A SPICE-based WiFi positioning super-resolution estimation method

Abstract

The application discloses a WiFi positioning super-resolution estimation method based on SPICE, and solves the problem of insufficient angle resolution caused by the limited physical aperture of an antenna array in existing WiFi positioning, and specifically comprises the following steps: first, channel state information is obtained from multiple antennas and subcarriers of a WiFi device, a measurement data model containing angle information is constructed, a sparse iterative covariance estimation algorithm is used to process the measurement data model, and the received signal covariance matrix is fitted through iteration, so that the sparsity of the solution can be promoted adaptively and without hyperparameters, so that the angle of signal arrival can be stably and high-precisely estimated from the channel state information under the condition of a small number of antennas, and compared with existing subspace algorithms, the application has higher precision under the condition of medium and low signal-to-noise ratio, and the positioning precision in an indoor environment is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication positioning technology, and more specifically to a WiFi positioning super-resolution estimation method based on SPICE. Background Technology

[0002] Traditional WiFi positioning technology relies primarily on received signal strength (RSSI). However, RSSI is susceptible to multipath fading, environmental obstruction, and dynamic interference, resulting in significant fluctuations and a positioning accuracy typically only at the meter level, which is insufficient for advanced applications such as precise navigation and behavior recognition.

[0003] To improve accuracy, positioning methods based on Channel State Information (CSI) have emerged. CSI finely characterizes the frequency response of a wireless channel, including amplitude and phase information. By extracting spatial parameters such as angle of arrival (AOA) or time of flight (TOF) from the CSI, higher-precision geometric positioning can be achieved. Among these methods, AOA estimation based on antenna arrays is one of the mainstream approaches. However, the angular resolution of traditional AOA estimation methods (such as beamforming algorithms based on Fourier transform) is limited by the aperture of the physical antenna array, following the Rayleigh limit. For commercial WiFi devices with a limited number of antennas, this inherent physical limitation prevents them from distinguishing multipath signals with too close angular intervals, resulting in a sharp decline in performance in complex and dense multipath environments.

[0004] To overcome the Rayleigh limit, super-resolution algorithms have been introduced into this field. Subspace algorithms, such as MUSIC, which represent multiple signal classification algorithms, can achieve super-resolution, but their performance is heavily dependent on the signal-to-noise ratio (SNR). In low SNR environments, peak search is prone to failure, and the algorithms require known or accurate estimation of the number of signal sources and involve eigenvalue decomposition of the covariance matrix, resulting in high computational complexity. Another type of algorithm based on sparse reconstruction theory transforms the problem into a regularized optimization problem, but its solution process often requires manual setting of regularization parameters. The selection of parameters directly affects the robustness of estimation performance, making it difficult to adapt to complex and ever-changing wireless environments in practical applications.

[0005] Therefore, the existing technology faces the following contradictions that urgently need to be addressed: on the one hand, WiFi hardware is limited and the physical resolution is insufficient; on the other hand, existing super-resolution algorithms may lack robustness and adaptability, or be computationally complex and have difficult-to-adjust parameters, which limits the practical deployment of high-precision and high-robust WiFi positioning technology in real and complex scenarios. Summary of the Invention

[0006] To overcome the defects and shortcomings of existing technologies, this invention provides a WiFi positioning super-resolution estimation method based on SPICE. This invention introduces the adaptive algorithm SPICE into the CSI super-resolution angle estimation of WiFi. In the entire estimation process, there is no need to manually set regularization parameters. By using the signal characteristics of WiFi, the CSI information on each antenna is used in concert to construct a super-resolution model, thereby achieving high-precision super-resolution positioning estimation. The SPICE algorithm used has both high resolution and high computational efficiency, and is suitable for real-time processing.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] This invention provides a WiFi positioning super-resolution estimation method based on SPICE, comprising the following steps:

[0009] The original channel state information is obtained from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point. The original channel state information is then phase-calibrated and noise-filtered to obtain the calibrated channel state information matrix.

[0010] A fine angle search grid covering the target space is constructed based on the antenna geometry of the WiFi access point, and an array manifold matrix containing the guiding vectors corresponding to each grid point is constructed based on the fine angle search grid.

[0011] Extract the spatial signal vector of a specific subcarrier or the average of all subcarriers from the calibrated channel state information matrix, and calculate its sample covariance matrix.

[0012] Set the initial estimate of the power spectrum of the signal source;

[0013] The covariance matrix of the signal is reconstructed based on the current iteration's power spectrum estimate of the signal source and the array manifold matrix;

[0014] After obtaining the covariance matrix, the estimated power spectrum of the signal source is calculated and updated for the next iteration until the iteration converges. The final estimated power spectrum of the signal source is then output as the high-resolution spatial spectrum.

[0015] As a preferred technical solution, phase calibration is performed on the original channel state information, specifically including:

[0016] The raw channel state information is represented as follows:

[0017] ;

[0018] in, Indicates the first root antenna and the first Signal attenuation on each subcarrier The original phase representing channel state information;

[0019] The original phase model is as follows:

[0020] ;

[0021] in, It is a phase related to the actual transmission path. and These are the subcarrier indexes. and antenna index The slope of the changing linear phase error. For random noise, specify the first root antenna and the first Using each subcarrier as a reference point, a two-dimensional linear regression algorithm is used to perform calculations on all... Phase at Perform fitting to estimate the slope of the linear error. and The original phase on all antennas and subcarriers is compensated to obtain the calibrated phase:

[0022] ;

[0023] The calibrated phase Compared with the original amplitude By combining these components, the calibrated channel state information matrix can be reconstructed. :

[0024] .

[0025] As a preferred technical solution, an array manifold matrix containing the guiding vectors corresponding to each grid point is constructed based on a fine-grained angle search grid, specifically including:

[0026] The possible angular spatial range of the target is set as follows: Divide the range evenly into The nth grid point, then the nth The angle values ​​for each grid point are:

[0027] ;

[0028] in, For grid resolution, and , This refers to the number of antennas.

[0029] For any grid point at any angle Its corresponding guide vector Represented as:

[0030] ;

[0031] in, The wavelength is the signal wavelength, and the superscript T indicates transpose. All... The guiding vectors corresponding to the angular grid points are arranged in columns to form an array manifold matrix. , is represented as:

[0032] .

[0033] As a preferred technical solution, the spatial signal vector of a specific subcarrier or the average of all subcarriers is extracted from the calibrated channel state information matrix, and its sample covariance matrix is ​​calculated, expressed as:

[0034] ;

[0035] Where y represents the spatial signal vector, and the superscript H represents the conjugate transpose.

[0036] As a preferred technical solution, the covariance matrix of the signal is reconstructed based on the current iterative signal source power spectrum estimate and the array manifold matrix, and is expressed as:

[0037] ;

[0038] in, This represents the power spectrum estimate of the signal source in the current iteration. Represents an array manifold matrix. This represents a diagonal matrix constructed from vector elements. This represents the total number of grid cells for fine-angle search.

[0039] As a preferred technical solution, after obtaining the covariance matrix, the estimated power spectrum of the signal source in the next iteration is calculated and updated, as follows:

[0040] ;

[0041] in, It is an array manifold matrix The corresponding number in the middle Guide vectors for each angle grid point This represents the total number of fine-angle search grids. Let represent the covariance matrix.

[0042] As a preferred technical solution, the final signal source power spectrum estimate is output as a high-resolution spatial spectrum until the iteration converges. The convergence judgment specifically includes:

[0043] Calculate the relative changes in the power spectrum before and after the update. :

[0044] ;

[0045] in, This represents the power spectrum estimate of the signal source in the current iteration. This represents the estimated power spectrum of the signal source in the next iteration;

[0046] like If the value is less than a preset threshold, the iteration is considered to have converged. When the iteration converges, the final power spectrum estimate of the signal source is output as a high-resolution spatial spectrum, and the angle grid point corresponding to the significant peak is the signal angle of arrival obtained by super-resolution estimation.

[0047] This invention also provides a SPICE-based WiFi positioning super-resolution estimation system for implementing the above-mentioned SPICE-based WiFi positioning super-resolution estimation method, comprising: a raw channel state information acquisition module, a preprocessing module, a fine angle search grid construction module, an array manifold matrix construction module, a sample covariance matrix calculation module, an initial value setting module, a reconstruction module, an iterative update module, and a result output module;

[0048] The original channel state information acquisition module is used to acquire original channel state information from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point;

[0049] The preprocessing module is used to perform phase calibration and noise filtering on the original channel state information to obtain a calibrated channel state information matrix.

[0050] The fine angle search grid construction module is used to construct a fine angle search grid covering the target space based on the antenna geometry of the WiFi access point;

[0051] The array manifold matrix construction module is used to construct an array manifold matrix containing the guiding vectors corresponding to each grid point based on a fine angle search grid;

[0052] The sample covariance matrix calculation module is used to extract a specific subcarrier or the spatial signal vector after averaging all subcarriers from the calibrated channel state information matrix and calculate its sample covariance matrix.

[0053] The initial value setting module is used to set the initial estimated value of the power spectrum of the signal source;

[0054] The reconstruction module is used to reconstruct the covariance matrix of the signal based on the current iteration's signal source power spectrum estimate and array manifold matrix;

[0055] The iterative update module is used to calculate and update the signal source power spectrum estimate for the next iteration after obtaining the covariance matrix.

[0056] The result output module is used to output the final signal source power spectrum estimate as a high-resolution spatial spectrum during iterative convergence.

[0057] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements the above-described SPICE-based WiFi positioning super-resolution estimation method.

[0058] The present invention also provides a computer device, including a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, it implements the above-described SPICE-based WiFi positioning super-resolution estimation method.

[0059] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0060] (1) The present invention introduces the adaptive algorithm SPICE into the CSI super-resolution angle estimation of WiFi, and no regularization parameters need to be manually set during the entire estimation process.

[0061] (2) The present invention uses the signal characteristics of WiFi to coordinate the CSI information on each antenna to construct a super-resolution model and realize high-precision CSI super-resolution positioning estimation.

[0062] (3) The SPICE algorithm used in this invention has both high resolution and high computational efficiency, and is suitable for real-time processing without introducing overly complex calculations. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the WiFi positioning super-resolution estimation method based on SPICE in Embodiment 1 of this paper.

[0064] Figure 2 This is a schematic diagram of the angle positioning of the smartphone at 21° in this embodiment 2. Detailed Implementation

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

[0066] Example 1

[0067] like Figure 1 As shown, this embodiment provides a WiFi positioning super-resolution estimation method based on SPICE, including the following steps:

[0068] S1: Channel State Information Acquisition and Preprocessing: Obtain raw channel state information from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point; perform phase calibration and noise filtering on the raw channel state information to obtain the calibrated channel state information matrix.

[0069] In step S1, phase calibration is performed on the original channel state information, specifically including:

[0070] Assuming the WiFi access point has root antenna and The subcarrier, the root antenna and the first The signal attenuation on each subcarrier is as follows: The original phase of the channel state information is The received channel state information can then be represented as:

[0071] ;

[0072] Original phase Including the true propagation phase and systematic errors, it can be modeled as follows:

[0073] ;

[0074] in, It is a phase related to the actual transmission path. and These are the subcarrier indexes. and antenna index The slope of the changing linear phase error. For random noise, specify the first root antenna and the first Using each subcarrier as a reference point, a two-dimensional linear regression algorithm is used to perform calculations on all... Phase at Perform fitting to estimate the slope of the linear error. and The original phase on all antennas and subcarriers is compensated to obtain the calibrated phase:

[0075] ;

[0076] The calibrated phase Compared with the original amplitude By combining these components, the calibrated channel state information matrix can be reconstructed. :

[0077] ;

[0078] S2: Super-resolution parameter estimation model construction: Based on the antenna geometry of the WiFi access point, a fine angle search grid for the coverage target space is defined; based on the fine angle search grid, an array manifold matrix containing the steering vectors corresponding to each grid point is constructed.

[0079] In step S2, a super-resolution parameter estimation model is constructed, specifically including:

[0080] Define a fine-grained angle search grid, and set the range of angles in the target's possible location in space as follows: Divide the range evenly into The nth grid point, then the nth The angle values ​​for each grid point are:

[0081] ;

[0082] in, For grid resolution, and ( (where the number of antennas is used to meet the network granularity requirements of super-resolution estimation. Assume the WiFi access point uses a...) A uniform linear array (ULA) consisting of antennas, with an element spacing of . Then for any grid point at any angle... Its corresponding guide vector It is Complex vectors:

[0083] ;

[0084] in, The wavelength is the signal wavelength, and the superscript T indicates transpose. All... The guiding vectors corresponding to the angular grid points are arranged in columns to form an array manifold matrix. :

[0085] ;

[0086] This is The matrix.

[0087] S3: Sparse Iterative Solution Based on SPICE: Using the calibrated channel state information matrix as input observation data and the array manifold matrix as the system model, a sparse iterative covariance estimation algorithm is executed, specifically including:

[0088] S31: Calculate the sample covariance matrix: from the calibrated channel state information matrix In the process, the spatial domain signal vector y, which is the average of a specific subcarrier or all subcarriers, is extracted, and its sample covariance matrix is ​​calculated. :

[0089] ;

[0090] The superscript H indicates conjugate transpose.

[0091] Then set the initial estimate of the signal source power spectrum. ,in Initialize the iteration index to represent the total number of fine-angle search grids. .

[0092] S32: Reconstruct the signal covariance matrix and update the signal source power spectrum estimate: based on the current iteration's signal source power spectrum estimate. and array manifold matrix The covariance matrix of the reconstructed signal :

[0093] ;

[0094] in, This represents a diagonal matrix composed of vector elements.

[0095] After obtaining the covariance matrix, calculate and update the signal source power spectrum estimate for the next iteration using the following formula. :

[0096] ;

[0097] in, It is an array manifold matrix The corresponding number in the middle The guide vector of each angle grid point.

[0098] S33: Convergence Criterion: Calculate the relative change in power spectrum before and after update. :

[0099] ;

[0100] like Less than the preset threshold If the iteration converges, then the iteration is considered complete; otherwise, let... Then return to step S31 to continue iterating. When the iteration converges, the final signal source power spectrum estimate is obtained. As a high-resolution spatial spectrum output, the angle grid points corresponding to significant peaks are... This is the signal arrival angle obtained from super-resolution estimation.

[0101] Example 2

[0102] This embodiment demonstrates high-precision angle-of-arrival estimation for a smartphone using a single WiFi router and wireless network card in a home or office environment. The specific steps include:

[0103] S1: This embodiment is carried out in a typical home or office environment. The core equipment required for implementation includes: a smartphone and an independent system as a measurement receiver. The system consists of a dedicated wireless network card (such as an Intel 5300 network card) that supports channel state information acquisition and its matching three-antenna array with known geometry. In this embodiment, the WiFi router only serves as a network access point to provide network connection for the mobile phone and does not participate in CSI measurement. The measurement receiver and the mobile phone work on the same WiFi channel, but are in listening mode, directly measuring the wireless signal from the mobile phone.

[0104] S2: Fix the antenna array of the measurement receiver at a measurement point with a known location and orientation (array normal direction). Place the target smartphone statically at multiple pre-determined positions (measured with a tape measure) at different angles within the room. At each position, the phone connects to the internet via a commercial WiFi router, generating continuous data traffic. Simultaneously, the wireless network card in monitoring mode captures and records all data packets from the phone signal, extracting the original complex matrix of channel state information corresponding to each data packet across all antennas and OFDM subcarriers of the network card. The true azimuth angle of each position of the mobile phone relative to the antenna array normal. Determined through geometric measurements.

[0105] S3: Due to hardware deviations in the RF path of the measurement receiver (wireless network card), the phase of the original CSI contains system errors. This step performs calibration on each acquired data, using the first antenna in the measurement receiver's antenna array and the center subcarrier in the CSI as phase reference points to calibrate the original phase. Perform a two-dimensional linear fit to estimate the hardware-induced variation with antenna index. and subcarrier index Linearly varying phase error coefficient and .

[0106] ;

[0107] S4: Based on known measurement receiver antenna spacing and WiFi signal center frequency Within the expected angular search range, a fine angular grid is generated. and construct the corresponding 3D array manifold matrix Among them, the first grid points Corresponding guide vector for:

[0108] ;

[0109] Then, the calibrated CSI data Convert to spatial observation vector And calculate its sample covariance matrix. :

[0110] ;

[0111] Next, initialize the signal power spectrum vector. And entered the SPICE core iteration, in the first... In the next iteration, the signal covariance matrix is ​​reconstructed:

[0112] ;

[0113] Update the power spectrum, where the first The power update formula for each grid point is:

[0114] ;

[0115] Iterate until the power spectrum converges. The angle corresponding to the strongest peak in the final high-resolution spatial spectrum vector output is the super-resolution angle of arrival estimate for the mobile phone signal. For example... Figure 2 As shown, the final angle estimation result is obtained, which has a narrower spectral width compared to traditional beamforming algorithms.

[0116] Example 3

[0117] This embodiment demonstrates two-dimensional location estimation for a smartphone using a single WiFi router and two wireless network cards in a home or office environment. The specific steps include:

[0118] S1: In this embodiment, two independent signal measurement systems are deployed in an indoor environment. Each system includes a dedicated wireless network card supporting CSI acquisition and its matching antenna array with a known geometric structure (such as a three-antenna uniform linear array). The two stations are fixedly placed at two different locations with known coordinates indoors, denoted as... and The baseline vector between the two stations is A regular smartphone is used as the signal source to be located, but its actual location is unknown. The WiFi router only provides network connectivity for mobile phones and is not included in the measurement.

[0119] S2: At the same time The mobile phone transmits a signal, and the wireless network cards of measurement stations A and B simultaneously listen to it, independently capture the signal, and obtain the raw CSI matrix. and Subsequently, the two stations independently performed the same processing procedure as in Example 1: after phase calibration, SPICE super-resolution estimation was used to obtain the relative angle of arrival estimate with reference to the normal direction of their respective antenna arrays. and .

[0120] S3: After obtaining the relative angle of arrival, it needs to be transformed to a unified global coordinate system to obtain the global azimuth angle. Construct a system of linear equations, obtained by simultaneously solving the equations of the two lines:

[0121] ;

[0122] Solve for the target coordinates The estimated value for:

[0123] ;

[0124] Obtain the smartphone coordinates.

[0125] Example 4

[0126] This embodiment provides a SPICE-based WiFi positioning super-resolution estimation system to implement the SPICE-based WiFi positioning super-resolution estimation method of Embodiment 1. It includes: a raw channel state information acquisition module, a preprocessing module, a fine angle search grid construction module, an array manifold matrix construction module, a sample covariance matrix calculation module, an initial value setting module, a reconstruction module, an iterative update module, and a result output module.

[0127] In this embodiment, the original channel state information acquisition module is used to acquire original channel state information from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point;

[0128] In this embodiment, the preprocessing module is used to perform phase calibration and noise filtering on the original channel state information to obtain a calibrated channel state information matrix;

[0129] In this embodiment, the fine angle search grid construction module is used to construct a fine angle search grid covering the target space based on the antenna geometry of the WiFi access point;

[0130] In this embodiment, the array manifold matrix construction module is used to construct an array manifold matrix containing the guiding vectors corresponding to each grid point based on a fine angle search grid;

[0131] In this embodiment, the sample covariance matrix calculation module is used to extract the spatial signal vector of a specific subcarrier or the average of all subcarriers from the calibrated channel state information matrix and calculate its sample covariance matrix.

[0132] In this embodiment, the initial value setting module is used to set the initial estimated value of the power spectrum of the signal source;

[0133] In this embodiment, the reconstruction module is used to reconstruct the covariance matrix of the signal based on the current iteration's signal source power spectrum estimate and the array manifold matrix;

[0134] In this embodiment, the iterative update module is used to calculate and update the signal source power spectrum estimate for the next iteration after obtaining the covariance matrix.

[0135] In this embodiment, the result output module is used to output the final signal source power spectrum estimate as a high-resolution spatial spectrum during iterative convergence.

[0136] Example 5

[0137] This embodiment provides a storage medium, which may be a ROM, RAM, disk, optical disk, or other storage medium. The storage medium stores one or more programs. When the programs are executed by the processor, they implement the SPICE-based WiFi positioning super-resolution estimation method of Embodiment 1.

[0138] Example 6

[0139] This embodiment provides a computing device, which may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer or other terminal device with display function. The computing device includes a processor and a memory. The memory stores one or more programs. When the processor executes the program stored in the memory, it implements the SPICE-based WiFi positioning super-resolution estimation method of Embodiment 1.

[0140] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A WiFi positioning super-resolution estimation method based on SPICE, characterized in that, Includes the following steps: The original channel state information is obtained from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point. The original channel state information is then phase-calibrated and noise-filtered to obtain the calibrated channel state information matrix. A fine angle search grid covering the target space is constructed based on the antenna geometry of the WiFi access point, and an array manifold matrix containing the guiding vectors corresponding to each grid point is constructed based on the fine angle search grid. Extract the spatial signal vector of a specific subcarrier or the average of all subcarriers from the calibrated channel state information matrix, and calculate its sample covariance matrix. Set the initial estimate of the power spectrum of the signal source; The covariance matrix of the signal is reconstructed based on the current iteration's power spectrum estimate of the signal source and the array manifold matrix; After obtaining the covariance matrix, the estimated power spectrum of the signal source is calculated and updated for the next iteration until the iteration converges. The final estimated power spectrum of the signal source is then output as the high-resolution spatial spectrum.

2. The SPICE-based WiFi positioning super-resolution estimation method according to claim 1, characterized in that, Phase calibration of the raw channel state information specifically includes: The raw channel state information is represented as follows: ； in, Indicates the first root antenna and the first Signal attenuation on each subcarrier The original phase representing channel state information; The original phase model is as follows: ； in, It is a phase related to the actual transmission path. and These are the subcarrier indexes. and antenna index The slope of the changing linear phase error. For random noise, specify the first root antenna and the first Using each subcarrier as a reference point, a two-dimensional linear regression algorithm is used to perform calculations on all... Phase at Perform fitting to estimate the slope of the linear error. and The original phase on all antennas and subcarriers is compensated to obtain the calibrated phase: ； The calibrated phase Compared with the original amplitude By combining these components, the calibrated channel state information matrix can be reconstructed. : 。 3. The WiFi positioning super-resolution estimation method based on SPICE according to claim 1, characterized in that, Based on a fine-grained angle search mesh, an array manifold matrix containing the guiding vectors corresponding to each grid point is constructed, specifically including: The possible angular spatial range of the target is set as follows: Divide the range evenly into Then the grid point, The angle values ​​for each grid point are: ； in, For grid resolution, and , This refers to the number of antennas. For any grid point at any angle Its corresponding guide vector Represented as: ； in, The wavelength is the signal wavelength, and the superscript T indicates transpose. All... The guiding vectors corresponding to the angular grid points are arranged in columns to form an array manifold matrix. , represented as: 。 4. The WiFi positioning super-resolution estimation method based on SPICE according to claim 1, characterized in that, Extract the spatial signal vector of a specific subcarrier or the average of all subcarriers from the calibrated channel state information matrix, and calculate its sample covariance matrix, which is expressed as: ； Where y represents the spatial signal vector, and the superscript H represents the conjugate transpose.

5. The WiFi positioning super-resolution estimation method based on SPICE according to claim 1, characterized in that, The covariance matrix of the signal, reconstructed from the current iterative power spectrum estimate of the signal source and the array manifold matrix, is expressed as: ； in, This represents the power spectrum estimate of the signal source in the current iteration. Represents an array manifold matrix. This represents a diagonal matrix constructed from vector elements. This represents the total number of grid cells for fine-angle search.

6. The WiFi positioning super-resolution estimation method based on SPICE according to claim 1, characterized in that, After obtaining the covariance matrix, calculate and update the signal source power spectrum estimate for the next iteration, as follows: ； in, It is an array manifold matrix The corresponding number in the middle Guide vectors for each angle grid point This represents the total number of fine-angle search grids. Let represent the covariance matrix.

7. The WiFi positioning super-resolution estimation method based on SPICE according to claim 1, characterized in that, The final power spectrum estimate of the signal source is output as a high-resolution spatial spectrum until the iteration converges. The convergence criteria specifically include: Calculate the relative changes in the power spectrum before and after the update. : ； in, This represents the power spectrum estimate of the signal source in the current iteration. This represents the estimated power spectrum of the signal source in the next iteration; like If the value is less than a preset threshold, the iteration is considered to have converged. When the iteration converges, the final power spectrum estimate of the signal source is output as a high-resolution spatial spectrum, and the angle grid point corresponding to the significant peak is the signal angle of arrival obtained by super-resolution estimation.

8. A WiFi positioning super-resolution estimation system based on SPICE, characterized in that, The method for implementing the SPICE-based WiFi positioning super-resolution estimation method according to any one of claims 1-7 includes: a raw channel state information acquisition module, a preprocessing module, a fine angle search grid construction module, an array manifold matrix construction module, a sample covariance matrix calculation module, an initial value setting module, a reconstruction module, an iterative update module, and a result output module. The original channel state information acquisition module is used to acquire original channel state information from multiple antennas and multiple orthogonal frequency division multiplexing subcarriers of the WiFi access point; The preprocessing module is used to perform phase calibration and noise filtering on the original channel state information to obtain a calibrated channel state information matrix. The fine angle search grid construction module is used to construct a fine angle search grid covering the target space based on the antenna geometry of the WiFi access point; The array manifold matrix construction module is used to construct an array manifold matrix containing the guiding vectors corresponding to each grid point based on a fine angle search grid; The sample covariance matrix calculation module is used to extract the spatial signal vector of a specific subcarrier or the average of all subcarriers from the calibrated channel state information matrix and calculate its sample covariance matrix. The initial value setting module is used to set the initial estimated value of the power spectrum of the signal source; The reconstruction module is used to reconstruct the covariance matrix of the signal based on the current iteration's signal source power spectrum estimate and array manifold matrix; The iterative update module is used to calculate and update the signal source power spectrum estimate for the next iteration after obtaining the covariance matrix. The result output module is used to output the final signal source power spectrum estimate as a high-resolution spatial spectrum during iterative convergence.

9. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the SPICE-based WiFi positioning super-resolution estimation method as described in any one of claims 1-7.

10. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the SPICE-based WiFi positioning super-resolution estimation method as described in any one of claims 1-7.

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