A wireless fast positioning method based on polarization code

Abstract

The present invention discloses a wireless rapid positioning method based on polarization code, which relates to the field of wireless communication technology, including: inputting a wireless signal into a polarization code encoder, generating a polarization coding sequence, constructing a channel measurement matrix, analyzing channel state information, extracting the delay characteristics and power loss characteristics of the path to generate a candidate path set, evaluating the path credibility, and screening out the optimal path set; for the generated optimal path set, extracting the characteristics of each path from the sparse characteristics of the signal, and generating a positioning characteristic vector for each path; constructing a target positioning model, fusing the path characteristics, combining a simulated annealing optimization algorithm for global search, and outputting the precise position coordinates of the target. The channel measurement matrix is ​​optimized through polarization coding characteristics, and global optimization is achieved by integrating multi-dimensional path characteristics, which not only improves positioning accuracy and robustness, and enhances adaptability in complex scenarios, but also significantly reduces computational complexity through lightweight design and efficient algorithms, and achieves efficient real-time positioning.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a wireless rapid positioning method based on polarization codes. Background Art

[0002] With the rapid development of wireless communication technology, the application demand of positioning technology in smart terminals, Internet of Things and smart driving is increasing. However, existing wireless positioning technology faces many challenges in complex channel environments, including signal multipath effects, noise interference and the influence of non-line-of-sight (NLOS) environment, resulting in positioning accuracy and robustness that cannot meet actual needs.

[0003] As an advanced channel coding technology, polarization coding has been widely used in modern communication systems due to its superior bit error rate performance and channel capacity close to the limit. However, existing technologies mainly focus on the application of polarization coding in data transmission, and its potential in wireless positioning is less studied.

[0004] Most of the current wireless positioning technologies rely on traditional channel measurement and positioning algorithms, which often require a lot of computing resources and are difficult to handle in real time for path optimization and position calculation in complex scenarios. In addition, existing path optimization methods are usually based on the characteristics of a single channel, lacking consideration of the comprehensive trade-offs of multiple channel characteristics, resulting in poor reliability and stability of positioning results. Summary of the invention

[0005] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a wireless rapid positioning method based on polarization codes to solve the above-mentioned technical problems.

[0006] To achieve the above object, the present invention provides the following technical solution: a wireless rapid positioning method based on polarization code, comprising:

[0007] Input the wireless signal into the polar code encoder to generate a polar code sequence, construct a channel measurement matrix based on the polar code sequence, analyze the channel state information, and extract the delay characteristics and power loss characteristics of the path;

[0008] Based on the channel state information and combined with the power loss characteristics, a candidate path set is generated, the path credibility is evaluated using a maximum likelihood sequence estimation algorithm, and an optimal path set is screened out according to the credibility;

[0009] For the generated optimal path set, the propagation time characteristic, frequency offset characteristic and direction vector characteristic of each path are extracted from the sparse characteristics of the signal to generate a positioning characteristic vector of each path;

[0010] The target positioning model is constructed based on the positioning characteristic vector of the path. The weighted particle filter algorithm is used to fuse the path characteristics. Combined with the simulated annealing optimization algorithm, a global search is performed to output the precise position coordinates of the target.

[0011] The present invention is further configured to input a wireless signal into a polar code encoder to generate a polar code sequence, including:

[0012] Define the initial bit sequence of the wireless signal as u, where: , N is the length of the bit sequence;

[0013] Constructing polar code generation matrix based on bit sequence length ,in, , , is the Kronecker product operation;

[0014] Interleave the bit sequence to generate an interleaved sequence ;

[0015] A polarization code sequence x is generated according to the polarization code generation matrix and the interleaving sequence, wherein: .

[0016] The present invention is further configured to construct a channel measurement matrix based on a polarization coding sequence, analyze channel state information, and extract a delay characteristic and a power loss characteristic of a path, including:

[0017] Get the polarization coded sequence of the time series of the wireless signal , perform time-frequency decomposition ,in, for The time-frequency transformation result represents the distribution of wireless signals in time and frequency. j is an imaginary unit used to describe the phase information of the signal. f is the frequency. e is a natural constant. is the integral variable, which is used to represent the integral signal within the time window. Represents the integral variable To perform integration, It is the windowing function in the short-time Fourier transform, which is used to limit the range of the signal in the time domain;

[0018] Extract the horizontal polarization component from the time-frequency decomposition results and the vertical polarization component , , ,in, is the polarization angle, used to separate the horizontal and vertical polarization components;

[0019] According to the horizontal polarization component and the vertical polarization component Construct the channel measurement matrix, ,in, is the horizontal polarization component The complex conjugate of is the vertical polarization component The complex conjugate of

[0020] performing eigenvalue decomposition on the channel measurement matrix, ,in, is the channel measurement matrix The eigenvalue matrix of , and are the eigenvalues ​​of the channel measurement matrix, representing the signal energy distribution of the primary path and the secondary path respectively;

[0021] The delay characteristics of the path are extracted through the channel measurement matrix, the multipath information is separated, and the main path delay characteristics are calculated. and secondary path delay characteristics ,in, , ;

[0022] The main path power loss characteristic is calculated based on the diagonal energy of the channel measurement matrix according to the path power loss and secondary path power loss characteristics , , .

[0023] The present invention is further configured to generate a candidate path set based on the channel state information and in combination with power loss characteristics, evaluate the path credibility using a maximum likelihood sequence estimation algorithm, and select an optimal path set according to the credibility, including:

[0024] Generate a candidate path set according to the channel state information matrix and path loss characteristics;

[0025] The maximum likelihood sequence estimation algorithm is used to quantify the path credibility of the candidate path set and calculate the credibility score of each path;

[0026] The first K paths with the highest credibility scores are set as the optimal path set.

[0027] The present invention is further configured to generate a candidate path set according to a channel state information matrix and a path loss characteristic, including:

[0028] Setting the path loss threshold , filter out the candidate path set C, , where f is the frequency variable, representing the components of different frequencies in the channel, ;

[0029] For the frequency f in the path set C, calculate the eigenvalue ratio , ,reserve The path, where is the path validity ratio threshold.

[0030] The present invention is further configured to quantify the path credibility of the candidate path set using the maximum likelihood sequence estimation algorithm, and calculate the credibility score of each path, including:

[0031] The channel state function of each path c in the path set C is defined as: ,in, is the channel state function, T is the total number of time steps, which represents the time range of the path credibility quantification process, is the conditional probability of the polarization coding sequence on path c;

[0032] Calculate the conditional probability based on the frequency energy distribution of the path, ;

[0033] Calculate the credibility score of the path, ,in, is the credibility score.

[0034] The present invention is further configured to extract the propagation time characteristics, frequency offset characteristics and direction vector characteristics of each path from the sparse characteristics of the signal for the generated optimal path set, and generate a positioning characteristic vector for each path, including:

[0035] From the generated optimal path set, the signal is decomposed into a sparse feature basis and corresponding sparse coefficients using a sparse representation method;

[0036] Calculate the propagation time characteristic, frequency offset characteristic and direction vector characteristic of each path according to the sparse characteristic basis;

[0037] A positioning characteristic vector of each path is generated according to the propagation time characteristic, the frequency offset characteristic and the direction vector characteristic.

[0038] The present invention is further configured to decompose the signal into a sparse characteristic basis and corresponding sparse coefficients using a sparse representation method, ,in, is the sparse coefficient of path i, is the sparse characteristic basis of path i, k is the total number of paths, and the sparse representation coefficient is solved by optimization: ,in, is the sparse regularization term, is the regularization parameter;

[0039] Propagation time characteristics is the moment when the signal strength is maximum, ;

[0040] Frequency offset characteristics is the frequency offset, ;

[0041] Direction vector properties , where the direction angle , is the delay change rate, ;

[0042] The positioning feature vector of each path .

[0043] The present invention is further configured to construct a target positioning model based on the positioning characteristic vector of the path, adopt a weighted particle filter algorithm to fuse the path characteristics, combine with a simulated annealing optimization algorithm to perform a global search, and output the precise position coordinates of the target, including:

[0044] Performing particle filtering fusion on the possibility of the target position according to the positioning characteristic vector to generate the probability distribution of the target, including: initializing a particle set, each particle in the particle set represents a target position, calculating the weight of each particle according to the path characteristic vector, and setting the weight of each particle to the probability distribution of the target;

[0045] The simulated annealing algorithm is used to globally optimize the particle set and converge to the precise position of the target, including: generating candidate positions near the current particle position according to the defined energy function of the target position, the initialization temperature and the current particle position, calculating the energy difference, accepting the new position according to the acceptance probability, repeating the iteration until the temperature drops to the threshold, and outputting the global optimal position.

[0046] The present invention is further configured that the particle set , is the position of the jth particle, N is the number of particles, and the calculation logic of the weight of each particle is: ;

[0047] Energy function of target position , , p is the assumed target position, It is an adjustment parameter used to balance the weight of the two parts of energy;

[0048] Energy gap The calculation logic is: , For candidate locations, is the current particle position;

[0049] Probability of acceptance The calculation logic is: , T is the current temperature, which gradually decreases with iterations;

[0050] The output logic of the global optimal position is: , is the global optimal position.

[0051] The present invention provides a wireless rapid positioning method based on polarization code, which comprises the following steps: inputting a wireless signal into a polarization code encoder to generate a polarization code sequence, constructing a channel measurement matrix based on the polarization code sequence, analyzing channel state information, and extracting a time delay characteristic and a power loss characteristic of a path; generating a candidate path set based on the channel state information and in combination with the power loss characteristic, evaluating the path credibility by using a maximum likelihood sequence estimation algorithm, and screening out an optimal path set according to the credibility; extracting the propagation time characteristic, the frequency offset characteristic and the direction vector characteristic of each path from the sparse characteristics of the signal for the generated candidate path set, and generating a positioning characteristic vector of each path; constructing a target positioning model based on the positioning characteristic vector of the path, adopting a weighted particle filter algorithm to fuse the path characteristics, and performing a global search in combination with a simulated annealing optimization algorithm, and outputting the precise position coordinates of the target, and the beneficial effects generated include:

[0052] Improve positioning accuracy: The characteristic sequence generated by polarization coding is used to optimize the channel measurement matrix, effectively separate multipath signals and non-line-of-sight signals, accurately extract the real path signal in a complex channel environment, and significantly reduce positioning errors. By comprehensively considering the multi-dimensional characteristics of the path, such as delay characteristics, frequency drift, and signal direction, the global optimization of the path is achieved, significantly improving the robustness and accuracy of the positioning results.

[0053] Enhanced adaptability to complex scenarios: For dynamic channel conditions and complex scenario environments, the dynamic adjustment and real-time optimization of path characteristic quantities can adapt to a variety of complex environments and ensure the stability of positioning performance; through the recognition of non-line-of-sight signals and path selection optimization, the inaccuracy problem of traditional positioning technology in non-line-of-sight scenarios is overcome, expanding the scope of application of positioning technology;

[0054] Improve computing efficiency and real-time performance: Through lightweight design, polarization coding characteristics are introduced into the channel matrix optimization and path selection process, reducing the computational complexity of path search and signal processing; dynamic global optimization strategy and efficient algorithm design are adopted to ensure that the positioning process can be completed efficiently with limited hardware resources and meet real-time requirements, which is particularly suitable for IoT devices and mobile terminal scenarios.

[0055] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0057] Figure 1 The present invention is a flowchart of a method for rapid wireless positioning based on polarization codes, which is shown as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0059] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0060] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0061] A wireless rapid positioning method based on polarization code, such as Figure 1 As shown, including:

[0062] Input the wireless signal into the polar code encoder to generate a polar code sequence, construct a channel measurement matrix based on the polar code sequence, analyze the channel state information, and extract the delay characteristics and power loss characteristics of the path;

[0063] Based on the channel state information and combined with the power loss characteristics, a candidate path set is generated, the path credibility is evaluated using a maximum likelihood sequence estimation algorithm, and an optimal path set is screened out according to the credibility;

[0064] For the generated optimal path set, the propagation time characteristic, frequency offset characteristic and direction vector characteristic of each path are extracted from the sparse characteristics of the signal to generate a positioning characteristic vector of each path;

[0065] The target positioning model is constructed based on the positioning characteristic vector of the path. The weighted particle filter algorithm is used to fuse the path characteristics. Combined with the simulated annealing optimization algorithm, a global search is performed to output the precise position coordinates of the target.

[0066] The present invention is further configured to input a wireless signal into a polar code encoder to generate a polar code sequence, including:

[0067] Define the initial bit sequence of the wireless signal as u, where: , N is the length of the bit sequence; the bit sequence is the input characteristic of the wireless signal and is the basic unit of the encoding process; u is the initial representation of the input signal in the polar code encoder, usually a binary sequence {0,1}, and the sequence length N is usually (n is a positive integer) to ensure that the construction of the generator matrix conforms to the definition of the polar code;

[0068] Constructing polar code generation matrix based on bit sequence length ,in, , , is the Kronecker product operation; the Kronecker product of a matrix is ​​an extended operation used to construct a matrix of a higher dimension, which is a prior art and will not be described in detail here; It is the mathematical basis of the core of polar code, which is used to convert the input bit sequence into the polar code sequence;

[0069] Interleave the bit sequence to generate an interleaved sequence ; The interleaving operation reduces interference in the channel by adjusting the order of the bit sequence and improves the robustness of the sequence. This is a prior art and will not be described in detail here; the interleaved sequence As a direct input to subsequent coding matrix operations, it ensures higher channel capacity utilization;

[0070] A polarization code sequence x is generated according to the polarization code generation matrix and the interleaving sequence, wherein: . Matrix multiplication expands the dimension and redundancy of the input sequence during the encoding process, improving the signal's anti-interference ability in the channel; the polarization code generation matrix is ​​designed based on the channel polarization theory, which can effectively distinguish between high-reliability and low-reliability channels and improve channel utilization.

[0071] The present invention is further configured to construct a channel measurement matrix based on a polarization coding sequence, analyze channel state information, and extract a delay characteristic and a power loss characteristic of a path, including:

[0072] Get the polarization coded sequence of the time series of the wireless signal , perform time-frequency decomposition ,in, for The time-frequency transformation result represents the distribution of wireless signals in time and frequency. j is an imaginary unit used to describe the phase information of the signal. f is the frequency. e is a natural constant. is the integral variable, which is used to represent the integral signal within the time window. Represents the integral variable To perform integration, It is the windowing function in the short-time Fourier transform, which is used to limit the range of the signal in the time domain, decompose the signal into frequency characteristics of small time periods, and avoid spectrum aliasing; It is a polarization-encoded time series signal, representing the original data propagated through the wireless channel. It is the intensity distribution of the signal at frequency f and time t; time-frequency analysis converts the signal from the time domain to the time-frequency domain, making it easier to extract the delay and frequency characteristics of the channel;

[0073] Extract the horizontal polarization component from the time-frequency decomposition results and the vertical polarization component , , , is the polarization angle, which is used to separate the horizontal and vertical polarization components. Determines the distribution of signals in the horizontal and vertical polarization directions; Decompose the signal to obtain horizontal and vertical components, which are used to construct a channel measurement matrix, decompose the polarization characteristics of the signal, and provide independent polarization components for subsequent channel modeling.

[0074] According to the horizontal polarization component and the vertical polarization component Construct the channel measurement matrix, ,in, is the horizontal polarization component The complex conjugate of is the vertical polarization component The complex conjugate of; the main diagonal of the matrix and represents the power of the horizontal and vertical polarization components, and Indicates the degree of coupling between horizontal and vertical polarization components. The channel measurement matrix reflects the polarization characteristics of the signal, including the power, phase and coupling degree of the signal;

[0075] performing eigenvalue decomposition on the channel measurement matrix, ,in, is the channel measurement matrix The eigenvalue matrix of , and are the eigenvalues ​​of the channel measurement matrix, representing the signal energy distribution of the primary path and the secondary path respectively; the primary and secondary path characteristics of the signal are extracted through eigenvalue decomposition;

[0076] The delay characteristics of the path are extracted through the channel measurement matrix, the multipath information is separated, and the main path delay characteristics are calculated. and secondary path delay characteristics ,in, , ;

[0077] The main path power loss characteristic is calculated based on the diagonal energy of the channel measurement matrix according to the path power loss and secondary path power loss characteristics , , The main and secondary path performance of the signal is quantified through delay and power characteristics, providing important characteristics for positioning and channel analysis; the time and frequency characteristics of the signal are extracted through time-frequency analysis, which enhances the accuracy of signal analysis; the main and secondary path characteristics are extracted using eigenvalue decomposition, which helps to analyze the multipath effect in the channel.

[0078] The present invention is further configured to generate a candidate path set based on the channel state information and in combination with power loss characteristics, evaluate the path credibility using a maximum likelihood sequence estimation algorithm, and select an optimal path set according to the credibility, including:

[0079] The present invention is further configured to generate a candidate path set according to a channel state information matrix and a path loss characteristic, including:

[0080] Setting the path loss threshold , filter out the candidate path set C, , where f is the frequency variable, representing the components of different frequencies in the channel, Specifically, the total loss of each path is calculated based on the power loss characteristics of the primary path and the secondary path. , by setting the loss threshold Screen the paths and generate a set of candidate paths. By screening the paths with higher signal quality and excluding the paths with stronger noise and interference, the positioning accuracy can be improved.

[0081] For the frequency f in the path set C, calculate the eigenvalue ratio , ,reserve The path, where is the path validity ratio threshold. is the effectiveness ratio of the path, reflecting the proportion of the main path in the total energy, is the validity threshold, with a value range of (0,1], usually 0.5, to ensure that the selected paths have dominant signals on the main path, further filter out signals with strong interference on the secondary paths, and only retain the valid paths dominated by the main path;

[0082] The present invention is further configured to quantify the path credibility of the candidate path set using the maximum likelihood sequence estimation algorithm, and calculate the credibility score of each path, including:

[0083] The channel state function of each path c in the path set C is defined as: ,in, is the channel state function, T is the total number of time steps, which represents the time range of the path credibility quantification process, is the conditional probability of the polarization coded sequence on path c; the channel state function is the cumulative quantitative value of path credibility;

[0084] Calculate the conditional probability based on the frequency energy distribution of the path, ; Conditional probability Represents the signal distribution energy of path c at time t; quantifies the credibility of the path through the maximum likelihood algorithm, providing a basis for subsequent selection of the optimal path;

[0085] Calculate the credibility score of the path, ,in, is the credibility score. right Take the logarithm and smooth the cumulative value;

[0086] The first K paths with the highest credibility scores are set as the optimal path set. The paths with the best positioning performance are selected through credibility ranking to construct the optimal path set.

[0087] The present invention is further configured to extract the propagation time characteristics, frequency offset characteristics and direction vector characteristics of each path from the sparse characteristics of the signal for the generated optimal path set, and generate a positioning characteristic vector for each path, including:

[0088] The present invention is further configured to decompose the signal into a sparse characteristic basis and corresponding sparse coefficients using a sparse representation method, ,in, is the sparse coefficient of path i, is the sparse characteristic basis of path i, k is the total number of paths, and the sparse representation coefficient is solved by optimization: ,in, is the sparse regularization term, is the regularization parameter; specifically, in optimizing the sparse representation coefficient, represents the reconstruction error of the signal decomposition, is a sparse regularization term used to control the degree of sparsity. The sparse representation method decomposes the signal into a weighted sum of finite path characteristic bases;

[0089] The propagation time characteristics, frequency offset characteristics and direction vector characteristics of each path are calculated based on the sparse characteristic basis; the propagation time characteristics is the moment when the signal strength is maximum, ;Frequency offset characteristics is the frequency offset, ; Direction vector characteristics , where the direction angle , is the delay change rate, ; Propagation time characteristics It is the propagation time of the signal from transmission to reception, reflecting the delay characteristics of the path; frequency offset characteristics It is the frequency characteristic of the path signal, reflecting the degree of frequency deviation of the signal during path propagation; directional vector characteristic Describes the directional distribution of the path, the rate of change of frequency through the propagation time and frequency offset Calculate path direction;

[0090] The positioning characteristic vector of each path is generated based on the propagation time characteristics, frequency offset characteristics and direction vector characteristics. The positioning characteristic vector of each path . Positioning feature vector It combines the time delay, frequency offset and direction information of the path to fully reflect the characteristics of the path. Through sparse optimization and multi-feature extraction, it can maintain stable path analysis capabilities under dynamic channel conditions.

[0091] The present invention is further configured to construct a target positioning model based on the positioning characteristic vector of the path, adopt a weighted particle filter algorithm to fuse the path characteristics, combine with a simulated annealing optimization algorithm to perform a global search, and output the precise position coordinates of the target, including:

[0092] The probability of the target position is subjected to particle filtering fusion according to the positioning characteristic vector to generate the probability distribution of the target, including: initializing a particle set, each particle in the particle set represents a target position, calculating the weight of each particle according to the path characteristic vector, and setting the weight of each particle as the probability distribution of the target; the present invention is further configured that the particle set , is the position of the jth particle, N is the number of particles, and the calculation logic of the weight of each particle is: ; The particle set is evenly distributed in the search space, indicating the possible location range of the target, and the weight Reflects the degree of match between the particle position and the path positioning characteristic vector; particles with a high degree of match have a larger weight, indicating that the particle is closer to the target position;

[0093] The simulated annealing algorithm is used to globally optimize the particle set and converge to the precise position of the target, including: generating candidate positions near the current particle position according to the defined energy function of the target position, the initialization temperature and the current particle position, calculating the energy difference, accepting the new position according to the acceptance probability, repeating the iteration until the temperature drops to the threshold, and outputting the global optimal position. Energy function of the target position , , p is the assumed target position, It is an adjustment parameter used to balance the weight of the two parts of energy; the energy difference The calculation logic is: , For candidate locations, is the current particle position; acceptance probability The calculation logic is: , T is the current temperature, which gradually decreases with iterations. It is a positive real number with an initial range of 1 to 10 and gradually decreases to 0.01. The output logic of the global optimal position is: , is the global optimal position. is the error between the particle position and the path direction vector, Weighted error between particles, balancing global distribution, adjusting parameters Adjust the relative importance of the two parts of the error. It is a positive real number and is adjusted according to the channel environment. The range is 0.1 to 10. In, when Always accept new solutions when When, according to probability Accept to jump out of the local optimal solution; the simulated annealing algorithm gradually reduces the temperature T and eventually converges to the global optimal solution ; It is the optimal estimate of the target position in the global search; it ensures that the most accurate target position is found in complex paths and noisy environments; by combining the particle filter algorithm and the simulated annealing algorithm, it realizes the global optimization search from the path characteristic vector to the target position. The logic is complete and the steps are clear. Through the calculation of particle weights and the minimization of energy functions, the high accuracy and robustness of the target position estimation are ensured. The reasonable setting of parameters further optimizes the algorithm performance, making it suitable for target positioning problems in multipath interference and complex channel environments.

[0094] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0095] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0096] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0103] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0104] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A wireless rapid positioning method based on polarization code, characterized in that: include: Input the wireless signal into the polar code encoder to generate a polar code sequence, construct a channel measurement matrix based on the polar code sequence, analyze the channel state information, and extract the delay characteristics and power loss characteristics of the path; Based on the channel state information and combined with the power loss characteristics, a candidate path set is generated, the path credibility is evaluated using a maximum likelihood sequence estimation algorithm, and an optimal path set is screened out according to the credibility; For the generated optimal path set, the propagation time characteristic, frequency offset characteristic and direction vector characteristic of each path are extracted from the sparse characteristics of the signal to generate a positioning characteristic vector of each path; The target positioning model is constructed based on the positioning characteristic vector of the path. The weighted particle filter algorithm is used to fuse the path characteristics. Combined with the simulated annealing optimization algorithm, a global search is performed to output the precise position coordinates of the target.

2. The method for rapid wireless positioning based on polarization code according to claim 1, characterized in that: The wireless signal is input into the polar code encoder to generate a polar code sequence, including: Define the initial bit sequence of the wireless signal as u, where: , N is the length of the bit sequence; Construct the polar code generation matrix G according to the bit sequence length N ,in, , , is the Kronecker product operation; Interleave the bit sequence to generate an interleaved sequence ; A polarization code sequence x is generated according to the polarization code generation matrix and the interleaving sequence, wherein: .

3. The wireless rapid positioning method based on polarization code according to claim 2, characterized in that: Construct a channel measurement matrix based on the polarization coding sequence, analyze the channel state information, and extract the path delay characteristics and power loss characteristics, including: Get the polarization coded sequence of the time series of the wireless signal , perform time-frequency decomposition ,in, for The time-frequency transformation result represents the distribution of wireless signals in time and frequency. j is an imaginary unit used to describe the phase information of the signal. f is the frequency. e is a natural constant. is the integral variable, which is used to represent the integral signal within the time window. Represents the integral variable To perform integration, It is the windowing function in the short-time Fourier transform, which is used to limit the range of the signal in the time domain; Extract the horizontal polarization component from the time-frequency decomposition results and the vertical polarization component , , ,in, is the polarization angle, used to separate the horizontal and vertical polarization components; According to the horizontal polarization component and the vertical polarization component Construct the channel measurement matrix, ,in, is the horizontal polarization component The complex conjugate of is the vertical polarization component The complex conjugate of performing eigenvalue decomposition on the channel measurement matrix, ,in, is the channel measurement matrix The eigenvalue matrix of , and are the eigenvalues ​​of the channel measurement matrix, representing the signal energy distribution of the primary path and the secondary path respectively; The delay characteristics of the path are extracted through the channel measurement matrix, the multipath information is separated, and the main path delay characteristics are calculated. and secondary path delay characteristics ,in, , ; The main path power loss characteristic is calculated based on the diagonal energy of the channel measurement matrix according to the path power loss and secondary path power loss characteristics , , .

4. The wireless rapid positioning method based on polarization code according to claim 3 is characterized in that: Based on the channel state information and in combination with the power loss characteristics, a candidate path set is generated, a maximum likelihood sequence estimation algorithm is used to evaluate the path credibility, and an optimal path set is screened out according to the credibility, including: Generate a candidate path set according to the channel state information matrix and path loss characteristics; The maximum likelihood sequence estimation algorithm is used to quantify the path credibility of the candidate path set and calculate the credibility score of each path; The first K paths with the highest credibility scores are set as the optimal path set.

5. The wireless rapid positioning method based on polarization code according to claim 4 is characterized in that: According to the channel state information matrix and path loss characteristics, a candidate path set is generated, including: Setting the path loss threshold , filter out the candidate path set C, , where f is the frequency variable, representing the components of different frequencies in the channel, ; For the frequency f in the path set C, calculate the eigenvalue ratio , ,reserve The path, where is the path validity ratio threshold.

6. The wireless rapid positioning method based on polarization code according to claim 4, characterized in that: The maximum likelihood sequence estimation algorithm is used to quantify the path credibility of the candidate path set and calculate the credibility score of each path, including: The channel state function of each path c in the path set C is defined as: ,in, is the channel state function, T is the total number of time steps, which represents the time range of the path credibility quantification process, is the conditional probability of the polarization coding sequence on path c; Calculate the conditional probability based on the frequency energy distribution of the path, ; Calculate the credibility score of the path, ,in, is the credibility score.

7. The wireless rapid positioning method based on polarization code according to claim 1, characterized in that: For the generated optimal path set, the propagation time characteristics, frequency offset characteristics and direction vector characteristics of each path are extracted from the sparse characteristics of the signal to generate the positioning characteristic vector of each path, including: From the generated optimal path set, the signal is decomposed into a sparse feature basis and corresponding sparse coefficients using a sparse representation method; Calculate the propagation time characteristic, frequency offset characteristic and direction vector characteristic of each path according to the sparse characteristic basis; A positioning characteristic vector of each path is generated according to the propagation time characteristic, the frequency offset characteristic and the direction vector characteristic.

8. The method for rapid wireless positioning based on polarization code according to claim 7, characterized in that: The sparse representation method is used to decompose the signal into sparse feature bases and corresponding sparse coefficients. ,in, is the sparse coefficient of path i, is the sparse characteristic basis of path i, k is the total number of paths, and the sparse representation coefficient is solved by optimization: ,in, is the sparse regularization term, is the regularization parameter; Propagation time characteristics is the moment when the signal strength is maximum, ; Frequency offset characteristics is the frequency offset, ; Direction vector properties , where the direction angle , is the delay change rate, ; The positioning feature vector of each path .

9. The wireless rapid positioning method based on polarization code according to claim 8, characterized in that: The target positioning model is constructed based on the positioning characteristic vector of the path. The weighted particle filter algorithm is used to fuse the path characteristics. Combined with the simulated annealing optimization algorithm, a global search is performed to output the precise position coordinates of the target, including: Performing particle filtering fusion on the possibility of the target position according to the positioning characteristic vector to generate the probability distribution of the target, including: initializing a particle set, each particle in the particle set represents a target position, calculating the weight of each particle according to the path characteristic vector, and setting the weight of each particle to the probability distribution of the target; The simulated annealing algorithm is used to globally optimize the particle set and converge to the precise position of the target, including: generating candidate positions near the current particle position according to the defined energy function of the target position, the initialization temperature and the current particle position, calculating the energy difference, accepting the new position according to the acceptance probability, repeating the iteration until the temperature drops to the threshold, and outputting the global optimal position.

10. The wireless rapid positioning method based on polarization code according to claim 9, characterized in that: Particle Set , is the position of the jth particle, N is the number of particles, and the calculation logic of the weight of each particle is: ; Energy function of target position , , p is the assumed target position, It is an adjustment parameter used to balance the weight of the two parts of energy; Energy gap The calculation logic is: , For candidate locations, is the current particle position; Probability of acceptance The calculation logic is: , T is the current temperature, which gradually decreases with iterations; The output logic of the global optimal position is: , is the global optimal position.

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