Pairing method and device for non-fixed addition-selection pseudo code

Abstract

The application provides a pairing method and device for non-fixed additional pseudo codes, wherein the method comprises the following steps: setting pairing indexes, the first index being the aligned cross-correlation value absolute value, and the second index being the absolute value of the difference between the leading and lagging correlation values; determining a target function and a cost matrix based on the first index and the second index; establishing an additional pseudo code allocation model based on the cost matrix and an allocation matrix; dividing the additional pseudo codes into two parts, determining an initial population based on the division result, and solving the model by using a JVC algorithm to determine the initial pairing scheme corresponding to the individuals in the initial population; performing iterative updating on the population based on a meta-heuristic algorithm or an improved meta-heuristic algorithm, and performing iterative updating on the pairing scheme corresponding to the individuals based on the JVC algorithm until a preset maximum number of iterations is reached, and determining the optimal pairing scheme obtained in the last iteration process as the optimal pairing scheme of the non-fixed additional pseudo codes; wherein the optimal pairing scheme obtained in the iteration process is determined based on the target function.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation technology, and more specifically, relates to a pairing method and apparatus for non-fixed additional pseudocodes. Background Technology

[0002] In a Global Navigation Satellite System (GNSS), pseudocode is a crucial component, providing the receiver with the basis for distinguishing between different satellites. The design of the pseudocode directly affects the degree of mutual interference between multiple access signals within the system, thus impacting the performance of the navigation system.

[0003] After selecting new pseudocodes from the existing code family, the performance of the pseudocodes themselves is determined. Therefore, choosing a suitable pairing scheme for the added pseudocodes is crucial. In a related technique, when optimizing the pseudocode family itself, a greedy algorithm is used for pseudocode selection. Similarly, related ideas can be used to perform local optimal pairing of pseudocodes to approximate the optimal solution. However, the greedy algorithm can lead to a significant local optimum phenomenon.

[0004] Another related technique addresses the impact of cross-correlation between pilot codes and data codes on the distortion of the discriminator function (i.e., the S-curve). It uses the minimum average S-curve deviation when tracking only pilot components at a specific correlator interval as the optimization objective for pseudo-code pairing, employing the Hungarian algorithm for this pairing. However, this scheme only considers signal tracking requirements in its pairing criteria, neglecting data demodulation needs. Furthermore, the Hungarian algorithm is only applicable when the pilot and data codes in the additional pseudo-code selection are fixed, limiting the available options and preventing the pairing pseudo-code from achieving optimal performance.

[0005] Therefore, in cases where the pilot code and data code in the supplementary pseudo-code are not fixed, it is urgent to find a solution for obtaining a high-performance pseudo-code pairing scheme. Summary of the Invention

[0006] To address the aforementioned deficiencies in existing technologies, this invention provides a method and apparatus for pairing non-fixed pseudocodes to achieve pairing of non-fixed pseudocodes.

[0007] In a first aspect, the present invention provides a pairing method for non-fixed incremental pseudocodes, comprising:

[0008] The pairing index for the satellite signal additional pseudocode includes a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead and lag correlation values ​​between the received signal pilot code and the received signal data code.

[0009] The objective function and cost matrix are determined based on the first and second indicators;

[0010] Based on the cost matrix and the allocation matrix of the supplementary pseudocode, an allocation model for the supplementary pseudocode is established.

[0011] The additional pseudocode is divided into two parts. The initial population is determined based on the division result. The JVC algorithm is used to solve the additional pseudocode allocation model corresponding to different division results to determine the initial pairing scheme for individuals in the initial population.

[0012] Based on a metaheuristic algorithm or an improved metaheuristic algorithm, the population is iteratively updated, and the pairing schemes corresponding to individuals in the population are iteratively updated based on the JVC algorithm until the preset maximum number of iterations is reached. The optimal pairing scheme obtained in the last iteration is determined as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iteration process is determined based on the objective function.

[0013] In some embodiments, the population is updated based on an improved metaheuristic algorithm, and the pairing schemes corresponding to individuals in the population are iteratively updated based on the JVC algorithm, including:

[0014] In the current iteration, K-means clustering is performed on the initial pairing scheme or the pairing scheme obtained in the previous iteration.

[0015] The clustered individuals are updated based on a preset update method, and the pairing scheme after clustering is updated based on the JVC algorithm.

[0016] The updated pairing scheme that minimizes the objective function is determined as the optimal pairing scheme obtained in the current iteration process.

[0017] In some embodiments, updating a subset of individuals in the clustered population based on a preset update method, and updating the pairing scheme after clustering based on the JVC algorithm, includes:

[0018] Randomly select target cluster centers, normalize the additional pseudocode indexes of the pairing schemes corresponding to the target cluster centers to the number of additional pseudocodes M, and update the normalized sequence using Cauchy mutation. Sort 1 to M according to the relative size of the values ​​in the updated sequence, and then use the JVC algorithm to generate the updated pairing schemes.

[0019] In some embodiments, updating a subset of individuals in the clustered population based on a preset update method, and updating the pairing scheme after clustering based on the JVC algorithm, includes:

[0020] Candidate pairing schemes are determined based on at least one of the following update methods;

[0021] Randomly select a target cluster individual or target cluster center, and exchange Num elements from the first N elements of the pairing scheme corresponding to the target cluster individual or target cluster center with the corresponding pairing elements from the last N elements. After the exchange, use the JVC algorithm to generate candidate pairing schemes; wherein, the number of exchanged element groups is Num = randi(1, floor(N / 2)).

[0022] Randomly select two cluster centers or other cluster individuals corresponding to two pairing schemes. Each pairing scheme has N sets of pseudocodes. Swap the code indexes corresponding to the pseudocodes with the lowest code value in the two pairing schemes. After the swap, use the JVC algorithm to generate two update schemes. Select the update scheme with the lower objective function in the two update schemes as the candidate pairing scheme.

[0023] Select the optimal pairing scheme obtained in the previous iteration, randomly select a set of pseudocode groups to exchange code indices, and then use the JVC algorithm to generate candidate pairing schemes after the exchange.

[0024] Select the optimal pairing scheme obtained in the previous iteration, and select one or more sets of pseudocode exchange code indices with the lowest replacement value. After the exchange, use the JVC algorithm to generate candidate pairing schemes.

[0025] When the objective function of the candidate pairing scheme is less than the objective function of the original pairing scheme, the candidate pairing scheme replaces the original pairing scheme to obtain the updated pairing scheme.

[0026] In some embodiments, the population is iteratively updated based on a metaheuristic algorithm, and the pairing schemes corresponding to individuals in the population are iteratively updated based on the JVC algorithm, including:

[0027] The population individuals are evolved using different update methods in the metaheuristic algorithm. In the current iteration, the additional pseudocode numbers 1 to M are sorted based on the relative size of the numerical values ​​contained in the individuals, and the pairing scheme corresponding to the individuals is determined based on the JVC algorithm.

[0028] In some embodiments, dividing the additional pseudocode into two parts includes:

[0029] After dividing the additional pseudocode into two parts, it is initialized using a Tent chaotic sequence.

[0030] In some embodiments, the method further includes:

[0031] If the objective function of the optimal pairing scheme obtained through NumG consecutive iterations does not decrease, then N is randomly selected from the optimal pairing scheme obtained in the current iteration. i Each pairing scheme is re-initialized;

[0032] Where, N i satisfy:

[0033]

[0034] Where MAXG represents the maximum number of iterations, N represents the number of satellites, which is half the number of the additional pseudocodes, and k represents the current number of iterations.

[0035] In some embodiments, the objective function is determined based on the first indicator and the second indicator, satisfying the following calculation formula:

[0036]

[0037] Among them, CCF0 j ΔR represents the first index of the j-th pair of pseudocodes. j The second index represents the pair of pseudocodes in the j-th pair, and α and β represent custom weighting coefficients.

[0038] In some embodiments, determining the cost matrix based on the first metric and the second metric includes:

[0039] A preliminary cost matrix is ​​determined based on the first and second indicators. The preliminary cost matrix contains the cost function values ​​of all paired additional pseudocodes.

[0040] The cost matrix is ​​determined based on the preliminary cost matrix;

[0041] The pre-cost matrix satisfies:

[0042]

[0043] Among them, CCF0 mn The first index, ΔR, represents the pairing of pseudocode m and pseudocode n. mn The second index represents the pairing of pseudocode m and pseudocode n, where α and β represent custom weighting coefficients.

[0044] Secondly, the present invention also provides a pairing device with a non-fixed incremental pseudocode, comprising:

[0045] The index setting unit is used to set the pairing index of the satellite signal additional pseudocode, including a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead and lag correlation values ​​between the received signal pilot code and the received signal data code.

[0046] A parameter determination unit is used to determine the objective function and cost matrix based on the first indicator and the second indicator;

[0047] The model building unit is used to build an additional pseudocode allocation model based on the cost matrix and the allocation matrix of the additional pseudocode.

[0048] An initialization unit is used to divide the additional pseudocode into two parts, determine the initial population based on the division result, and use the JVC algorithm to solve the allocation model of the additional pseudocode corresponding to different division results to determine the initial pairing scheme corresponding to the individuals in the initial population.

[0049] An iterative unit is used to iteratively update the population based on a metaheuristic algorithm or an improved metaheuristic algorithm, and to iteratively update the pairing schemes corresponding to individuals in the population based on the JVC algorithm, until a preset maximum number of iterations is reached. The optimal pairing scheme obtained in the last iteration process is determined as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iterative process is determined based on the objective function.

[0050] Thirdly, the present invention provides an electronic device comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0052] Fifthly, the present invention provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0053] The present invention provides a pairing method and apparatus for non-fixed additional pseudocodes. For pilot codes and data codes in non-fixed additional pseudocodes, considering the cross-correlation between pilot codes and data codes to meet data demodulation requirements, a first index for additional pseudocode pairing is set. Considering the impact of data codes on pilot codes during tracking, a second index for additional pseudocode pairing is set to meet signal tracking requirements. Simultaneously considering the first and second indices, a cost matrix and objective function are determined, and an additional pseudocode allocation model is established using the allocation matrix and cost matrix. The additional pseudocodes are divided into two equal parts, and an initial population is determined based on the equal division result. The JVC algorithm is used to solve the additional pseudocode allocation model corresponding to different equal division results to obtain the initial pairing scheme for individuals in the initial population. Then, a metaheuristic algorithm or an improved metaheuristic algorithm, combined with the JVC algorithm, is used to continuously update the pairing scheme, using the objective function as the update basis, ultimately obtaining the optimal pairing scheme with the minimum objective function, thus achieving optimal pairing of non-fixed additional pseudocodes. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is one of the flowcharts illustrating the pairing method for non-fixed incremental pseudocodes provided in this embodiment of the invention;

[0056] Figure 2 This is a second schematic flowchart of the pairing method for non-fixed incremental pseudocodes provided in the embodiments of the present invention;

[0057] Figure 3 This is the third flowchart illustrating the pairing method for non-fixed incremental pseudocodes provided in this embodiment of the invention.

[0058] Figure 4 This is a comparative diagram of the CCF0 performance curves of the pairing results of three different algorithms provided in the embodiments of the present invention.

[0059] Figure 5 This is a comparative schematic diagram of the ΔR performance curves of the pairing results of three different algorithms provided in the embodiments of the present invention;

[0060] Figure 6 This is a schematic diagram of the CCF0 performance as a function of the weighting coefficient α, provided in an embodiment of the present invention.

[0061] Figure 7This is a schematic diagram of the curve of ΔR performance as a function of the weighting coefficient α provided in the embodiments of the present invention;

[0062] Figure 8 This is a schematic diagram of the relationship curve between SumCCF0 and R provided in an embodiment of the present invention;

[0063] Figure 9 This is a schematic diagram of the structure of the pairing device for non-fixed incremental pseudocode provided in an embodiment of the present invention;

[0064] Figure 10 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0066] The terms "first" and "second," etc., used in this article are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0067] The fixed additional pseudocode pairing method and apparatus provided in this invention are applicable to the BeiDou system, such as the pairing of additional pseudocodes for the BeiDou B1C signal master code.

[0068] Figure 1 This is one of the flowcharts illustrating the pairing method for non-fixed incremental pseudocodes provided in this embodiment of the invention, such as... Figure 1 As shown, the method includes at least the following steps:

[0069] S101. The pairing index of the satellite signal additional pseudocode includes a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead-lag correlation value and the lead-lag correlation value between the received signal pilot code and the received signal data code.

[0070] Specifically, in the process of adding pseudo-code pairing, it is necessary to set pairing indicators for the added pseudo-codes. On the one hand, considering the data demodulation requirements and the cross-correlation between the pilot code and the data code, the first indicator is set as the absolute value of the alignment cross-correlation between the received signal pilot code and the local data code, CCF0.

[0071]

[0072] Where, p i d iThese represent the paired pilot code and data code, respectively, and L represents the code length of a single additional pseudocode.

[0073] On the other hand, considering the signal tracking requirements and taking into account the impact of the data code on the pilot code during tracking, a second indicator is set as the absolute value of the difference between the lead-lag correlation value and the received signal pilot code and the received signal data code, ΔR = |R dp [1]-R dp [-1]|.

[0074] S102. Determine the objective function and cost matrix based on the first and second indicators.

[0075] Specifically, the addition of M pseudocodes can be described by the following linear allocation model:

[0076]

[0077]

[0078] Where S represents the allocation matrix, and N = M / 2 represents the number of satellites.

[0079] The expression for the allocation matrix is ​​as follows:

[0080]

[0081]

[0082] The column indices of this allocation matrix represent satellite indices, and the row indices represent incremental pseudocode indices; if s ij =1, which means that the j-th satellite contains pseudocode i, and pseudocode i will be paired with another pseudocode of the satellite. j (m,n) represents the cost required to pair pseudocode m with pseudocode n in the j-th satellite, C M×M ={c mn} M×M The cost matrix is ​​expressed as follows:

[0083]

[0084] Among them, c mn =c nm .

[0085] Therefore, after determining the pairing index of the additional pseudocode, it is necessary to set an appropriate cost matrix, objective function, and pairing scheme update method. During the iteration process, the pairing scheme needs to be updated according to the size of the objective function.

[0086] Regarding the setting of the objective function: First, considering the primary metric, the aligned cross-correlation value of each pairwise correlation of the pseudocode has a minimum value (e.g., approximately -74.1769 dB in B1C). Since the magnitude of the cross-correlation value has a direct impact on system performance, the initial objective function O can be set as follows:

[0087]

[0088] Among them, CCF0 j The first index represents the absolute value of the alignment cross-correlation of the j-th group of pilot data codes (paired pseudocodes).

[0089] Furthermore, in the pilot data code tracking stage, a second metric needs to be considered. Therefore, the objective function O can be rewritten as follows:

[0090]

[0091] Among them, CCF0 j Let ΔR represent the absolute value of the alignment cross-correlation of the j-th pair of pseudocodes. j Let α represent the absolute value of the difference between the lead and lag correlation values ​​of the j-th pair of pseudocodes, and let α and β represent custom weighting coefficients.

[0092] Similarly, the preliminary cost matrix is ​​set as follows:

[0093]

[0094] This preliminary cost matrix contains the cost function values ​​for all pairwise pairings of pseudocodes. After each equal division of the pseudocodes, this matrix is ​​used to calculate the cost matrix C required for pairing. N×N .

[0095] S103. Based on the cost matrix and the allocation matrix of the additional pseudocode, establish an allocation model for the additional pseudocode.

[0096] Specifically, after determining the cost matrix of the additional pseudocode, the formula of the cost matrix is ​​substituted into the aforementioned linear allocation model, and combined with the allocation matrix of the additional pseudocode, an allocation model for the additional pseudocode is established.

[0097] S104. Divide the additional pseudocode into two parts, determine the initial population based on the division result, and use the JVC algorithm to solve the additional pseudocode allocation model corresponding to different division results to determine the initial pairing scheme for individuals in the initial population.

[0098] S105. Based on a metaheuristic algorithm or an improved metaheuristic algorithm, iteratively update the population and iteratively update the pairing schemes corresponding to individuals in the population based on the JVC algorithm until the preset maximum number of iterations is reached. Determine the optimal pairing scheme obtained in the last iteration as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iteration process is determined based on the objective function.

[0099] Specifically, in the pseudocode pairing process, the cost matrix and the actual allocation matrix are coupled and difficult to decouple, making them undeterminable. Therefore, the M additional pseudocodes can be divided into two parts, and the aforementioned linear programming problem can be reduced to a linear programming problem.

[0100] The M additional pseudocodes have a common... There are several ways to divide the material equally, and different ways of dividing the material equally will lead to different optimal pairing schemes. Therefore, it is necessary to find the optimal pairing scheme and the best way of dividing the material equally.

[0101] The Jonker-Volgenant-Castanon (JVC) algorithm is a linear programming method based on the shortest augmenting path. It solves perfect matching problems by performing dual primal optimization, iteratively increasing the number of objects to be assigned by finding a series of minimum-cost augmenting paths to obtain the globally optimal solution. Compared to other optimal matching algorithms, the JVC algorithm achieves higher efficiency while obtaining the optimal solution. Therefore, this invention uses the JVC algorithm to solve for the optimal pairing scheme under different equal distribution methods, completing the initialization of the pairing scheme.

[0102] The implementation process of the JVC algorithm is as follows:

[0103] initialization;

[0104] Find the shortest augmenting path;

[0105] Update the dual variable;

[0106] The previous pairing results are expanded based on the shortest augmenting path found until all columns in the cost matrix have been searched, and the optimal pairing scheme is output.

[0107] Furthermore, it is necessary to clarify the update method for the pairing scheme. Metaheuristic algorithms are general heuristic strategies that do not rely on the specific conditions of the problem, but rather impose certain requirements during the search process. They can perform a global search to find an approximate solution to the optimal solution and avoid getting trapped in local optima.

[0108] This invention considers applying metaheuristic algorithms to the pairing of non-fixed addition pseudocodes: Based on the even distribution of the addition pseudocodes, an initial population is obtained. The JVC algorithm is used to solve the addition pseudocode allocation model corresponding to each individual in the initial population, yielding the initial pairing scheme. Then, various metaheuristic algorithms are used to iteratively update the population, and during the iteration process, the JVC algorithm is also used to iteratively update the pairing schemes of individuals until a pre-set maximum number of iterations is reached. The optimal pairing scheme obtained in the last iteration is taken as the optimal pairing scheme for the non-fixed addition pseudocode.

[0109] In each iteration, the optimal pairing scheme is selected based on the objective function, and the pairing scheme with the smallest objective function value is selected as the optimal pairing scheme obtained in the current iteration.

[0110] Optionally, the metaheuristic algorithm can be a traditional metaheuristic algorithm, such as simulated annealing, tabu search, genetic algorithm, evolutionary strategy, differential evolution, ant colony optimization, particle swarm optimization, etc.; or it can be a recently published balance optimization algorithm, flow optimization algorithm, snake optimization algorithm, star-raven optimization algorithm, etc. In the process of evolving the population using different update methods in the metaheuristic algorithm, for each iteration, the additional pseudocode indices 1 to M are sorted based on the relative size of the numerical values ​​contained in the population individuals, and paired using the JVC algorithm to obtain the pairing scheme represented by the current individual.

[0111] Furthermore, considering that most metaheuristic algorithms search based on information such as position and direction, the search lacks specificity. Therefore, this invention considers a targeted search for the pairing of non-fixed addition pseudocodes, utilizing an improved metaheuristic algorithm to perform targeted iterative updates to the initial population, while employing the JVC algorithm to accelerate the iteration speed of the pairing scheme.

[0112] The pairing method for non-fixed additional pseudocodes provided in this invention addresses the case of pilot codes and data codes in non-fixed additional pseudocodes. For data demodulation requirements, considering the cross-correlation between pilot codes and data codes, a first index for additional pseudocode pairing is set. For signal tracking requirements, taking into account the impact of data codes on pilot codes during tracking, a second index for additional pseudocode pairing is set. Simultaneously considering the first and second indices, a cost matrix and objective function are determined. An additional pseudocode allocation model is established using the allocation matrix and cost matrix. The additional pseudocodes are evenly divided into two parts. Based on the even division result, an initial population is determined, and the JVC algorithm is used to solve the additional pseudocode allocation model corresponding to different even division results to obtain the initial pairing scheme for individuals in the initial population. Then, using a metaheuristic algorithm or an improved metaheuristic algorithm combined with the JVC algorithm, the pairing scheme is continuously updated, with the objective function as the update basis. Finally, the optimal pairing scheme with the minimum objective function is obtained, achieving optimal pairing of non-fixed additional pseudocodes.

[0113] In some embodiments, in S104, the additional pseudocode is divided into two parts, including:

[0114] After dividing the additional pseudocode into two parts, it is initialized using the Tent chaotic sequence.

[0115] Specifically, before the iteration begins, embodiments of the present invention may also use a Tent chaotic sequence to initialize the pairing scheme.

[0116] The initialization methods for metaheuristic algorithms vary. Chaos, as a ubiquitous nonlinear phenomenon in nature, possesses characteristics of randomness, ergodicity, and regularity. It not only effectively maintains population diversity but also helps the algorithm escape local optima, improving global search capabilities. The Tent chaos mapping expression is as follows:

[0117]

[0118] Initializing x0 = rand() and a = 0.7 yields a set of Tent chaotic sequences. By sorting the values ​​1 to M according to their relative magnitudes within the Tent chaotic sequence and then pairing them using the JVC algorithm, a set of initialization pseudocode pairing schemes can be obtained.

[0119] The improved metaheuristic algorithm designed in this invention is described below:

[0120] In some embodiments, S105 iteratively updates the population based on an improved metaheuristic algorithm and iteratively updates the pairing schemes corresponding to individuals in the population based on the JVC algorithm, including:

[0121] In the current iteration, K-means clustering is performed on the initial pairing scheme or the pairing scheme obtained in the previous iteration.

[0122] The clustered individuals are updated based on a preset update method, and the pairing scheme after clustering is updated based on the JVC algorithm.

[0123] The updated pairing scheme that minimizes the objective function is determined as the optimal pairing scheme obtained in the current iteration process.

[0124] Specifically, in one iteration, K-means clustering is performed on the pairing schemes obtained in the previous iteration. If it is the first iteration, K-means clustering is performed on the initial pairing schemes obtained during initialization.

[0125] K-means clustering is a distance-based clustering method that divides data into K distinct categories, minimizing the difference in objective function values ​​among individuals within each category while maximizing the distance between categories. By comparing the objective function values ​​of different clustering schemes, it identifies which schemes perform better within the same category, providing a basis for optimization and adjustment, and making the search for the globally optimal pairing more targeted.

[0126] While introducing clustering, a preset pairing scheme is used to update some individuals in the clustered population. The objective function values ​​of different clustering schemes are compared to continuously update the individuals. At the same time, the JVC algorithm is used to update the pairing scheme of the updated individuals, thereby obtaining an approximate optimal solution to the original pairing problem.

[0127] During the iteration process, the objective function is used as the basis for updating the pairing scheme and selecting the optimal pairing scheme.

[0128] The targeted update method in the improved metaheuristic algorithm will be further introduced below.

[0129] In some embodiments, the preset update method includes:

[0130] Update Method 1: Randomly select target cluster centers, normalize the additional pseudocode indices of the pairing schemes corresponding to the target cluster centers to the number of additional pseudocodes M, and update the normalized sequence using Cauchy mutation. Sort the values ​​from 1 to M according to their relative magnitudes in the updated sequence. After sorting, use the JVC algorithm to generate the updated pairing schemes. The Cauchy mutation formula is as follows:

[0131] z n =x n ×(1+tan(π×(rand()-0.5)))

[0132] Where, x n This represents the normalized sequence of the added pseudocode index.

[0133] In some embodiments, the preset update method includes:

[0134] Update method 1: Randomly select the target cluster individual, and swap the corresponding pairing elements in the first N elements of the pairing scheme of the target cluster individual with the corresponding pairing elements in the last N elements. After the swap, use the JVC algorithm to generate candidate pairing schemes; where the number of swapped element groups is Num = randi(1, floor(N / 2)).

[0135] Update method 2: Randomly select the target cluster center, and swap the corresponding Num elements in the first N elements of the pairing scheme corresponding to the target cluster center with the corresponding pairing elements in the last N elements. After the swap, use the JVC algorithm to generate candidate pairing schemes; where the number of swapped element groups is Num = randi(1, floor(N / 2)).

[0136] Update Method 3: Randomly select two pairing schemes corresponding to the cluster centers of two clusters or individuals in other clusters. Each pairing scheme has N sets of pseudocodes. Swap the code indices corresponding to the pseudocode sets with the lowest code value in the two selected pairing schemes. After the swap, use the JVC algorithm to generate two update schemes. Select the update scheme with the lower objective function as the candidate pairing scheme; or,

[0137] Update Method 4: Select the optimal pairing scheme with the minimum objective function obtained in the previous iteration, randomly select a set of pseudocode groups to exchange code indices, and then use the JVC algorithm to generate candidate pairing schemes; or,

[0138] Update method 5: Select the optimal pairing scheme with the smallest objective function obtained in the previous iteration, select one or more sets of pseudocode exchange code indices with the lowest replacement value, and use the JVC algorithm to generate candidate pairing schemes after the exchange.

[0139] For update methods 1-5 above, the candidate pairing scheme is used to replace the original pairing scheme only when the objective function value of the candidate pairing scheme is less than that of the original pairing scheme, thus realizing the iterative update of the pairing scheme.

[0140] The pairing method for non-fixed additional pseudocodes provided in this invention addresses the case of pilot codes and data codes in non-fixed additional pseudocodes. For data demodulation requirements, considering the cross-correlation between pilot codes and data codes, a first index for additional pseudocode pairing is set. For signal tracking requirements, taking into account the impact of data codes on pilot codes during tracking, a second index for additional pseudocode pairing is set. Simultaneously considering the first and second indices, a cost matrix and objective function are determined, and an additional pseudocode allocation model is established using the allocation matrix and cost matrix. The additional pseudocode is divided into two equal parts, and the JVC algorithm is used to solve the allocation model corresponding to different equal division results, obtaining initial pairing schemes for different initial equal division results. Then, combining an improved metaheuristic algorithm and the JVC algorithm, the pairing scheme is continuously updated, using the objective function as the update basis, ultimately obtaining the optimal pairing scheme with the minimum objective function, thus achieving optimal pairing of non-fixed additional pseudocodes.

[0141] Compared to most metaheuristic algorithms that search based on information such as position and direction, the improved metaheuristic algorithm of this invention uses K-means clustering to cluster pairing schemes, designs a targeted update method to update the individuals in the clustered population, performs targeted search for pseudocode optimization requirements, and uses the JVC algorithm to accelerate the iteration speed.

[0142] In some embodiments, the pairing method for non-fixed incremental pseudocodes further includes:

[0143] When the objective function of the optimal pairing scheme obtained in NumG consecutive iterations does not decrease, N is randomly selected from the optimal pairing scheme obtained in the current iteration. i Each pairing scheme is re-initialized;

[0144] Where, N i satisfy:

[0145]

[0146] Where MAXG represents the maximum number of iterations, N represents the number of satellites, which is half the number of additional pseudocodes, and k represents the current iteration number.

[0147] Specifically, during the iterative process of the pairing scheme, if the objective function of the optimal pairing scheme obtained after NumG consecutive iterations does not decrease, meaning the iteration process cannot obtain a better pairing scheme after NumG iterations, then in addition to the optimal pairing scheme obtained in the current iteration, N other pairs of pairs are randomly selected. i Each pairing scheme is reinitialized, which can be achieved through a Tent chaotic mapping. After reinitialization, the iteration process resumes, continuously updating the pairing scheme until the maximum number of iterations is reached.

[0148] The technical solutions provided by the embodiments of the present invention will be further illustrated below through specific examples.

[0149] Figure 2 This is a second schematic flowchart of the pairing method for non-fixed incremental pseudocodes provided in this embodiment of the invention, as shown below. Figure 2 As shown, during the pseudocode pairing process, the cost matrix and the actual allocation matrix are coupled and difficult to decouple, making them undeterminable. Therefore, the M additional pseudocodes can be divided into two parts. At this point, the original pairing problem can degenerate into a linear programming problem. Under this condition, the JVC method is used to pair the pseudocodes to obtain the optimal solution under the current linear programming problem.

[0150] The M additional pseudocodes have a common... There are several ways to divide the data equally, and different ways of dividing the data equally will lead to different optimal pairing schemes. Therefore, it is necessary to find the best way of dividing the data equally among them to achieve the optimal pairing result.

[0151] During initialization, various permutations are randomly generated and clustered to uncover multiple potential domains. This paper employs K-means clustering to classify different schemes. Simultaneously with clustering, multiple pairing scheme update strategies are used, continuously updating the algorithm by comparing the objective function values ​​of schemes across different clusters; thus, an approximate optimal solution to the original pairing problem is obtained. This method is referred to as the HCPO algorithm in this paper.

[0152] The final approximate optimal solution is S. * It is set in the following form during iteration: S * = {1-M permutation values}. The pairing scheme is S. * The pseudocode sequences corresponding to the first N values ​​are paired one-to-one with the pseudocode sequences corresponding to the last N values.

[0153] Figure 3 This is the third flowchart illustrating the pairing method for non-fixed incremental pseudocodes provided in this embodiment of the invention, as shown below. Figure 3 As shown, firstly, it is necessary to set an appropriate cost matrix, objective function, and matching scheme update method. During the iteration process, it is sometimes necessary to update the scheme based on the size of the objective function.

[0154] The objective function is as follows:

[0155]

[0156] The preparation cost matrix is:

[0157]

[0158] This preliminary cost matrix contains the cost function values ​​for all pairwise pairings of pseudocodes. After each equal division of the pseudocodes, this matrix is ​​used to calculate the cost matrix C required for pairing. N×N .

[0159] The updated pairing scheme specifically includes the following two parts:

[0160] The first part uses the Tent chaotic sequence to initialize the pairing scheme. The values ​​1 to M are sorted according to their relative size in the Tent sequence, and then paired using the JVC algorithm to obtain a set of initialization pseudocode pairing schemes.

[0161] The second part involves the formal iteration after initialization. Each iteration begins with K-means clustering of the individuals from the previous iteration. For example, in the first iteration, K-means clustering is performed on the initialization scheme. Then, an arbitrary update method based on a metaheuristic algorithm, combined with the JVC algorithm, is used to update the pairing schemes. After one iteration, the scheme with the minimum objective function value is considered the optimal scheme at that time. If the objective function value of the optimal scheme does not decrease after multiple iterations, a portion of the schemes is randomly initialized.

[0162] Figure 4 This is a comparative diagram of the CCF0 performance curves of the pairing results of three different algorithms provided in this embodiment of the invention. Figure 5 This is a comparative diagram of the ΔR performance curves of the pairing results of three different algorithms provided in this embodiment of the invention, as shown in the figure. Figure 4 and Figure 5 As shown, taking the BeiDou B1C signal as an example, the 118 newly selected B1C master codes are paired, considering the following pairing criteria:

[0163] ①CCF0=|R pd (0)|

[0164] ②ΔR=|R dp [1]-R dp [-1]|

[0165] After selecting the pseudocodes to be paired, the pairing of the pseudocodes under different cost matrices is completed using the fixed pseudocode pairing method based on the JVC algorithm, the pseudocode pairing method based on the greedy algorithm, and the non-fixed pseudocode pairing method based on the HCPO algorithm.

[0166] Specifically, let α = 0.5, β = 0.5, and the maximum number of iterations for the HCPO algorithm is 200. Let... Table 1 shows a comparison of the SumCCF0 (dB) values ​​and the maximum CCF0 value for the pairing results of the three algorithms, and Table 2 shows the corresponding R (dB) and maximum ΔR value.

[0167] Table 1: Comparison of CCF0 Performance

[0168] Pairing method SumCCF0(dB) Maximum CCF0 value (dB) Greedy Algorithm -21.1708 -36.9105 JVC algorithm -21.6091 -46.9424 HCPO algorithm -24.1557 -50.6551

[0169] Table 2: ΔR Performance Comparison Table

[0170] Pairing method R(dB) Maximum ΔR value Greedy Algorithm -18.8487 0.01447 JVC algorithm -15.5681 0.01173 HCPO algorithm -20.2323 0.007038

[0171] comprehensive Figure 4 and Figure 5 From Tables 1 and 2, we can draw the following conclusions:

[0172] ① Overall performance and stability of CCF0: HCPO algorithm yields the best pairing results; JVC algorithm yields the second best pairing results; greedy algorithm yields the worst pairing results.

[0173] ② The number of pilot code / data code groups obtained from the minimum CCF0 value: HCPO algorithm has the most pairing results; greedy algorithm is the second; JVC algorithm has the fewest.

[0174] ③ Maximum CCF0 performance: HCPO algorithm has the best pairing results; JVC algorithm is second best; greedy algorithm has the worst performance.

[0175] ④Overall performance and stability of ΔR: The HCPO algorithm yields the best pairing results; the greedy algorithm is second best; and the JVC algorithm performs the worst.

[0176] ⑤ Worst-case ΔR performance: HCPO algorithm has the best pairing result; greedy algorithm is second best; JVC algorithm is the worst.

[0177] It can be seen that the HCPO algorithm provided in this embodiment of the invention can produce pairing results with excellent CCF0 and ΔR performance.

[0178] Furthermore, depending on system requirements, different ΔR performance gains can be obtained by changing the custom weighting coefficients α and β. Figure 6 This is a schematic diagram of the CCF0 performance as a function of the weighting coefficient α, provided in an embodiment of the present invention. Figure 7 This is a schematic diagram of the curve of ΔR performance as a function of the weighting coefficient α provided in the embodiments of the present invention, as shown below. Figure 6 and Figure 7 As shown, the weight coefficient α is set to 0.01 to 0.99 (step size 0.01), the number of iterations for all algorithms is set to 200, and the population size is 100. In comparison, the HCPO algorithm has the best overall CCF0 performance, the smallest maximum CCF0 value, and the best overall ΔR performance in most cases. Figure 8 This is a schematic diagram of the relationship curve between SumCCF0 and R provided in an embodiment of the present invention, as shown below. Figure 8 As shown, after comprehensively evaluating the three algorithms, the HCPO algorithm has the best performance.

[0179] It is worth noting that the optimization curve of the HCPO algorithm can be considered an approximation of the Pareto front curve. All solutions on this curve are Pareto optimal, and each Pareto solution has its own preference for the two indices mentioned. Therefore, a suitable pairing scheme can be selected as needed.

[0180] Figure 9 This is a schematic diagram of the pairing device for non-fixed incremental pseudocode provided in an embodiment of the present invention, as shown below. Figure 9 As shown, the device includes at least:

[0181] The index setting unit 901 is used to set the pairing index of the satellite signal additional pseudocode, including a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead and lag correlation values ​​between the received signal pilot code and the received signal data code.

[0182] The parameter determination unit 902 is used to determine the objective function and cost matrix based on the first and second indicators;

[0183] Model building unit 903 is used to build an additional pseudocode allocation model based on the cost matrix and the allocation matrix of the additional pseudocode.

[0184] The initialization unit 904 is used to divide the additional pseudocode into two parts, determine the initial population based on the division result, and use the JVC algorithm to solve the additional pseudocode allocation model corresponding to different division results to determine the initial pairing scheme corresponding to the individuals in the initial population.

[0185] The iteration unit 905 is used to iteratively update the population based on a metaheuristic algorithm or an improved metaheuristic algorithm, and iteratively update the pairing schemes corresponding to individuals in the population based on the JVC algorithm, until the preset maximum number of iterations is reached, and to determine the optimal pairing scheme obtained in the last iteration process as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iteration process is determined based on the objective function.

[0186] In some embodiments, the iteration unit 905 is specifically used for:

[0187] In the current iteration, K-means clustering is performed on the initial pairing scheme or the pairing scheme obtained in the previous iteration.

[0188] The clustered individuals are updated based on a preset update method, and the pairing scheme after clustering is updated based on the JVC algorithm.

[0189] The updated pairing scheme that minimizes the objective function is determined as the optimal pairing scheme obtained in the current iteration process.

[0190] In some embodiments, the iteration unit 905 is specifically used for:

[0191] Randomly select target cluster centers, normalize the additional pseudocode index of the pairing scheme corresponding to the target cluster centers to the number of additional pseudocodes M, and update the normalized sequence using Cauchy mutation. Sort 1 to M according to the relative size of the values ​​in the updated sequence, and use the JVC algorithm to generate the updated pairing scheme after sorting.

[0192] In some embodiments, the iteration unit 905 is specifically used for:

[0193] Candidate pairing schemes are determined based on at least one of the following update methods;

[0194] Randomly select a target cluster individual or target cluster center, and exchange the corresponding pairing elements in the first N elements of the pairing scheme corresponding to the target cluster individual or target cluster center with the corresponding pairing elements in the last N elements. After the exchange, use the JVC algorithm to generate candidate pairing schemes; where the number of exchanged elements is Num = randi(1, floor(N / 2)).

[0195] Randomly select two pairing schemes corresponding to the cluster centers of two clusters or other cluster individuals. Each pairing scheme has N sets of pseudocodes. Swap the code indices corresponding to the pseudocodes with the lowest code value in the two pairing schemes. After the swap, use the JVC algorithm to generate two update schemes. Select the update scheme with the lower objective function as the candidate pairing scheme.

[0196] Select the optimal pairing scheme obtained in the previous iteration, randomly select a set of pseudocode groups to exchange code indices, and then use the JVC algorithm to generate candidate pairing schemes after the exchange.

[0197] Select the optimal pairing scheme obtained in the previous iteration, and select one or more sets of pseudocode exchange code indices with the lowest replacement value. After the exchange, use the JVC algorithm to generate candidate pairing schemes.

[0198] When the objective function of the candidate pairing scheme is less than that of the original pairing scheme, the candidate pairing scheme is used to replace the original pairing scheme to obtain the updated pairing scheme.

[0199] In some embodiments, the iteration unit 905 is specifically used for:

[0200] The population individuals are evolved using different update methods in the metaheuristic algorithm. In the current iteration, the additional pseudocode numbers 1 to M are sorted based on the relative size of the numerical values ​​contained in the individuals, and the pairing scheme corresponding to the individuals is determined based on the JVC algorithm.

[0201] In some embodiments, the initialization unit 904 is further configured to:

[0202] After dividing the additional pseudocode into two parts, it is initialized using the Tent chaotic sequence.

[0203] In some embodiments, the device further includes:

[0204] The reinitialization unit is used to randomly select N pairs of pairs other than the optimal pairing obtained in the current iteration when the objective function of the optimal pairing obtained in NumG consecutive iterations has not decreased. i Each pairing scheme is re-initialized;

[0205] Where, N i satisfy:

[0206]

[0207] Where MAXG represents the maximum number of iterations, N represents the number of satellites, which is half the number of additional pseudocodes, and k represents the current iteration number.

[0208] In some embodiments, the objective function is determined based on the first and second indicators, satisfying the following calculation formula:

[0209]

[0210] Among them, CCF0 j Let ΔR represent the first index of the j-th pair of pseudocodes. j Let α represent the second index of the j-th pair of pseudocodes, and let α and β represent custom weighting coefficients.

[0211] In some embodiments, the parameter determining unit 902 is specifically used for:

[0212] The preliminary cost matrix is ​​determined based on the first and second indicators. The preliminary cost matrix contains the cost function values ​​of all paired additional pseudocodes.

[0213] Determine the cost matrix based on the preliminary cost matrix;

[0214] The preparatory cost matrix satisfies:

[0215]

[0216] Among them, CCF0 mn ΔR represents the first index when pseudocode m and pseudocode n are paired. mn The second index represents the pairing of pseudocode m and pseudocode n, and α and β represent custom weighting coefficients.

[0217] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description in the aforementioned method embodiments, and will not be repeated here.

[0218] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0219] Based on the methods described in the above embodiments, this invention provides an electronic device. The device may include at least one memory for storing a program and at least one processor for executing the program stored in the memory. When the program stored in the memory is executed, the processor performs the methods described in the above embodiments.

[0220] Figure 10 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 10 As shown, the electronic device may include a processor 1001, a communications interface 1020, a memory 1003, and a communication bus 1004, wherein the processor 1001, the communications interface 1002, and the memory 1003 communicate with each other via the communication bus 1004. The processor 1001 can call software instructions in the memory 1003 to execute the methods described in the above embodiments.

[0221] Based on the methods in the above embodiments, this embodiment of the invention provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0222] Based on the methods in the above embodiments, this embodiment of the invention provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0223] It is understood that the processor in the embodiments of the present invention can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0224] The method steps in these embodiments of the invention can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0225] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0226] It is understood that the various numerical designations used in the embodiments of the present invention are merely for the convenience of description and are not intended to limit the scope of the embodiments of the present invention.

[0227] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for pairing non-stationary spreading codes, characterized in that, include: The pairing index for the satellite signal additional pseudocode includes a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead and lag correlation values ​​between the received signal pilot code and the received signal data code. The objective function and cost matrix are determined based on the first and second indicators; Based on the cost matrix and the allocation matrix of the supplementary pseudocode, an allocation model for the supplementary pseudocode is established. The additional pseudocode is divided into two parts, and the division result is initialized with a Tent chaotic sequence. The initial population is determined based on the initialized division result. The JVC algorithm is used to solve the additional pseudocode allocation model corresponding to different division results to determine the initial pairing scheme for individuals in the initial population. Based on a metaheuristic algorithm or an improved metaheuristic algorithm, the population is iteratively updated, and the pairing schemes corresponding to individuals in the population are iteratively updated based on the JVC algorithm until a preset maximum number of iterations is reached. The optimal pairing scheme obtained in the last iteration is determined as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iterative process is determined based on the objective function. The objective function determined based on the first and second indicators satisfies the following calculation formula: wherein, denotes the first index of the pseudo-code of the group j denotes the second index of the pseudo-code of the group denotes the first index of the pseudo-code of the group j denotes the second index of the pseudo-code of the group α and β denotes a user-defined weight coefficient; This also includes: when the objective function of the optimal pairing scheme obtained by the consecutive NumG times of iteration processes does not decrease, randomly selecting one pairing scheme other than the optimal pairing scheme obtained by the current iteration process to re-initialize the pairing scheme wherein satisfies: wherein, MAXG represents the maximum number of iterations, N represents the number of satellites, which is half the number of the additional pseudo-codes, k represents the current number of iterations.

2. The method of claim 1, wherein the non-fixed additional selection code pair is generated by the steps of: generating a first additional selection code pair; generating a second additional selection code pair; and combining the first and second additional selection code pairs to generate the non-fixed additional selection code pair. Based on an improved metaheuristic algorithm, the population is updated, and the pairing schemes corresponding to individuals in the population are iteratively updated based on the JVC algorithm, including: In the current iteration, K-means clustering is performed on the initial pairing scheme or the pairing scheme obtained in the previous iteration. The clustered individuals are updated based on a preset update method, and the pairing scheme after clustering is updated based on the JVC algorithm. The updated pairing scheme that minimizes the objective function is determined as the optimal pairing scheme obtained in the current iteration process.

3. The method of claim 2, wherein the non-fixed additional selection code pair is generated by The process of updating a subset of individuals in the clustered population based on a preset update method, and updating the pairing scheme after clustering based on the JVC algorithm, includes: Randomly select a target cluster center, and the target cluster center corresponds to the selected pseudo code index of the selected pseudo code number M Normalization is performed, and the normalized sequence is updated using the Cauchy variation. According to the relative size of the values in the updated sequence, 1 to M Sorting is performed, and the JVC algorithm is used to generate an updated pairing scheme after sorting.

4. The pairing method for non-fixed incremental pseudocodes according to claim 2, characterized in that, The process of updating a subset of individuals in the clustered population based on a preset update method, and updating the pairing scheme after clustering based on the JVC algorithm, includes: Candidate pairing schemes are determined based on at least one of the following update methods; Randomly select a target cluster individual or a target cluster center, exchange the corresponding pairing scheme of the target cluster individual or the target cluster center N Randomly select one element from the Randomly select one element from the N Randomly select one element from the corresponding pairing elements in the ; Randomly select two pairings corresponding to the cluster centers of two clusters or other cluster individuals, and each pairing has N The code index corresponding to the pseudo code with the lowest value in the two pairings is exchanged, and the JVC algorithm is used to generate two updated schemes after the exchange. The updated scheme with a lower target function in the two updated schemes is selected as the candidate pairing scheme. Select the optimal pairing scheme obtained in the previous iteration, randomly select a set of pseudocode groups to exchange code indices, and then use the JVC algorithm to generate candidate pairing schemes after the exchange. Select the optimal pairing scheme obtained in the previous iteration, and select one or more sets of pseudocode exchange code indices with the lowest replacement value. After the exchange, use the JVC algorithm to generate candidate pairing schemes. When the objective function of the candidate pairing scheme is less than the objective function of the original pairing scheme, the candidate pairing scheme replaces the original pairing scheme to obtain the updated pairing scheme.

5. The method of claim 1, wherein the non-fixed additional selection code pair is generated by the steps of: generating a first additional selection code pair; generating a second additional selection code pair; and combining the first and second additional selection code pairs to generate the non-fixed additional selection code pair. Based on the metaheuristic algorithm, the population is iteratively updated, and based on the JVC algorithm, the pairing schemes corresponding to individuals in the population are iteratively updated, including: The population individuals are evolved using different update methods in the metaheuristic algorithm. In the current iteration, the additional pseudocode numbers 1 to M are sorted based on the relative size of the numerical values ​​contained in the individuals, and the pairing scheme corresponding to the individuals is determined based on the JVC algorithm.

6. The method of claim 1, wherein the non-fixed additional selection code pair is generated by the steps of: generating a first additional selection code pair; generating a second additional selection code pair; and combining the first and second additional selection code pairs to form the non-fixed additional selection code pair. Determining the cost matrix based on the first and second indicators includes: A preliminary cost matrix is ​​determined based on the first and second indicators. The preliminary cost matrix contains the cost function values ​​of all paired additional pseudocodes. The cost matrix is ​​determined based on the preliminary cost matrix; The pre-cost matrix satisfies: wherein denotes the first indicator when the pseudo code m and the pseudo code n are paired, denotes the second indicator when the pseudo code m and the pseudo code n are paired, α and β denotes a user-defined weight coefficient.

7. A pairing device with a non-fixed incremental pseudocode, characterized in that, For performing the method according to any one of claims 1-6, comprising: The index setting unit is used to set the pairing index of the satellite signal additional pseudocode, including a first index and a second index. The first index is the absolute value of the alignment cross-correlation value between the received signal pilot code and the local data code, and the second index is the absolute value of the difference between the lead and lag correlation values ​​between the received signal pilot code and the received signal data code. A parameter determination unit is used to determine the objective function and cost matrix based on the first indicator and the second indicator; The model building unit is used to build an additional pseudocode allocation model based on the cost matrix and the allocation matrix of the additional pseudocode. An initialization unit is used to divide the additional pseudocode into two parts, determine the initial population based on the division result, and use the JVC algorithm to solve the allocation model of the additional pseudocode corresponding to different division results to determine the initial pairing scheme corresponding to the individuals in the initial population. An iterative unit is used to iteratively update the population based on a metaheuristic algorithm or an improved metaheuristic algorithm, and to iteratively update the pairing schemes corresponding to individuals in the population based on the JVC algorithm, until a preset maximum number of iterations is reached. The optimal pairing scheme obtained in the last iteration process is determined as the optimal pairing scheme of the non-fixed addition pseudocode; wherein, the optimal pairing scheme obtained in the iterative process is determined based on the objective function.

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