,f_max]Setting the initial value of Doppler frequency search to-f_maxAnd obtaining detection variables through signal capturing processes of frequency mixing, pseudo code correlation, energy accumulation and the like, and storing the detection variables. Setting the second frequency point of the Doppler search to be-f_max+1/2T_cohAnd the complex signal capturing process obtains a second detection variable for storage. Setting an initial value of a first fuzzy controller FL _1 according to the two stored detection variables, starting the first fuzzy controller FL _1 to search Doppler frequency, and adaptively adjusting Doppler search stepping to obtain a new search frequency pointThen, continuing to execute the signal capturing process, and updating the value of the detection variable in the buffer until the output search step is smaller than the threshold gamma₁The system proceeds to state S2.

(2) Transition phase

The transition phase system is in state S2. The system traverses the frequency points of the real Doppler adjacent area, and then selects the detection variables corresponding to the three frequency points as the initial values of the second fuzzy controller FL _ 2. Stepping the Doppler search to be less than a set threshold gamma₁The frequency point corresponding to the time is marked as the frequency point f_MGo through frequency point f_MSelecting three frequency points from nearby frequency points, and then obtaining the initialization parameters of the second fuzzy controller FL _2 in the judgment stage corresponding to the three frequency pointsAndin the transition stage, the initial value of the second fuzzy controller FL _2 is set through traversing and point selection, so that the problem of sidelobe mis-capturing can be effectively avoided, and the problem that the searching stepping direction deviates from the actual frequency point due to the fact that the signal capturing starting time is earlier than the signal arrival time can be solved.

(3) Decision phase

The decision phase system is in state S3 and a doppler search is performed using the second fuzzy controller FL _ 2. The input variables of the second controller FL _2 are set using the initialization parameters obtained in the transition phase. Output variable Deltau of the second fuzzy controller FL _2_tAs a step for the next Doppler search, i.e.Wherein f is_MIs composed ofAnd (4) corresponding frequency points. By usingTo replace the last iterationAndone of the corresponding three frequency points; every time the fuzzy control Doppler search is carried out, the search result is updated according to the new search resultAndthe value of (c). Doppler search step less than threshold gamma₂And when the system stops capturing, outputting the estimated Doppler frequency value and the pseudo code phase value, namely completing capturing of the direct sequence spread spectrum signal.

The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it will be understood by those skilled in the art that various changes and modifications may be made herein without departing from the spirit and scope of the invention as defined in the appended claims.