Positioning method and device and electronic equipment

Abstract

The invention provides a positioning method and device and electronic equipment, and the method comprises the steps: inputting a received orthogonal frequency division multiplexing signal into a pre-constructed signal receiving model, and obtaining a signal parameter of the orthogonal frequency division multiplexing signal; identifying data missing information in the signal parameters based on a pre-constructed data missing matrix; constructing a signal optimization function based on the data missing information; solving the signal optimization function based on an alternating multiplier algorithm to obtain arrival angle information corresponding to the orthogonal frequency division multiplexing signal; and determining position information of the user based on the arrival angle information. According to the invention, the accuracy and reliability of single base station positioning under the condition of data missing are improved.

Description

Technical Field

[0001] The present application relates to the field of communication technologies, and in particular, to a positioning method, apparatus, and electronic device. Background Art

[0002] In a single-base station positioning scenario, when the base station and the user terminal arrange antenna arrays to form a Multiple-Input Multiple-Output (MIMO) system, the multipath components in actual communication can theoretically improve the positioning accuracy. However, multipath has two sides. On the one hand, each additional path can provide more new information about the position of the mobile user. On the other hand, the increase in the number of paths also means a greater risk of noise interference, which may lead to position ambiguity and inaccuracy.

[0003] To reduce the multipath impact between multiple antennas, the Orthogonal Frequency Division Multiplexing (OFDM) technology has been widely used in the 5th Generation Mobile Communication Technology (5G) communication due to its advantage of using the orthogonality between subcarriers to resist multipath interference. However, most of the existing positioning technologies based on the OFDM architecture do not fully consider the errors brought by the broadband effect in OFDM. The broadband effect is particularly significant in high-speed mobile scenarios or complex channel environments, which may lead to frequency offset, phase distortion, and beam pointing deviation of the signal, thus having a negative impact on the positioning accuracy. At the same time, with the use of large-scale arrays and high-frequency bands in 6G, data loss has also become one of the factors that must be considered in the positioning process.

[0004] Therefore, how to improve the accuracy of single-base station positioning and consider the factor of data loss during the positioning process is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the purpose of the present application is to propose a positioning method, apparatus, and electronic device.

[0006] As an aspect of the present application, a positioning method is provided, including:

[0007] Input the received orthogonal frequency division multiplexing signal into a pre-constructed signal reception model to obtain the signal parameters of the orthogonal frequency division multiplexing signal;

[0008] Based on a pre-constructed data loss matrix, identify the data loss information in the signal parameters;

[0009] Construct a signal optimization function based on the missing data information;

[0010] Solve the signal optimization function based on the alternating direction method of multipliers (ADMM) to obtain the angle of arrival (AoA) information corresponding to the orthogonal frequency division multiplexing (OFDM) signal;

[0011] Determine the location information of the user based on the AoA information.

[0012] Optionally, constructing a signal optimization function based on the missing data information includes:

[0013] Construct an optimization objective for the OFDM signal based on the missing data information;

[0014] Generate an initial optimization function based on the Hankel matrix, Toeplitz matrix, and the optimization objective;

[0015] Add a preset auxiliary matrix variable to the initial optimization function to generate the signal optimization function.

[0016] Optionally, solving the signal optimization function based on the ADMM to obtain the AoA information corresponding to the OFDM signal includes:

[0017] Construct an augmented Lagrangian function corresponding to the signal optimization function based on the signal optimization function;

[0018] Iteratively update the augmented Lagrangian function based on the ADMM to obtain the Toeplitz matrix;

[0019] Solve the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial AoA information;

[0020] Perform broadband fusion processing on the initial AoA information to obtain the AoA information.

[0021] Optionally, iteratively updating the augmented Lagrangian function based on the ADMM to obtain the Toeplitz matrix includes:

[0022] Iteratively update the auxiliary structure matrix, variables, and Lagrange multipliers in the augmented Lagrangian function based on the ADMM;

[0023] Generate the Toeplitz matrix in response to the convergence of the ADMM.

[0024] Optionally, solving the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial AoA information includes:

[0025] Perform eigenvalue decomposition on the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain multiple sub-vector matrices;

[0026] Construct multiple sub-vector matrices based on the eigenvector matrix;

[0027] Determine the eigenvalues of the Toeplitz matrix based on multiple sub-vector matrices;

[0028] Determine the initial angle of arrival information based on the eigenvalues.

[0029] Optionally, the location information includes: the distance between the user and the base station, and the radial velocity of the user;

[0030] Determine the location information of the user based on the angle of arrival information, including:

[0031] Construct a channel gain sequence based on the angle of arrival information;

[0032] Construct a subcarrier channel gain change vector based on the channel gain sequence;

[0033] Determine the distance between the user and the base station based on the subcarrier channel gain change vector;

[0034] Construct a time slot channel gain change vector based on the channel gain sequence;

[0035] Determine the radial velocity of the user based on the time slot channel gain change vector.

[0036] Optionally, determining the distance between the user and the base station based on the subcarrier channel gain change vector includes:

[0037] Construct a frequency domain phase model based on the subcarrier channel gain change vector;

[0038] Perform an inverse discrete Fourier transform on the frequency domain phase model to determine the first angular frequency;

[0039] Determine the distance between the user and the base station based on the first angular frequency.

[0040] Optionally, determining the radial velocity of the user based on the time slot channel gain change vector includes:

[0041] Construct a time phase model based on the time slot channel gain change vector;

[0042] Perform an inverse discrete Fourier transform on the time phase model to determine the second angular frequency;

[0043] Determine the radial velocity of the user based on the second angular frequency.

[0044] As the second aspect of the present application, a positioning device is provided, including: an input module, an identification module, a construction module, a solution module, and a determination module,

[0045] The input module is configured to input the received orthogonal frequency division multiplexing signal into a pre-constructed signal reception model to obtain the signal parameters of the orthogonal frequency division multiplexing signal;

[0046] The identification module is used to identify the missing data information in the signal parameters based on the pre-constructed data missing matrix;

[0047] The construction module is used to construct a signal optimization function based on the missing data information;

[0048] The solution module is used to solve the signal optimization function based on the alternating multiplier algorithm to obtain the arrival angle information corresponding to the orthogonal frequency division multiplexing signal;

[0049] The determination module is used to determine the location information of the user based on the arrival angle information.

[0050] Optionally, the construction module is specifically used to construct the optimization objective of the orthogonal frequency division multiplexing signal based on the missing data information;

[0051] Generate an initial optimization function based on the Hankel matrix, Toeplitz matrix, and optimization objective;

[0052] Add a preset auxiliary matrix variable to the initial optimization function to generate a signal optimization function.

[0053] Optionally, the solution module is specifically used to construct an augmented Lagrangian function corresponding to the signal optimization function based on the signal optimization function;

[0054] Iteratively update the augmented Lagrangian function based on the alternating multiplier algorithm to obtain a Toeplitz matrix;

[0055] Solve the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial arrival angle information;

[0056] Perform broadband fusion processing on the initial arrival angle information to obtain the arrival angle information.

[0057] Optionally, the solution module is specifically used to iteratively update the auxiliary structure matrix, variables, and Lagrange multipliers in the augmented Lagrangian function based on the alternating multiplier algorithm;

[0058] In response to the convergence of the alternating multiplier algorithm, generate a Toeplitz matrix.

[0059] Optionally, the solution module is specifically used to perform eigenvalue decomposition on the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain multiple sub-vector matrices;

[0060] Construct multiple sub-vector matrices based on the eigenvector matrix;

[0061] Determine the eigenvalues of the Toeplitz matrix based on multiple sub-vector matrices;

[0062] Determine the initial arrival angle information based on the eigenvalues.

[0063] Optionally, the location information includes: the base station distance of the user, the radial velocity of the user; the determination module is specifically configured to construct a channel gain sequence based on the angle of arrival information;

[0064] Based on the channel gain sequence, construct a subcarrier channel gain change vector;

[0065] Based on the subcarrier channel gain change vector, determine the base station distance of the user;

[0066] Based on the channel gain sequence, construct a time slot channel gain change vector;

[0067] Based on the time slot channel gain change vector, determine the radial velocity of the user.

[0068] Optionally, the determination module is specifically configured to construct a frequency domain phase model based on the subcarrier channel gain change vector;

[0069] Perform an inverse discrete Fourier transform on the frequency domain phase model to determine the first angular frequency;

[0070] Based on the first angular frequency, determine the base station distance of the user.

[0071] Optionally, the determination module is specifically configured to determine the radial velocity of the user based on the time slot channel gain change vector, including:

[0072] Based on the time slot channel gain change vector, construct a time phase model;

[0073] Perform an inverse discrete Fourier transform on the time phase model to determine the second angular frequency;

[0074] Based on the second angular frequency, determine the radial velocity of the user.

[0075] As a third aspect of the present application, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the above positioning method when executing the program.

[0076] As a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, and the non-transitory computer-readable storage medium stores computer instructions for causing a computer to execute the above positioning method provided by the present application.

[0077] As can be seen from the above, in the case where there is data loss in the received OFDM signal, not only can the signal parameters of the OFDM signal be determined through the constructed signal reception model, but also a data loss matrix is introduced to mark the data loss information in the signal parameters. Further, signal optimization parameters are constructed based on the data loss information, and the alternating multiplier algorithm is used to solve them to obtain the angle of arrival information corresponding to the OFDM signal, and the position information of the user is determined based on the angle of arrival information, so as to overcome the problems of low positioning accuracy caused by broadband effects and data loss in the OFDM system, and significantly improve the positioning accuracy and reliability of a single base station in a complex environment. Description of the Drawings

[0078] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0079] Figure 1 Schematic diagram of the architecture of a communication system provided by an embodiment of the present application;

[0080] Figure 2 Schematic diagram of the flow of a positioning method provided by an embodiment of the present application;

[0081] Figure 3 Schematic diagram of the system structure on the base station side provided by an embodiment of the present application;

[0082] Figure 4 Schematic diagram of the flow of another positioning method provided by an embodiment of the present application;

[0083] Figure 5 Schematic diagram of the flow of yet another positioning method provided by an embodiment of the present application;

[0084] Figure 6 Schematic diagram of the flow of yet another positioning method provided by an embodiment of the present application;

[0085] Figure 7 Schematic diagram of the flow of yet another positioning method provided by an embodiment of the present application;

[0086] Figure 8 Schematic diagram of the flow of yet another positioning method provided by an embodiment of the present application;

[0087] Figure 9 Schematic diagram of the flow of yet another positioning method provided by an embodiment of the present application;

[0088] Figure 10Schematic flowchart of yet another positioning method provided by an embodiment of the present application;

[0089] Figure 11 Estimation effect diagram of an angle of arrival provided by an embodiment of the present application;

[0090] Figure 12 Another estimation effect diagram of an angle of arrival provided by an embodiment of the present application;

[0091] Figure 13 Estimation effect diagram of a channel gain provided by an embodiment of the present application;

[0092] Figure 14 Yet another estimation effect diagram of an angle of arrival provided by an embodiment of the present application;

[0093] Figure 15 Yet another estimation effect diagram of an angle of arrival provided by an embodiment of the present application;

[0094] Figure 16 Yet another estimation effect diagram of a channel gain provided by an embodiment of the present application;

[0095] Figure 17 Schematic diagram of the composition of a positioning device provided by an embodiment of the present application;

[0096] Figure 18 Schematic diagram of the composition of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0097] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the following further elaborates on the present application in detail with reference to specific embodiments and the accompanying drawings. Apparently, the described embodiments are only a part rather than all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0098] It should be noted that in the embodiments of the present application, words such as "exemplarily" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplarily" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "exemplarily" or "for example" aims to present relevant concepts in a specific manner. Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meanings understood by those with ordinary skills in the field to which the present application pertains. Summary of the Invention

[0100] In MIMO systems, the classic positioning methods are mainly trilateration or triangulation, which involve the following two stages: First, estimate location-related parameters, such as the strength of the received signal, the angle of arrival, and the time of arrival. Then, based on the above location-related parameters, the location information of the user is restored. In addition, fingerprint recognition is also a common positioning technique. These positioning methods commonly used in wireless cellular networks usually require the participation and cooperation of multiple base stations. Specifically, the positioning method that combines multiple base stations can significantly improve the positioning accuracy by multiple base stations working together, meeting the requirements of high-precision scenarios, such as intelligent transportation and disaster rescue, and achieving centimeter-level positioning accuracy. However, this method has various disadvantages, such as high computational complexity, strict synchronization requirements between base stations, and high deployment costs, which may limit its application in actual complex scenarios.

[0101] With the development of 5G and future wireless communication systems, the large bandwidth and high time resolution brought by high-frequency carriers, as well as the extremely narrow beams and high spatial resolution endowed by large antenna arrays, have broken through the technical limitations of traditional multi-base-station positioning systems, providing broader possibilities for achieving high-precision positioning based on a single base station. Especially in cellular networks, considering that mobile terminals usually establish connections with only one base station at any given time, the positioning method for mobile users based on a single base station is more common in actual deployments and has higher applicability. Compared with traditional multi-base-station cooperation schemes, the single-base-station positioning system shows many advantages in indoor scenarios: its system architecture is more concise, the deployment cost is lower, and it is easier to maintain; at the same time, since there is no need for complex inter-base-station synchronization and communication mechanisms, it is more suitable for Internet of Things and intelligent terminal scenarios with limited resources or high requirements for response real-time. However, due to the single-base-station positioning relying on limited signal sources and measurement data, its positioning accuracy in complex outdoor environments (such as multipath interference) is usually lower than that of multi-base-station positioning, and generally can achieve decimeter-level positioning accuracy, which needs to be further improved.

[0102] In the positioning scenario of a single base station, when the base station and the user terminal arrange antenna arrays to form a MIMO system, the multipath components in actual communication can theoretically improve the positioning accuracy. However, multipath has two sides. On the one hand, each additional path can provide more new information for determining the position of the mobile user. On the other hand, the increase in the number of paths also means a greater risk of noise interference, which may lead to position ambiguity and inaccuracy. In some cases, the negative impact of multipath effects on positioning accuracy exceeds the benefits of the additional information it provides, especially in the positioning applications of mobile users.

[0103] Therefore, to reduce the multipath effects between multiple antennas, OFDM technology has been widely used in 5G communication due to its advantage of using the orthogonality between subcarriers to resist multipath interference. However, most of the existing positioning technologies based on the OFDM architecture do not fully consider the errors brought by the broadband effects in OFDM (such as Doppler effect, spatial broadband effect). The broadband effects are particularly significant in high-speed mobile scenarios or complex channel environments, which may lead to frequency offset, phase distortion and beam pointing deviation of the signal, thus having a negative impact on the positioning accuracy. Due to the usually more complex multipath propagation conditions and more drastic signal dynamic changes in outdoor environments, the cumulative effect of this error is particularly prominent in outdoor scenarios. Therefore, these insufficiently considered broadband effects have become one of the key factors restricting the realization of high-precision positioning of mobile users by a single base station in outdoor scenarios, especially in scenarios requiring centimeter-level or higher precision.

[0104] At the same time, with the continuous evolution of 6G technology and the wide application of large-scale antenna arrays and high-frequency carriers, the problem of missing data in received signals has become an important challenge that cannot be ignored in wireless communication systems. On the one hand, as the scale of the array continues to expand, the probability of aging and damage of antenna elements in complex environments has increased significantly; on the other hand, the continuous increase in the communication frequency band has further shortened the signal wavelength, making the signal more vulnerable to interference and attenuation by obstacles during propagation. The combined effect of the above factors has significantly threatened the signal integrity in the actual system, and the phenomenon of data loss has become increasingly serious.

[0105] Missing data not only weakens the continuity of the communication system, but may also have a serious impact on critical service scenarios that rely on real-time data transmission. For example, in high-speed mobile communication environments (such as high-speed railway communication scenarios), the interruption or instability of data may lead to a decline in communication quality, affect the passenger experience, and even endanger train operation safety. Therefore, the effective completion of missing data and signal recovery has become a key problem that needs to be solved urgently. Although existing methods such as compressive sensing and atomic norm minimization show certain flexibility in dealing with incomplete data, they are generally restricted by the resolution limit and are difficult to be stably applied in high-precision parameter estimation scenarios. In recent years, the signal domain line spectrum estimation method based on rank-constrained structured matrix recovery and alternating direction method of multipliers has achieved a breakthrough in the framework of exact maximum likelihood estimation, and can maintain high-precision estimation of signal parameters (such as angles, frequencies, etc.) under the condition of missing data, breaking through the resolution bottleneck of traditional methods. However, the current high-precision recovery methods have not been effectively integrated with the perception and positioning tasks of mobile users. Especially in the complex environment where data loss and high-speed user movement coexist, how to simultaneously complete signal reconstruction and parameter perception is still an important direction that needs to be studied urgently.

[0106] Therefore, how to improve the accuracy of single-base station positioning and take into account the data loss factor during the positioning process is an urgent problem to be solved.

[0107] Currently, the following positioning methods mainly exist in the prior art:

[0108] (1) Multi-base station joint positioning estimation with high complexity and single-base station positioning estimation with low accuracy

[0109] In the technology of multi-base station joint estimation of the mobile user's location, first, multiple base stations are deployed within the target area. These base stations receive the user's signal through the antenna array and extract location-related characteristic parameters, such as the angle of arrival, angle of departure, and time of arrival, etc., to reflect the relative position relationship between the user and the base station. Subsequently, each base station integrates the extracted signal characteristic data through methods such as weighted average, maximum likelihood estimation, or Kalman filtering. Based on the integrated data, algorithms such as trilateration, least squares method, or weighted least squares method are used to calculate the user's location, and an optimization algorithm (such as alternately optimizing the beamforming weight and user location parameters) can be further adopted to improve the positioning accuracy and achieve centimeter-level positioning of mobile users in complex outdoor environments.

[0110] Compared with multi-base station positioning, single-base station avoids the complexity of inter-base station synchronization and data fusion, and is more suitable for scenarios where mobile devices in the cellular network are only associated with a single base station. Generally speaking, single-base station positioning technology usually utilizes the high time resolution and high spatial resolution of high-frequency carriers and large antenna arrays. The base station directly transmits signals through the antenna array and receives the reflected signals, extracts location-related characteristic parameters from them, and then conducts subsequent mobile user positioning. However, due to the fact that single-base station positioning relies on a single signal source and the data volume is relatively limited, its positioning accuracy is usually lower than that of multi-base station joint positioning, generally showing a positioning accuracy at the decimeter level.

[0111] (2) Utilizing the MIMO OFDM architecture to improve the positioning accuracy in the single-base station mobile user positioning technology

[0112] To further improve the positioning accuracy of mobile users, OFDM technology, due to the orthogonality between its subcarriers, can effectively resist multipath interference and is widely used in millimeter-wave MIMO communication systems. OFDM distributes the signal to multiple orthogonal subcarriers, significantly reducing multipath interference, and utilizes pilot signals to quickly obtain channel state information, thereby accurately extracting key positioning parameters such as time of arrival and angle of arrival, and then significantly improving the positioning accuracy.

[0113] (3) Parameter estimation methods in the case of data loss have not been closely integrated with the positioning and sensing technology of mobile users

[0114] Compressive sensing and atomic norm minimization methods are flexible in dealing with incomplete data but are usually affected by resolution limitations. Another effective data recovery method is to map the original data into the form of a Hankel matrix, and low-rank matrix factorization techniques (such as nuclear norm minimization, singular value decomposition, etc.) can be used to recover the lost data. Subsequently, the data recovery problem is transformed into an optimization problem and solved by alternating minimization or the alternating direction method of multipliers, so as to efficiently recover the complete signal from limited sampled data. This method is particularly suitable for the recovery of high-dimensional exponential signals and can reconstruct the complete signal from a small number of samples while maintaining its exponential characteristics. Although the above methods perform well in data recovery, they have not been effectively combined with existing mobile user positioning technologies. There is still a certain research gap in the field of mobile user positioning in the case of data loss.

[0115] As can be seen from the above, there are still inevitable disadvantages in the prior art. Specifically, the disadvantages include: Most of the prior art relies on multiple base stations to jointly locate mobile users, which has the disadvantages of high requirements for synchronization between base stations, strong dependence on channel state information, and high deployment costs. The existing technologies for locating multiple high-speed mobile users using a single base station avoid the high synchronization requirements between multiple base stations and greatly reduce the deployment costs, but the accuracy needs to be improved. Although the prior art often uses the MIMO-OFDM architecture to locate mobile users, most of them ignore the broadband effect of OFDM, resulting in a decline in the performance of angle-of-arrival estimation, which in turn affects the positioning accuracy. The prior art often models based on ideal situations and lacks consideration of data loss in actual communication. However, the parameter estimation method in the case of data loss has not been closely combined with the positioning and sensing technology of mobile users.

[0116] Based on this, the present application provides a positioning method, device, and electronic device, which can, in the case of data loss in the received OFDM signal, not only determine the signal parameters of the OFDM signal through the constructed signal reception model, but also introduce a data loss matrix to mark the data loss information in the signal parameters. Further, according to the data loss information, signal optimization parameters are constructed and solved using the alternating multiplier algorithm to obtain the angle-of-arrival information corresponding to the OFDM signal, and the position information of the user is determined based on the angle-of-arrival information, so as to overcome the problems of low positioning accuracy caused by the broadband effect and data loss in the OFDM system, and significantly improve the positioning accuracy and reliability of a single base station in a complex environment.

[0117] Overview of Application Scenarios

[0118] A positioning method provided by an embodiment of the present application can be applied to various communication systems, for example, a new radio (NR) communication system using 5G communication technology, a future evolved system, or a multi-communication fusion system, etc.

[0119] Exemplarily, Figure 1 A schematic structural diagram of a communication system provided by an embodiment of the present application is shown. The communication system may include a base station and multiple terminals (such as terminal 1 and terminal 2 in the figure), and the base station may be communicatively connected to the one or more terminals.

[0120] Among them, the base station can be used to implement functions such as resource scheduling, radio resource management, and radio access control of the terminal. Specifically, the base station can be any one of a small base station, a wireless access point, a transmission receive point (TRP), a transmission point (TP), and some other access nodes.

[0121] The terminal can be a terminal or a device with the function of a terminal. The terminal can also be referred to as a terminal, a user equipment (UE), a mobile station, a mobile terminal, etc. The terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality terminal, an augmented reality terminal, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote surgery, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, and so on. The specific device form adopted by the terminal in the embodiments of the present application is not limited.

[0122] It should be noted that, Figure 1 it is only an exemplary framework diagram, Figure 1 the number of devices included, the names of each device are not limited, and in addition to Figure 1 the devices shown, the communication system may also include other devices, such as core network devices.

[0123] The application scenarios of the embodiments of the present application are not limited. The system architecture and service scenarios described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation to the technical solutions provided by the embodiments of the present application. Those of ordinary skill in the art know that with the evolution of the network architecture and the emergence of new service scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0124] Figure 2 It is a schematic flow diagram of a positioning method provided by an embodiment of the present application. As Figure 2 shown, it specifically includes the following steps:

[0125] S101. Input the received orthogonal frequency division multiplexing signal into a pre-constructed signal reception model to obtain the signal parameters of the orthogonal frequency division multiplexing signal.

[0126] Note that when constructing the signal reception model, wireless broadband millimeter-wave communication between the uniform planar array on the base station side and high-speed mobile users is considered, and each user's terminal is equipped with a single antenna. Taking the use of the classic MIMO-OFDM architecture as an example, it consists of N s subcarriers, and the interval between each subcarrier is where T is the duration of the OFDM signal. The frequency of the n s th subcarrier is where f0 is the frequency of the 0th subcarrier, and n s ranges from 0 to N s -1. The center carrier frequency is f c , and its value is As shown in Figure 3 , it is the system structure diagram on the base station side. The base station side is a uniform planar array, composed of M×N antenna elements, and the spacing between adjacent antennas in the x-axis and y-axis directions is both where The origin of the three-dimensional right-handed coordinate system is located at the antenna at (0,0). The coordinates of the (m,n)th antenna element at the base station are (mD,nD), where m and n change from 0 to M-1 and N-1 respectively.

[0127] From the above, it can be known that the signal of the n s th subcarrier sent by the kth user to the planar array is The signal received by the base station at the (m,n)th antenna at time t from all K mobile users is:

[0128]

[0129] In the above formula, α k is the known channel fading coefficient, is the approximate propagation delay caused by the kth high-speed mobile user, is the first-order Taylor approximation of the distance between the (m,n)th antenna and the kth user at real time t, is the distance between the user and the base station, and θ k,x and θ k,y represent the angles of arrival with respect to the x-axis and y-axis respectively. In addition, ‖v k ‖ is a constant value of the speed. Since the frame duration is very short, such as the frame duration in the long-term evolution communication system is 1ms, the change in the user speed within each frame can be ignored.

[0130] For the (m,m)th antenna, the signal received from all K mobile users at the n t th time slot t = m t T on the n s th subcarrier is:

[0131]

[0132] In the above formula, represents independent and identically distributed circularly symmetric complex Gaussian random variables with a mean of zero and a variance of σ 2 . In addition, and ω k,x = cosθ k,x and ω k,y = cosθ k,y . In addition, the complex channel gain is expressed as:

[0133]

[0134] where and are the initial phases, is the radial velocity. ω k,s is proportional to the distance between the k-th user and the base station, while ω k,t is proportional to the radial velocity of the vehicle.

[0135] Finally, by defining an N-dimensional steering vector in the form of a N (ω)=[1,e -jω ,…,e -j(N-1)ω T the signal received by the entire planar array at the n t -th time slot on the n s -th subcarrier at the base station side can be expressed as (i.e., the signal reception model):

[0136]

[0137] In the above formula,

[0138]

[0139] The above content describes the construction process of the signal reception model and the specific content of the signal reception model. When the OFDM signal sent by the user terminal is received, inputting the OFDM signal into the signal reception model can obtain the signal parameters of the OFDM signal, including information such as amplitude, phase, and channel response. At the same time, the signal reception model also characterizes the relationship between the signal and the position information of the user terminal, that is, the correspondence relationship between the signal and the angle of arrival, the distance from the base station, and the radial velocity. Through the ω k,x , ω k,y , ω k,s and other parameters related to the position information of the user (such as position, velocity) in the model, the angle of arrival θ k,x , θ k,y ​, Base station distance and radial velocity and other information, so as to achieve the purpose of positioning perception and radial velocity estimation (specific reference is made to the description below). In addition, for the convenience of subsequent expression, and are abbreviated as Y, X, and W respectively.

[0140] S102. Based on the pre-constructed data missing matrix, identify the data missing information in the signal parameters.

[0141] It should be noted that to avoid data missing situations caused by antenna array failures at any position in the uniform planar array, by introducing the data missing matrix P, the data missing information in the signal parameters is identified, so that the signal at the missing position of the corresponding data is replaced by e -γ (γ > 0), while other signal parameters and elements remain unchanged. Based on this, the OFDM signal with data missing can be characterized by the following formula:

[0142] Y loss = P⊙(X + W)

[0143] In the above formula, ⊙ represents the Hadamard product of two matrices. In addition, the data missing situation of the OFDM signal can also be statistically analyzed by defining a data missing rate r m where the data missing rate r m = number of missing samples / number of complete samples.

[0144] S103. Based on the data missing information, construct a signal optimization function.

[0145] In some embodiments, the maximum likelihood estimation problem of the signal is transformed into an equivalent structured matrix recovery problem with rank constraints. By embedding a structured matrix composed of a Hankel matrix and a Toeplitz matrix, a signal optimization function in the case of data missing is constructed.

[0146] S104. Based on the alternating multiplier algorithm, solve the signal optimization function to obtain the arrival angle information corresponding to the orthogonal frequency division multiplexing signal.

[0147] In some embodiments, according to the alternating multiplier algorithm, the corresponding solution is iteratively updated. Then, based on the above solution, using the two-dimensional matrix pencil pairing algorithm, the initial arrival angle information is solved. Immediately afterwards, considering the broadband effect of OFDM, using the least squares method, the arrival angle information of multiple carriers is synthesized to obtain the arrival angle information in the case of missing data.

[0148] S105. Based on the arrival angle information, determine the location information of the user.

[0149] In some embodiments, the determined angle-of-arrival information is brought back into the OFDM signal to obtain the complex channel gain on each time slot and carrier, and this gain contains the distance information and radial velocity information of the mobile user. Then, channel gain sequences based on different time slots and different carriers are respectively constructed, and through the inverse discrete Fourier transform, the distance and radial velocity information of multiple users is restored.

[0150] It should be noted that the common signal parameter estimation problem is usually formulated as a maximum likelihood estimation problem with the goal of minimizing noise. However, for the signal data missing situation in this application, the accuracy of this estimation method is relatively low and it is not the most preferred. Therefore, in this application, the maximum likelihood estimation problem of the signal is transformed into an equivalent problem of restoring a structured matrix with rank constraint. By embedding a structured matrix composed of a Hankel matrix and a Toeplitz matrix, an optimization function in the case of data missing is constructed. Specifically, as Figure 4 shown, based on the data missing information, constructing a signal optimization function can be specifically implemented as the following steps:

[0151] S201. Based on the data missing information, construct the optimization objective of the orthogonal frequency division multiplexing signal.

[0152] Exemplarily, for the maximum likelihood estimation problem of the two-dimensional planar array received signal, it can be characterized by the following formula (i.e., the optimization objective):

[0153]

[0154] S202. Based on the Hankel matrix, Toeplitz matrix and the optimization objective, generate the initial optimization function.

[0155] It should be noted that since the above optimization objective is equivalent to the general form of two-dimensional line spectrum estimation, in this application, this maximum likelihood problem is expressed as a rank-constrained Hankel-Toeplitz optimization problem, which can be characterized by the following formula (i.e., the initial optimization function):

[0156]

[0157] In the above formula, represents a second-order Toeplitz matrix, represents a Hankel matrix. Taking M and N as odd numbers as an example, and making m1 = (M + 1) / 2, n1 = (N + 1) / 2, and using T m,: to represent the m-th row of the two-dimensional complex array T ∈ C M×N .

[0158] Based on this, a Toeplitz matrix of n1 × n1 is formed It should be noted that except that is a Hermitian matrix, the remaining submatrices are all non-Hermitian matrices.

[0159] All of the above first-order Toeplitz matrices are used to form a Hermitian second-order Toeplitz matrix Specifically, it can be characterized by the following formula:

[0160]

[0161] In addition, for X, by using X m,: a Hankel matrix of n1×n1 is formed where the element at the (i, j) position is provided by the (i + j - 1)-th element of X m,: where i, j ∈ {1, …, m1}.

[0162] All of the above first-order Hankel matrices are used to form a second-order Hankel matrix Characterized by the following formula:

[0163]

[0164] S203. Add a preset auxiliary matrix variable to the initial optimization function to generate a signal optimization function.

[0165] It should be noted that, in order to solve the initial optimization function, an auxiliary matrix variable Q is additionally introduced into the initial optimization function to obtain a signal optimization function, which is specifically characterized by the following formula:

[0166]

[0167] In the above formula, represents the set of Hermitian positive semi-definite matrices with rank not exceeding K, is an indicator function. From the above content, it can be seen that the signal optimization function at this time equivalently transforms a maximum likelihood estimation problem into a Hankel-Toeplitz structured matrix recovery problem with rank constraint.

[0168] In some embodiments, as Figure 5 shown, based on the alternating multiplier algorithm, solve the signal optimization function to obtain the arrival angle information corresponding to the orthogonal frequency division multiplexing signal, which can be specifically implemented as the following steps:

[0169] S301. Based on the signal optimization function, construct an augmented Lagrangian function corresponding to the signal optimization function.

[0170] It should be noted that the alternating multiplier algorithm is an effective choice for solving non-convex optimization problems (especially rank constraint problems). In order to perform the alternating multiplier algorithm, an augmented Lagrangian function corresponding to the signal optimization parameters needs to be constructed, which is characterized by the following formula:

[0171]

[0172] In the above formula, represents the Hermitian Lagrange multiplier, and ρ represents the penalty parameter.

[0173] S302. Iteratively update the augmented Lagrangian function based on the alternating multiplier algorithm to obtain a Toeplitz matrix.

[0174] In some embodiments, as Figure 6 shown, iteratively updating the augmented Lagrangian function based on the alternating multiplier algorithm to obtain a Toeplitz matrix can be specifically implemented as the following steps:

[0175] S3021. Iteratively update the auxiliary structure matrix, variables, and Lagrange multipliers in the augmented Lagrangian function based on the alternating multiplier algorithm.

[0176] It should be noted that in the alternating multiplier algorithm, for the l-th iteration (l ∈ {1,..., L}), the update of the relevant parameters can be specifically characterized by the following formula:

[0177]

[0178]

[0179] In the above formula, Q and Λ can be written in the form of block matrices, such as:

[0180]

[0181] Furthermore, the following formula (auxiliary structure matrix) can be obtained:

[0182]

[0183] In the above formula, the projection is generated according to the following steps: by performing a truncated eigenvalue decomposition on the Hermitian matrix parameter and setting all eigenvalues except the largest K eigenvalues to zero to obtain the above projection This projection ensures that Q is always in the set i.e., making this projection a Hermitian positive semi-definite matrix with a rank not exceeding K.

[0184] The above content is the relevant process of iteratively updating the auxiliary structure matrix in the augmented Lagrangian function.

[0185] It should be noted that due to the separability of variables X and T, setting the derivative of the augmented Lagrangian function with respect to {X, T} to zero gives its corresponding update solution as

[0186]

[0187] In the above formula, IMN denotes the \(MN\times MN\) identity matrix, \(J = \text{diag}\{1, 2, \ldots, m_1, m_1 - 1, \ldots, 1\}\), \(R = \text{diag}\{1, 2, \ldots, n_1, n_1 - 1, \ldots, 1\}\), and are special mapping operators for matrix \(X\), \(\text{vec}(A)\) represents stacking matrix \(A\) column - by - column into a vector, and \(\text{de - vec}(\cdot)\) represents the inverse operation of the \(\text{vec}\) operation. denotes the Kronecker product.

[0188] The above content is the relevant process of iteratively updating the variables in the augmented Lagrangian function.

[0189] It can be understood that in the actual application process, the alternating multiplier iterative update also includes the update of the Lagrange multiplier, which is used to promote the convergence of the constraint and ensure that the matrix is consistent with the signal structure. This is not elaborated here.

[0190] S3022: In response to the completion of the convergence of the alternating multiplier algorithm, generate a Toeplitz matrix.

[0191] It should be noted that the mapping operator represents the operation of mapping an \(m_1n_1\times m_1n_1\) matrix \(X\) into an \(M\times N\) matrix. Among them, represents a sparse matrix, which represents a two - dimensional conjugate adjoint operator corresponding to \(m_1n_1\times m_1n_1\), and its specific definition is the following formula:

[0192] M ad (row,:)=\(\text{vec}(\Psi p,q ) T

[0193]

[0194] In the above formula, row \(\in\{1, \ldots, MN\}\) represents the row index of the matrix. Matrix \(\Psi\) represents the Kronecker product of two diagonal matrices, and its definition is where, 1 N represents an \(N\times1\) all - one vector. The value ranges of parameters \(p\) and \(q\) include at least the following two cases: when the value range of \(p\) is from \(-m_1 + 1\) to \(-1\), the value range of \(q\) is from \(-n_1 + 1\) to \(n_1 - 1\). The value range of \(p\) is fixed at 0, and the value range of \(q\) is from \(-n_1 + 1\) to \(-1\).

[0195] The mapping operator represents the operation of mapping an \(m_1n_1\times m_1n_1\) matrix \(X\) into an \(MN\times1\) - dimensional vector. Among them, \(X rot [i,j]=X[m_1n_1 - j + 1,i].

[0196] In some embodiments, when the original residual and the dual residual are small enough, the alternating multiplier algorithm completes convergence. At this time, there exists a unique final solution T L , which is uniquely determined by the final solution of X L . The corresponding second-order Toeplitz matrix can be regarded as an m1×m1 block Toeplitz positive semi-definite matrix (i.e., Toeplitz matrix), and satisfies the Vandermonde decomposition, which is specifically characterized by the following formula:

[0197]

[0198] In the above formula, where, and are Vandermonde matrices, satisfying and In this form, it allows the subsequent use of the two-dimensional matrix pencil pairing method to extract the two-dimensional angle of arrival from .

[0199] S303. Solve the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial angle of arrival information.

[0200] In some embodiments, such as Figure 7 , solving the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial angle of arrival information can be specifically implemented as the following steps:

[0201] S3031. Perform eigenvalue decomposition on the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain multiple sub-vector matrices.

[0202] Specifically, calculate the eigenvector matrix U of the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain multiple sub-vector matrices, including: U x,1 =U[1:m1n1 - n1,:], U x,2 =U[n1 + 1:m1n1,:], U y,1 =U[1:n1:m1n1,:], U y,2 =

[0203] U[n1:n1:m1n1,:].

[0204] S3032. Determine the eigenvalues of the Toeplitz matrix based on multiple sub-vector matrices.

[0205] It can be understood that the generalized eigenvalues λ1 and λ2 of the matrix pencil pair and satisfy the following formula:

[0206]

[0207] In the above formula, z 1k and z 2k represent the corresponding generalized eigenvectors,

[0208] S3033. Determine the initial angle of arrival information based on the eigenvalue.

[0209] Based on the above eigenvalue, the parameters related to the angle of arrival can be determined, which are specifically determined by the following formula:

[0210]

[0211] At this time, let The estimated value (initial angle of arrival information) is the following formula:

[0212]

[0213] In the above formula, and represent the estimation error.

[0214] S304. Perform broadband fusion processing on the initial angle of arrival information to obtain the angle of arrival information.

[0215] In some embodiments, to utilize the broadband effect, in the actual application process, the estimation results of multiple carriers are integrated, and the and on all subcarriers are collected, and combined with the vector to obtain and the least squares estimated value (angle of arrival information), which is characterized by the following formula:

[0216]

[0217] As can be seen from the above, the angle of arrival information in the case of data absence in the OFDM signal is obtained at this time.

[0218] In some embodiments, the location information includes: the distance between the user and the base station, the radial velocity of the user. As Figure 8 shown, based on the angle of arrival information, to determine the location information of the user, it can be specifically implemented as the following steps:

[0219] S401. Construct a channel gain sequence based on the angle of arrival information.

[0220] In some embodiments, after determining the angle-of-arrival information, the complex channel gain on each time slot and subcarrier is brought back into the OFDM signal, and a channel gain sequence is generated, which is characterized by the following formula:

[0221]

[0222] S402. Based on the channel gain sequence, construct a subcarrier channel gain change vector.

[0223] To facilitate determining the distance between the user and the base station it is necessary to focus on N s subcarriers and estimate ω k,s . Rewrite the format into the format. Among them, in this way, the vector of the channel gain change of different subcarriers on its n t th time slot (subcarrier channel gain change vector) is characterized by the following formula:

[0224]

[0225] S403. Based on the subcarrier channel gain change vector, determine the distance between the user and the base station.

[0226] In some embodiments, as Figure 9 shown, based on the subcarrier channel gain change vector, determining the distance between the user and the base station can be specifically implemented as the following steps:

[0227] S4031. Based on the subcarrier channel gain change vector, construct a frequency-domain phase model.

[0228] As can be seen from the above, the subcarrier channel gain change vector can be regarded as a signal sequence with a length of N s , and its n s th element can be characterized by the following formula (frequency-domain phase model):

[0229]

[0230] S4032. Perform an inverse discrete Fourier transform on the frequency-domain phase model to determine the first angular frequency.

[0231] In some embodiments, performing an inverse discrete Fourier transform on the frequency-domain phase model gives the following formula:

[0232]

[0233] In the above formula, ω1 = 2π / N s , is the result of the inverse discrete Fourier transform. Let ω k,s =(l s +β s )ω1, where l s ∈{0,…,N s -1} and β s ∈[0,1). Based on this, β s can be determined to satisfy the following formula:

[0234]

[0235] Based on this, the first angular frequency ω k,s is determined to satisfy the following formula:

[0236]

[0237] S4033. Based on the first angular frequency, determine the distance between the user and the base station.

[0238] Based on this, the distance between the base station and the k-th user (i.e., the distance between the user and the base station) can be determined through the first angular frequency, which is characterized by the following formula:

[0239]

[0240] S404. Based on the channel gain sequence, construct a slot channel gain change vector.

[0241] It should be noted that, for the convenience of determining the radial velocities of multiple mobile users, a vector representing the change of the channel gain with time slots (slot channel gain change vector) is similarly constructed on the n s -th subcarrier based on the channel gain sequence, which is characterized by the following formula:

[0242]

[0243] In the above formula,

[0244] S405. Based on the slot channel gain change vector, determine the radial velocity of the user.

[0245] In some embodiments, as Figure 10 shown, based on the slot channel gain change vector, determining the radial velocity of the user can be specifically implemented as the following steps:

[0246] S4051. Based on the slot channel gain change vector, construct a time-phase model.

[0247] Performing phase correction on the slot channel gain change vector can obtain a signal sequence with a length of N t ​ Its n t th element is characterized by the following formula (time phase model):

[0248]

[0249] S4052. Perform inverse discrete Fourier transform processing on the time phase model to determine the second angular frequency.

[0250] Specifically, performing inverse discrete Fourier transform processing on the time phase model can obtain the following formula:

[0251]

[0252] In the above formula, ω2 = 2π / N t , is the result of the inverse discrete Fourier transform of Let t where l t ∈ {0, …, N t -1} and β t ∈ [0, 1). β can be determined as the following formula:

[0253]

[0254] Furthermore, based on β t the initial second angular frequency can be determined and characterized by the following formula:

[0255]

[0256] It can be understood that the second angular frequency is related to the carrier. Utilizing the broadband effect, the least squares estimated value (second angular frequency) of ω estimated from all subcarriers and the frequency vector can be obtained and is characterized by the following formula: k,t

[0257]

[0258] S4053. Determine the radial velocity of the user based on the second angular frequency.

[0259] In some embodiments, after determining the second angular frequency, the radial velocity of the user can be characterized by the following formula:

[0260]

[0261] ​As can be seen from the above, in the case where the received OFDM signal has missing data, not only can the signal parameters of the OFDM signal be determined through the constructed signal reception model, but also a missing data matrix is introduced to mark the missing data information in the signal parameters. Further, signal optimization parameters are constructed based on the missing data information, and the alternating multiplier algorithm is used to solve them to obtain the angle of arrival information corresponding to the OFDM signal, and the position information of the user is determined based on the angle of arrival information, so as to overcome the problems of low positioning accuracy due to broadband effects and missing data in the OFDM system, and significantly improve the positioning accuracy and reliability of a single base station in a complex environment.

[0262] To verify the feasibility of this application, simulation experiments are carried out on it. Taking a base station with a carrier frequency fixed at 38 GHz, a subcarrier spacing of 1 MHz, and an antenna array being a 7×7 uniform planar array as an example, and setting up a high-speed railway system, which includes two high-speed moving train users (K = 2). The angle of arrival of user 1 is fixed at [45°, 60°], and the angle of arrival of user 2 is fixed at [30°, 36°], and their speeds are 90 m / s and 100 m / s respectively. User 1 is 5 meters away from the base station, and the speed direction angle is 30°; user 2 is 15 meters away from the base station, and the speed direction angle is 22.5°. Assuming that the corresponding observed actual sample sizes are 40 and 30 respectively, and the complete data size is 49, which represent missing rates of approximately 20% and 40% respectively.

[0263] Specifically, as Figures 11 - 13 shown, it characterizes the determination effect of the relevant parameters of user 1. Among them, Figure 11 represents the estimation of the angle of arrival θ 1,x of user 1 under different missing data rates, Figure 12 represents the estimation of the angle of arrival θ 1,y of user 1 under different missing data rates, Figure 13 represents the estimation of the channel gain α k,t,ns of user 1 under different missing data rates. Similarly, as Figures 14 - 16 shown, it characterizes the determination effect of the relevant parameters of user 2. Among them, Figure 14 represents the estimation of the angle of arrival θ 2,x of user 2 under different missing data rates, Figure 15 represents the estimation of the angle of arrival θ 2,y of user 2 under different missing data rates, Figure 16 represents the estimation of the channel gain α k,t,ns of user 1 under different missing data rates. It can be seen that as the missing data rate increases, the mean squared error (MSE) should increase accordingly.

[0264] Among them, taking User 1 as an example, as the data missing rate increases, the mean squared error (MSE) of the angle of arrival estimation also increases accordingly. Similarly, as the sample size decreases, the Cramér-Rao Lower Bound (CRB) corresponding to different sample sizes will also increase slightly. When the missing rate is 20%, the estimation accuracy has no significant difference compared with the case of complete data, and at certain signal-to-noise ratios (SNRs), it is almost close to the estimation accuracy in the case of complete data. However, when the data missing rate is 40%, the estimation performance is extremely poor at low SNRs, and the MSEs of the two angles of arrival drop sharply at 3 dB and 0 dB respectively. This trend can be attributed to the fact that a larger sample size (i.e., a smaller missing rate) provides more information for accurate estimation, thereby reducing the influence of noise and other interferences and improving the estimation accuracy. Similarly, Figure 13 shows the results of channel gain estimation at different data missing rates. When the MSE increases as the data missing rate increases, it shows a similar downward trend as the increase in SNR and maintains a consistent magnitude at high SNRs. Based on these gains, we located the users and finally estimated the distance and radial velocity. For a missing rate of 20%, the estimated radial velocities of the two users are 77.9444 m / s and 92.3879 m / s respectively, and the estimation errors are controlled within 0.0269‰. The estimated distances are 5.0057 m and 15.0001 m respectively, and the estimation errors are controlled within 1.14‰. For a missing rate of 40%, the estimated results of the radial velocities are 77.9451 m / s and 92.4032 m / s respectively, and the estimation errors are controlled within 0.1645‰. The estimated distances are 5.0051 m and 15.0001 m respectively, and the estimation errors are controlled within 1.02‰. As the data missing rate increases, the estimation error of the radial velocity increases, while the estimation error of the distance even decreases. It is worth noting that as the data missing rate changes, the estimation results only show small fluctuations. This indicates that the sample size has little effect on the estimation accuracy, and the technical solution provided by this application still has high accuracy in positioning in the case of missing data.

[0265] In addition, this application also has the following beneficial effects:

[0266] (1) This application realizes high-precision distance positioning and radial velocity perception of multiple high-speed moving users relying only on a single base station, achieving a positioning accuracy of centimeter level or even millimeter level.

[0267] (2) By comprehensively utilizing the broadband effect, this application significantly improves the estimation accuracy of the angle of arrival, making it approach the theoretical optimal performance limit, the Cramér-Rao performance bound, and greatly reducing the estimation error.

[0268] (3) This application combines the rank-constrained matrix data recovery method based on the alternating multiplier algorithm with positioning technology. For the scenario of data loss caused by any damaged antenna element, an optimization function is established, and a new positioning method for high-precision positioning and radial velocity perception of multiple high-speed moving users in the case of data loss is proposed.

[0269] It should be noted that this application also has other implementation methods, and its overall positioning process can achieve breakthroughs through multidisciplinary integration: for example, combining the compressed sensing theory to sparsely reconstruct the multi-target parameter space, thereby significantly improving the calculation efficiency. In terms of multi-carrier processing technology, a non-orthogonal waveform system can be explored, such as using filter bank multi-carrier technology to break through the limitation of the OFDM cyclic prefix. For the data loss problem, the tensor completion algorithm can be used to maintain the integrity of the multi-dimensional data structure, and the deep features of the data can be mined through the generative adversarial network to achieve high-precision reconstruction.

[0270] This application is mainly applied to the intelligent transportation system to achieve high-precision positioning and speed estimation of autonomous driving vehicles, significantly improving vehicle navigation and safety performance. At the same time, this technology can also be extended to the underwater environment, using acoustic signals to provide positioning and speed estimation for underwater robots or submarines, facilitating marine resource exploration and underwater operations. By measuring parameters such as the propagation time and Doppler frequency shift of sound waves and combining with optimized data processing algorithms, effective positioning in the underwater environment is achieved. Specifically, it can also be used in scenarios such as vehicle-to-everything (V2X) positioning in the intelligent transportation system, unmanned aerial vehicle (UAV) swarm navigation, and industrial 4.0 automated guided vehicles, reshaping the traditional industrial form. In special scenarios, such as underground mine rescue positioning, spacecraft autonomous navigation, and medical robot surgical guidance, this technology highlights important social value. These extended applications build a complete technical system through dimensions such as signal regime innovation, array configuration optimization, and data processing algorithm upgrade on the basis of maintaining the core architecture of "single base station multi-parameter joint estimation", and have high adaptability and development potential.

[0271] It should be noted that the method of the embodiment of this application can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of these multiple devices can only execute one or more steps of the method of the embodiment of this application, and these multiple devices will interact with each other to complete the described method.

[0272] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0273] Based on the same inventive concept, corresponding to any of the above-described method embodiments, the present application also provides a positioning device.

[0274] Referring to Figure 17 , the positioning device includes: an input module 1701, an identification module 1702, a construction module 1703, a solution module 1704, and a determination module 1705.

[0275] The input module 1701 is configured to input the received orthogonal frequency division multiplexing signal into a pre-constructed signal reception model to obtain the signal parameters of the orthogonal frequency division multiplexing signal.

[0276] The identification module 1702 is configured to identify the missing data information in the signal parameters based on a pre-constructed data missing matrix.

[0277] The construction module 1703 is configured to construct a signal optimization function based on the missing data information.

[0278] The solution module 1704 is configured to solve the signal optimization function based on the alternating multiplier algorithm to obtain the angle of arrival information corresponding to the orthogonal frequency division multiplexing signal.

[0279] The determination module 1705 is configured to determine the location information of the user based on the angle of arrival information.

[0280] In some embodiments, the construction module 1703 is specifically configured to construct an optimization objective of the orthogonal frequency division multiplexing signal based on the missing data information.

[0281] Generate an initial optimization function based on the Hankel matrix, Toeplitz matrix, and optimization objective.

[0282] Add a preset auxiliary matrix variable to the initial optimization function to generate a signal optimization function.

[0283] In some embodiments, the solution module 1704 is specifically configured to construct an augmented Lagrangian function corresponding to the signal optimization function based on the signal optimization function.

[0284] Iteratively update the augmented Lagrangian function based on the alternating multiplier algorithm to obtain a Toeplitz matrix.

[0285] Solve the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial angle of arrival information;

[0286] Perform broadband fusion processing on the initial angle of arrival information to obtain the angle of arrival information.

[0287] In some embodiments, the solving module 1704 is specifically configured to iteratively update the auxiliary structure matrix, variables, and Lagrange multipliers in the augmented Lagrangian function based on the alternating multiplier algorithm;

[0288] In response to the convergence of the alternating multiplier algorithm, generate a Toeplitz matrix.

[0289] In some embodiments, the solving module 1704 is specifically configured to perform eigenvalue decomposition on the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain multiple sub-vector matrices;

[0290] Construct multiple sub-vector matrices based on the eigenvector matrix;

[0291] Based on the multiple sub-vector matrices, determine the eigenvalues of the Toeplitz matrix;

[0292] Based on the eigenvalues, determine the initial angle of arrival information.

[0293] In some embodiments, the location information includes: the base station distance of the user, the radial velocity of the user; the determining module 1705 is specifically configured to construct a channel gain sequence based on the angle of arrival information;

[0294] Construct a subcarrier channel gain change vector based on the channel gain sequence;

[0295] Based on the subcarrier channel gain change vector, determine the base station distance of the user;

[0296] Construct a time slot channel gain change vector based on the channel gain sequence;

[0297] Based on the time slot channel gain change vector, determine the radial velocity of the user.

[0298] In some embodiments, the determining module 1705 is specifically configured to construct a frequency domain phase model based on the subcarrier channel gain change vector;

[0299] Perform inverse discrete Fourier transform processing on the frequency domain phase model to determine the first angular frequency;

[0300] Based on the first angular frequency, determine the base station distance of the user.

[0301] In some embodiments, the determining module 1705 is specifically configured to determine the radial velocity of the user based on the time slot channel gain change vector, including:

[0302] Construct a time-phase model based on the time-slot channel gain change vector;

[0303] Perform an inverse discrete Fourier transform on the time-phase model to determine the second angular frequency;

[0304] Determine the radial velocity of the user based on the second angular frequency.

[0305] For the convenience of description, when describing the above device, it is divided into various modules according to functions and described separately. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0306] The device of the above embodiment is used to implement the corresponding positioning method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0307] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the positioning method described in any of the above embodiments.

[0308] Figure 18 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1810, a memory 1820, an input / output interface 1830, a communication interface 1840, and a bus 1850. Among them, the processor 1810, the memory 1820, the input / output interface 1830, and the communication interface 1840 are communicatively connected to each other inside the device through the bus 1850.

[0309] The processor 1810 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0310] The memory 1820 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1820 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1820 and are called and executed by the processor 1810.

[0311] The input / output interface 1830 is used to connect to the input / output module to achieve information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input devices can include keyboards, mice, touchscreens, microphones, various sensors, etc., and the output devices can include displays, speakers, vibrators, indicator lights, etc.

[0312] The communication interface 1840 is used to connect to the communication module (not shown in the figure) to achieve communication and interaction between this device and other devices. Among them, the communication module can achieve communication through wired means (such as USB, network cable, etc.) or through wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0313] The bus 1850 includes a path for transmitting information between various components of the device (such as the processor 1810, the memory 1820, the input / output interface 1830, and the communication interface 1840).

[0314] It should be noted that although the above device only shows the processor 1810, the memory 1820, the input / output interface 1830, the communication interface 1840, and the bus 1850, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0315] The electronic device of the above embodiment is used to implement the corresponding positioning method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0316] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium, and the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the positioning method described in any of the foregoing embodiments.

[0317] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0318] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the positioning method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0319] Based on the same inventive concept, corresponding to the positioning method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes a computer program. In some embodiments, the computer program is executable by one or more processors to cause the processors to execute the positioning method. Corresponding to the execution subjects corresponding to the steps in each embodiment of the positioning method, the processors executing the corresponding steps can belong to the corresponding execution subjects.

[0320] The computer program product of the above embodiment is used to cause the processor to execute the positioning method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0321] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; within the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.

[0322] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present application difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (i.e., these details should be fully within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be regarded as illustrative rather than restrictive.

[0323] Although the present application has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0324] Embodiments of the present application are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A positioning method, characterized in that, Including: Input the received orthogonal frequency division multiplexing (OFDM) signal into a pre-constructed signal reception model to obtain the signal parameters of the OFDM signal; Based on a pre-constructed data missing matrix, identify the data missing information in the signal parameters; Based on the data missing information, construct a signal optimization function; Based on the alternating direction method of multipliers (ADMM) algorithm, solve the signal optimization function to obtain the angle of arrival (AoA) information corresponding to the OFDM signal; Based on the AoA information, determine the location information of the user.

2. The method according to claim 1, wherein The constructing the signal optimization function based on the data missing information includes: Based on the data missing information, construct the optimization objective of the OFDM signal; Based on the Hankel matrix, Toeplitz matrix, and the optimization objective, generate an initial optimization function; Add a preset auxiliary matrix variable to the initial optimization function to generate the signal optimization function.

3. The method according to claim 1, wherein The solving the signal optimization function based on the ADMM algorithm to obtain the AoA information corresponding to the OFDM signal includes: Based on the signal optimization function, construct the augmented Lagrangian function corresponding to the signal optimization function; Based on the ADMM algorithm, iteratively update the augmented Lagrangian function to obtain the Toeplitz matrix; Based on the two-dimensional matrix pencil pairing algorithm, solve the Toeplitz matrix to obtain the initial AoA information; Perform broadband fusion processing on the initial AoA information to obtain the AoA information.

4. The method according to claim 3, wherein The iteratively updating the augmented Lagrangian function based on the ADMM algorithm to obtain the Toeplitz matrix includes: Based on the ADMM algorithm, iteratively update the auxiliary structure matrix, variables, and Lagrange multipliers in the augmented Lagrangian function; In response to the ADMM algorithm completing convergence, generate the Toeplitz matrix.

5. The method according to claim 3, characterized in that, The solving the Toeplitz matrix based on the two-dimensional matrix pencil pairing algorithm to obtain the initial AoA information includes: Based on the two-dimensional matrix pencil pairing algorithm, perform eigenvalue decomposition on the Toeplitz matrix to obtain multiple sub-vector matrices; Based on the eigenvector matrix, construct multiple sub-vector matrices; Based on the multiple sub-vector matrices, determine the eigenvalues of the Toeplitz matrix; Based on the eigenvalues, determine the initial AoA information.

6. The method according to claim 1, characterized in that, The location information includes: the distance between the user and the base station, and the radial velocity of the user; The determining the location information of the user based on the AoA information includes: Based on the AoA information, construct a channel gain sequence; Based on the channel gain sequence, construct a subcarrier channel gain change vector; Based on the subcarrier channel gain change vector, determine the distance between the user and the base station; Based on the channel gain sequence, construct a time slot channel gain change vector; Based on the time slot channel gain change vector, determine the radial velocity of the user.

7. The method according to claim 6, characterized in that The determining the distance between the user and the base station based on the subcarrier channel gain change vector includes: Based on the subcarrier channel gain change vector, construct a frequency domain phase model; Perform inverse discrete Fourier transform processing on the frequency domain phase model to determine the first angular frequency; Based on the first angular frequency, determine the distance between the user and the base station.

8. The method according to claim 6, characterized in that, Determining the radial velocity of the user based on the time slot channel gain change vector includes: Constructing a time phase model based on the time slot channel gain change vector; Performing an inverse discrete Fourier transform process on the time phase model to determine a second angular frequency; Determining the radial velocity of the user based on the second angular frequency.

9. A positioning device, characterized in that, Including: An input module, an identification module, a construction module, a solution module, and a determination module, The input module is configured to input the received orthogonal frequency division multiplexing signal into a pre-constructed signal reception model to obtain the signal parameters of the orthogonal frequency division multiplexing signal; The identification module is configured to identify the missing data information in the signal parameters based on a pre-constructed data missing matrix; The construction module is configured to construct a signal optimization function based on the missing data information; The solution module is configured to solve the signal optimization function based on the alternating multiplier algorithm to obtain the arrival angle information corresponding to the orthogonal frequency division multiplexing signal; The determination module is configured to determine the location information of the user based on the arrival angle information.

10. An electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

Images