Wireless communication terminal accurate positioning device and use method thereof

Abstract

The invention relates to the technical field of wireless communication, and discloses a wireless communication terminal accurate positioning device and a use method thereof, and the method comprises the following steps: S1, receiving a composite signal of a terminal through a signal receiving module, and then carrying out filtering and frequency domain analysis to reduce noise and interference; s2, sparse decomposition is carried out on the preprocessed signals, direct path signals are extracted, and a signal state model containing time, direction and intensity is constructed; s3, signals are optimized and acquired through a dynamic filtering algorithm, and the optimal direction is determined through information entropy analysis; and S4, calculating the distance between the terminal and the base station, and further obtaining the three-dimensional space coordinates of the terminal. By eliminating the multipath interference and extracting the time and direction characteristics of the signal, the technical effect of realizing high-precision positioning in a complex environment is achieved. Compared with a positioning scheme depending on multiple antennas and beamforming in the prior art, the method solves the defect that direction analysis in a high-complexity scene is easily interfered by reflected signals.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a precise positioning device for a wireless communication terminal and a use method thereof. Background Art

[0002] In the current wireless communication field, positioning technology has become one of the key application directions. AOA (angle of arrival) positioning technology has been widely used in many scenarios because of its advantage of not requiring large-scale clock synchronization. By analyzing the direction of arrival of the signal through the antenna array and combining it with distance measurement, the spatial positioning of the terminal can be achieved. At the same time, radar positioning has shown its important value with its high accuracy, especially in indoor environments. In addition, in order to support large-scale user positioning, existing technologies have gradually combined methods such as beamforming and multipath signal modeling to improve system performance. However, the implementation of these technologies usually requires a high degree of collaboration between the terminal and the positioning system, or relies on complex hardware equipment and high-performance algorithms, which limits the adaptability of positioning technology in some special scenarios.

[0003] Although the existing technology has achieved certain results, there are still some problems that need to be solved. For example, AOA positioning is easily interfered by reflected signals in a multipath environment, resulting in a decrease in the accuracy of directional resolution. Although radar positioning can achieve high precision in some scenarios, its high hardware cost and complexity limit its popularity. In addition, in multi-user, large-scale scenarios, it is difficult for existing technologies to effectively balance high precision and system real-time performance, and performance bottlenecks are prone to occur when locating high-density users. In particular, for low-cost positioning requirements and those that do not require terminal collaboration, existing technologies still lack a low-complexity, highly adaptable solution. Therefore, how to achieve high-precision, low-cost, large-scale positioning in a complex environment has become a key issue that needs to be addressed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a precise positioning device for a wireless communication terminal and a method for using the same, which solves the problem that the prior wireless communication positioning technology cannot take into account both high-precision positioning and large-scale user support in complex environments.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for using a wireless communication terminal precise positioning device comprises the following steps: S1. Receive a composite signal sent by a wireless communication terminal through a signal receiving module, and pre-process the received signal, including filtering and frequency domain analysis to reduce noise and interference; S2. Decomposing the preprocessed composite signal, extracting the direct path signal through sparse signal decomposition, and constructing a signal state model based on the extracted direct path signal; S3. Track and optimize the signal state through a dynamic filtering algorithm, obtain the optimized time, direction and strength information, and perform information entropy analysis on the optimized signal direction distribution to determine the optimal direction; S4. Calculate the distance between the terminal and the base station based on the optimal direction and signal propagation delay, and obtain a three-dimensional positioning result of the terminal based on the distance and direction information.

[0006] Preferably, in the step S1, the pretreatment step comprises: (1) Bandpass filtering the composite signal to eliminate high-frequency noise and environmental interference; (2) Perform fast Fourier transform on the filtered signal to obtain the frequency domain characteristics of the signal for subsequent signal decomposition.

[0007] Preferably, in step S2, signal decomposition is achieved by a sparse optimization method, which specifically includes: (1) Sparsely represent the signal based on the sparse basis matrix and decompose the signal into a linear combination of sparse coefficients; (2) Minimize the absolute value sum of the sparse coefficients and extract the high-amplitude part of the sparse coefficients corresponding to the direct path signal; (3) Reconstruct the direct path signal based on the extraction results.

[0008] Preferably, in step S2, the signal state model includes a time state, a direction state and a signal strength state, and the state change satisfies the following state transition equation and measurement equation: (1) State transfer equation: ; (2) Measurement equation: ; in, is the signal state vector, is the measurement vector, is the state transfer matrix, is the measurement matrix, and are process noise and measurement noise respectively, which satisfy Gaussian distribution.

[0009] Preferably, in step S3, the dynamic filtering algorithm includes: (1) Predict signal states and state covariance; (2) Update the signal state and covariance matrix based on the received measurement information; (3) Obtain optimized time, direction, and intensity information through the corrected signal state.

[0010] Preferably, in the step S3, the step of performing information entropy analysis on the signal direction distribution includes: (1) Normalize the signal direction distribution and calculate the probability distribution of the normalized signal; (2) Calculate the entropy value of the direction distribution based on the normalized distribution; (3) Determine the optimal direction based on the direction with the smallest directional distribution entropy value.

[0011] Preferably, in the step S4, when calculating the distance between the terminal and the base station, the propagation delay is determined by the arrival time of the direct path signal, and the distance is calculated by the product of the propagation delay and the signal propagation speed, wherein the signal propagation speed is dynamically corrected based on the ambient temperature.

[0012] Preferably, in step S4, the three-dimensional positioning result of the terminal is obtained by: (1) The horizontal plane direction is determined by the optimal direction angle; (2) The vertical direction is analyzed by the vertical component signal based on the antenna array to calculate the vertical angle; (3) Calculate the three-dimensional coordinates by combining the horizontal direction, vertical direction and distance information.

[0013] A wireless communication terminal precise positioning device, comprising: A signal receiving module, used to receive a composite signal sent by a wireless communication terminal and pre-process the signal, wherein the pre-processing includes filtering and frequency domain analysis of the signal to reduce noise and interference; A signal decomposition module is used to perform sparse decomposition on the preprocessed composite signal and extract the direct path signal; The signal optimization module is used to build a signal state model based on the extracted direct path signal and optimize the time, direction and strength of the signal through a dynamic filtering algorithm; A direction determination module is used to perform information entropy analysis on the optimized signal direction distribution and determine the optimal direction; A dynamic correction module is used to update the direction weight according to the deviation between the path direction and the optimal direction, and to correct the direction distribution; A positioning calculation module, used to calculate the positioning result of the terminal by combining the optimal direction, propagation delay and direction information after dynamic correction; The vertical component analysis module is used to determine the vertical angle by combining the received vertical direction signal with the signal analysis of the antenna array to support three-dimensional positioning calculation.

[0014] Preferably, the signal decomposition module includes a sparse representation unit and a sparse optimization unit, the sparse representation unit is used to represent the composite signal as a linear combination of sparse basis matrices, and the sparse optimization unit extracts the direct path signal by optimizing the absolute value sum of the sparse coefficients; The signal optimization module includes a state modeling unit and a filtering unit, the state modeling unit is used to construct a signal state model, and the filtering unit optimizes the time, direction and strength information of the signal through a dynamic filtering algorithm of prediction and correction; The direction determination module calculates information entropy on the normalized probability distribution of the direction signal, and the direction with the minimum entropy corresponds to the optimal direction.

[0015] The present invention provides a wireless communication terminal precise positioning device and a method for using the same. It has the following beneficial effects: 1. The present invention adopts a technical solution based on direct path signal extraction and sparse decomposition to eliminate multipath interference and extract the time and direction characteristics of the signal, thereby achieving the technical effect of high-precision positioning in complex environments. Compared with the positioning solution that relies on multiple antennas and beamforming in the prior art, it solves the problem that directional resolution in highly complex scenes is easily interfered by reflected signals.

[0016] 2. The present invention combines the Kalman filter algorithm and the information entropy analysis method to dynamically optimize the time and direction state of the signal, and successfully achieves stable direction determination in multi-user scenarios. This technical solution avoids the problem of low efficiency of radar positioning systems in large-scale user scenarios in the prior art, and significantly reduces the dependence on hardware equipment, which is suitable for low-cost deployment.

[0017] 3. The present invention further improves the positioning accuracy by combining the propagation delay correction technology with the three-dimensional angle analysis solution, especially maintaining stable performance in dynamic environments. Compared with the distance calculation method that relies on the fixed propagation speed assumption in the prior art, it solves the problem of inaccurate positioning results caused by propagation speed errors in dynamic environments, and is particularly suitable for positioning needs in complex buildings or dynamic environments.

[0018] 4. The present invention adopts a single-device positioning solution that does not require terminal cooperation, and supports real-time and accurate positioning of high-density terminals. Compared with the collaborative positioning solution in the prior art that requires high-performance support from terminals, it solves the problems of inconsistent terminal performance and high deployment costs, and achieves the goal of a simple system, low cost and suitable for large-scale positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method steps of the present invention; Figure 2 It is a schematic diagram of the device module of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1: Please see attached Figure 1 The embodiment of the present invention provides a method for using a wireless communication terminal precise positioning device, comprising the following steps: S1. Receive a composite signal sent by a wireless communication terminal through a signal receiving module, and pre-process the received signal, including filtering and frequency domain analysis to reduce noise and interference; S2. Decomposing the preprocessed composite signal, extracting the direct path signal through sparse signal decomposition, and constructing a signal state model based on the extracted direct path signal; S3. Track and optimize the signal state through a dynamic filtering algorithm, obtain the optimized time, direction and strength information, and perform information entropy analysis on the optimized signal direction distribution to determine the optimal direction; S4. Calculate the distance between the terminal and the base station based on the optimal direction and signal propagation delay, and obtain a three-dimensional positioning result of the terminal based on the distance and direction information; In step S1, the preprocessing steps include: (1) Bandpass filtering the composite signal to eliminate high-frequency noise and environmental interference; (2) Perform fast Fourier transform on the filtered signal to obtain the frequency domain characteristics of the signal for subsequent signal decomposition; In step S2, signal decomposition is achieved through sparse optimization methods, including: (1) Sparsely represent the signal based on the sparse basis matrix and decompose the signal into a linear combination of sparse coefficients; (2) Minimize the absolute value sum of the sparse coefficients and extract the high-amplitude part of the sparse coefficients corresponding to the direct path signal; (3) Reconstruct the direct path signal based on the extraction results; In step S2, the signal state model includes time state, direction state and signal strength state, and the state change satisfies the following state transition equation and measurement equation: (1) State transfer equation: ; (2) Measurement equation: ; in, is the signal state vector, is the measurement vector, is the state transfer matrix, is the measurement matrix, and are process noise and measurement noise respectively, which satisfy Gaussian distribution; In step S3, the dynamic filtering algorithm includes: (1) Predict signal states and state covariance; (2) Update the signal state and covariance matrix based on the received measurement information; (3) Obtain optimized time, direction, and intensity information through the corrected signal state; In step S3, the steps of performing information entropy analysis on the signal direction distribution include: (1) Normalize the signal direction distribution and calculate the probability distribution of the normalized signal; (2) Calculate the entropy value of the direction distribution based on the normalized distribution; (3) Determine the optimal direction based on the direction with the smallest directional distribution entropy value; In step S4, when calculating the distance between the terminal and the base station, the propagation delay is determined by the arrival time of the direct path signal, and the distance is calculated by the product of the propagation delay and the signal propagation speed, where the signal propagation speed is dynamically corrected based on the ambient temperature; In step S4, the three-dimensional positioning result of the terminal is obtained by: (1) The horizontal plane direction is determined by the optimal direction angle; (2) The vertical direction is analyzed by the vertical component signal based on the antenna array to calculate the vertical angle; (3) Calculate the three-dimensional coordinates by combining the horizontal direction, vertical direction and distance information.

[0022] Specifically, step S1 plays a role in laying the foundation in the present invention. Signal reception and preprocessing are directly related to the accuracy of subsequent signal decomposition and dynamic optimization. The received signal is subject to various interferences in a multipath environment, including direct path signals and multiple reflected path signals, and may even be mixed with random noise. In order to ensure that the subsequent steps can accurately extract the direct path signal, signal preprocessing is an indispensable step. The following is a detailed description of the specific implementation method of signal reception and preprocessing.

[0023] The signal receiving module in the present invention is used to capture the composite signal sent by the wireless communication terminal. The composite signal is expressed in the form of: ; in, : Composite signal, which is the complete signal captured by the receiving end; : Direct path signal, which is the key signal for subsequent extraction and optimization in the present invention; : No. The reflected path signal usually has different delays and directions; : Random noise, assumed to be Gaussian white noise, satisfies )~ ; : The number of reflected path signals is usually closely related to the complexity of the surrounding environment.

[0024] In some embodiments, the signal receiving module is equipped with a wide coverage antenna, such as a directional plate antenna or an omnidirectional antenna, to ensure that sufficient signal power can be received. At the same time, the signal sampling rate inside the receiving module satisfies the Nyquist sampling theorem, and the sampling frequency Should be at least twice the signal bandwidth: ; in, : sampling frequency; : Signal bandwidth, usually determined by the standard of the communication system, such as 20MHz or 40MHz.

[0025] In this embodiment, the received signal is first filtered by a bandpass filter to eliminate high-frequency noise and low-frequency interference. The design of the bandpass filter is based on the center frequency of the signal. and bandwidth By making a selection, specific parameters can be adjusted to suit different communication environments.

[0026] The filtered signal can be expressed as: ; in, : The filtered signal; : impulse response of the bandpass filter; : Convolution operation.

[0027] As an option, the center frequency of the filter and bandwidth It can be set according to the actual needs of the communication system.

[0028] For example: For signals in the 2.4GHz band, the bandpass filter can be set .

[0029] For signals in the 5 GHz band, the bandpass filter can be set .

[0030] In some embodiments, a finite impulse response (FIR) filter may be used to implement bandpass filtering. The impulse response of the FIR filter is: ; in, : filter weight coefficient; : Delay is The impact function of : filter order; : Sampling period.

[0031] The design of FIR filter can be done by window function method, such as Hanning window, Heiman window or Kaiser window, and the specific choice depends on the bandwidth and transition band requirements.

[0032] The filtered signal The frequency domain analysis is further performed through Fast Fourier Transform (FFT). The purpose of FFT is to extract the frequency characteristics of the signal and provide basic data for subsequent sparse decomposition.

[0033] The formula for FFT is as follows: ; in, : The amplitude of the signal at the frequency; : The value of the time domain signal at the sampling point; : sampling period; : The number of sampling points of FFT; : Complex exponential basis function.

[0034] In the frequency domain, different path signals usually appear as different frequency components. The frequency component amplitude of the direct path signal is larger, while the amplitude of the reflected path signal is relatively smaller. Based on the amplitude, the possible frequency components of the direct path signal can be preliminarily screened out.

[0035] In some embodiments, the FFT result can be truncated at a low amplitude to further reduce the impact of noise. The specific method is to set the amplitude threshold ,when When , the corresponding frequency component is set to zero. Threshold It can be dynamically adjusted according to the signal-to-noise ratio, for example, set to 5% of the maximum amplitude.

[0036] In practical applications, signal preprocessing may need to be optimized according to environmental conditions. For example: In a strong interference environment, the bandwidth of the bandpass filter can be dynamically adjusted to enhance anti-interference capabilities; In complex multipath environments, the number of FFT sampling points can be increased to improve frequency resolution.

[0037] In addition, in some high-noise scenarios, adaptive filters can be introduced. The adaptive filter adjusts the weight coefficients in real time. , further optimize the filtering effect.

[0038] The core goal of step S2 is to extract the direct path signal from the preprocessed composite signal through sparse signal decomposition, and further construct the state model of the signal. Composite signals usually contain complex interference caused by multipath effects, such as delays and direction changes of reflected path signals. Sparse signal decomposition technology can extract high-confidence components related to direct path signals from multidimensional signals through sparse basis optimization methods. Subsequently, the state vectors of time, direction and intensity are constructed through the extracted direct path signal, providing an accurate basis for subsequent dynamic optimization.

[0039] In this embodiment, sparse signal decomposition is achieved by combining a sparse base matrix and a sparse optimization algorithm, and a complex composite signal is represented as a linear combination of sparse coefficients.

[0040] First, assume that the signal after preprocessing in step S1 is Has the following expression: ; in, : Preprocessed time domain signal; : sparse basis matrix, size is ,in is the number of signal sampling points, is the characteristic number of the sparse basis; : sparse coefficient vector, dimension is , where only a few elements are non-zero; : Residual noise, satisfying .

[0041] In general, a sparse basis matrix The selection of needs to match the signal characteristics. In this embodiment, commonly used sparse bases include discrete cosine transform (DCT) base and discrete wavelet transform (DWT) base. Among them, the DCT base is suitable for sparse representation of stationary signals, while the DWT base has advantages in processing multi-resolution signals.

[0042] Based on the sparse representation, the sparse coefficient vector is solved by the sparse optimization method. The optimization goal is to minimize the sparse coefficients norm, and at the same time control the signal reconstruction error within the allowable range. The optimization problem can be expressed as: ; in, : Sparse coefficients Norm, used to indicate the degree of sparsity; : represents solving the minimization problem; : signal reconstruction error; : Error tolerance, generally adjusted dynamically according to the signal-to-noise ratio, usually set to ϵ≤0.05× .

[0043] In one possible implementation, the sparse optimization algorithm may use an iterative convergence method, such as an iterative soft threshold algorithm (ISTA) or an accelerated iterative soft threshold algorithm (FISTA). For example, the iterative update formula of ISTA is: ; in, : No. The sparse coefficients of the iteration; : Learning rate, used to control the convergence speed; : Regularization parameter used to balance sparsity and reconstruction error; : The signal after filtering; : Soft threshold function, defined as: ; Through multiple iterations, the sparse coefficient vector Finally, it converges. The non-zero coefficient part corresponds to the main component of the direct path signal. Then, using the sparse basis matrix and the sparse coefficient , the direct path signal can be reconstructed: ; in, : sparse basis associated with the direct path signal; : The sparse coefficient corresponding to the direct path signal.

[0044] After the direct path signal extraction is completed, it is necessary to further construct a signal state model to describe the time, direction and strength characteristics of the signal. In this embodiment, the signal state model is defined as: ; in, : signal arrival time; : signal arrival direction; : Signal reception strength.

[0045] In general, the signal arrival time is calculated from the time delay of the direct path signal, and its formula is: ; in, : The index value of the sampling point where the main peak of the direct path signal is located; : Sampling frequency, in Hz.

[0046] Signal arrival direction The direction finding and positioning module can be used to control the beam scanning angle of the receiving antenna. A multi-channel receiving array is usually used to adjust the phase deviation of each antenna to form directional sensitivity, thereby measuring the direction of arrival of the signal.

[0047] Signal Strength is calculated by the amplitude of the direct path signal, which can be obtained by the square sum operation: ; in, : Indicates the strength of the signal in power (such as dBm), which is calculated by the amplitude of the direct path signal; : Indicates at time The direct path signal of : Indicates the total number of time points or sampling points of the signal sampling, which is usually related to the time domain resolution of the signal; : Indicates the direct path signal at time The square of the amplitude is usually used to represent the energy or strength of the signal.

[0048] In some embodiments, the state model can be expanded into a multi-dimensional form to simultaneously describe the states of the direct path signal and the reflected path signal. The expanded state vector is: ; in, :Indicates the The iteration time The signal state vector contains the time, direction and strength information of the signal; :Indicates the The iteration time The signal arrival time; :Indicates the The iteration time The direction of arrival of the signal; :Indicates the The iteration time Signal strength; Indicates the path number, Corresponding direct path signal Corresponding to the reflected path signal.

[0049] Step S3 aims to track and optimize the signal state through a dynamic filtering algorithm, thereby adjusting the signal parameters in real time so that it can adapt to the complex wireless communication environment. On this basis, the information entropy analysis of the signal direction distribution is performed to determine the optimal direction, providing reliable input for subsequent steps.

[0050] The purpose of step S3 is to track and optimize the signal state in real time through a dynamic filtering algorithm, so that the signal parameters can maintain high accuracy in a dynamic environment. At the same time, by performing information entropy analysis on the optimized signal direction distribution, the optimal direction of the signal is further determined, thereby providing key input for subsequent positioning calculations.

[0051] In this embodiment, the dynamic filtering algorithm uses Kalman filtering to achieve real-time tracking and optimization of signal states. Kalman filtering is an optimal estimation method based on a linear Gaussian model, which is divided into a prediction phase and an update phase.

[0052] The signal state model is described by the state transition equation and the measurement equation. The state vector Contains signal arrival time, direction and strength.

[0053] The state transition equation describes the change of signal state over time: ; in, : State transfer matrix, used to describe the evolution law of signal state without external input; : Process noise, satisfying , is the process noise covariance matrix.

[0054] In general, the state transfer matrix Can be set to: ; When the dynamic change of the signal is small, the unit matrix form can be selected. If the direction or time of the signal has an acceleration change, it can also be expanded is a dynamic matrix.

[0055] The measurement equation describes the relationship between the state vector and the observation value: ; in, : observation vector, containing the signal time, direction and strength obtained from the measurement; : Measurement matrix, describing the observed characteristics of the state vector; : Measurement noise, satisfying , is the measurement noise covariance matrix.

[0056] In one possible implementation, the measurement matrix It can usually be expressed as: ; This form indicates that the observations correspond directly to the components of the state vector.

[0057] The Kalman filter is divided into a prediction phase and an update phase.

[0058] The state prediction formula is: ; The prediction covariance formula is: ; in, : The state prediction value at the current moment; : state prediction covariance; : The state covariance at the previous moment.

[0059] In the update phase, the Kalman gain matrix is ​​first calculated : ; Then update the status: ; Update the covariance: ; in, : Kalman gain, which indicates the contribution of the current measurement to the state estimation; : identity matrix.

[0060] Through the above process, the Kalman filter can dynamically balance between prediction and update, providing a high-confidence estimate of the signal state.

[0061] After dynamic filtering optimization, this embodiment further determines the optimal direction of the signal through information entropy analysis.

[0062] Assumptions Indicates direction The signal strength on , the normalized probability distribution is: ; in, :direction The normalized probability of : The sum of directional signal strengths; Entropy of directional distribution The calculation formula is: ; Entropy Describes the concentration characteristics of directional signals. Generally speaking, the smaller the entropy value, the higher the concentration of the signal in a certain direction.

[0063] According to the principle of minimum entropy value, the optimal direction Defined as: ; in, Indicates the direction The entropy value on Minimize.

[0064] In order to further improve the robustness of direction determination, dynamic weight correction can be introduced. The direction correction weight formula is: ; in, :direction The correction weight of :direction Angle : The current optimal direction; : Attenuation coefficient, used to control the correction range.

[0065] The corrected directional intensity distribution is: ; in, : Corrected direction It is the signal strength after combining the modified weights and is used for further direction determination or distribution optimization; : Original direction It indicates the signal strength without dynamic weight correction; :direction The weight is based on the direction The degree of deviation is dynamically calculated and used to adjust the relative contribution of signal strength.

[0066] Step S4 aims to calculate the distance between the wireless communication terminal and the base station by combining the optimal direction and propagation delay information of the signal, and further determine the three-dimensional coordinates of the terminal. This step is based on the precise time delay and direction angle information of the direct path signal. By analyzing the horizontal and vertical angles and combining the distance data, the terminal can be accurately positioned in three-dimensional space. To adapt to complex environments, this step also combines the strategies of dynamic propagation speed correction and antenna array optimization.

[0067] In this embodiment, the positioning calculation is divided into two core parts: distance calculation and three-dimensional angle analysis.

[0068] Regarding distance calculation, first, the distance between the terminal and the base station is calculated based on the propagation delay of the signal. is the time required for the direct path signal to be transmitted from the terminal to the base station, and this value is obtained from the direct path signal data extracted in steps S2 and S3.

[0069] The distance calculation formula is: ; in, : The distance between the terminal and the base station, in meters; : signal propagation speed, in m / s; : Direct path signal propagation delay, in seconds.

[0070] In general, the propagation delay The arrival time of the signal and launch time The difference calculation is: ; in, : The signal arrival time recorded by the receiving end, usually accurately measured by the receiving module of the base station; : The signal transmission time of the sender, provided by the terminal clock.

[0071] To improve accuracy, the signal propagation speed It can be corrected according to environmental conditions. For example, changes in air temperature and humidity have a significant impact on the propagation speed. The correction formula is: ; in, : The signal propagation speed under standard air conditions, which is about 3×10 8  m / s; : The absolute temperature of the current environment, in Kelvin (K), for example 298K (25°C); : Standard temperature, usually 293K (20℃).

[0072] After the distance calculation is completed, this embodiment further analyzes the three-dimensional position of the terminal in combination with the horizontal angle and vertical angle information.

[0073] Optimal horizontal angle It is obtained through the information entropy analysis of step S3. This angle directly reflects the arrival direction of the signal on the horizontal plane.

[0074] Angle from vertical The vertical antenna array of the base station is used for analysis. Specifically, the base station receiving module calculates the vertical angle through the phase difference between adjacent antennas in the array. The calculation formula for the vertical angle is as follows: ; in, : vertical signal incident angle, in radians; : Signal wavelength, in meters; : The phase difference of the received signals between adjacent antennas, in radians; : Array spacing between adjacent antennas, in meters.

[0075] wavelength By signal frequency The calculation results in: ; in, : Signal frequency, in Hertz (Hz). For example, the wavelength corresponding to a 5GHz signal is .

[0076] In some embodiments, to further improve the resolution accuracy of the vertical angle, the number of array antennas may be increased to obtain a more stable result through more phase difference calculations.

[0077] Combined with the distance calculated above , horizontal angle and vertical angle , you can get the three-dimensional coordinates of the terminal The calculation formula for the three-dimensional coordinates is: ; ; ; in, , , : The position coordinates of the terminal in three-dimensional space, in meters; : The distance between the terminal and the base station; : vertical angle; : Horizontal angle.

[0078] Through the above formula, the delay and direction information of the signal can be converted into the three-dimensional coordinates of the terminal, thereby achieving accurate positioning of the terminal in space.

[0079] In complex environments, such as when the multipath effect is significant indoors, multipath information can be introduced to assist in positioning. Specifically, for secondary reflection paths with significant signal strength, corrections can be made based on their propagation delay and direction information. For example, by comparing the time delay difference between the primary path and the secondary path, the distance calculation result can be further optimized.

[0080] In dynamic scenes, an iterative optimization method can be used to gradually correct the calculation of the three-dimensional coordinates. Specifically, the weights of the angle and distance parameters can be dynamically adjusted so that the calculation results gradually approach the actual position.

[0081] In addition, in multi-layer scenes such as high-rise buildings, vertical resolution can be enhanced by combining the geometric information of the building. For example, the overlapping characteristics of multi-layer signals can be used to further improve the accuracy of vertical resolution.

[0082] Embodiment 2: Please see attached Figure 2 , a wireless communication terminal precise positioning device, comprising: A signal receiving module, used to receive a composite signal sent by a wireless communication terminal and pre-process the signal, wherein the pre-processing includes filtering and frequency domain analysis of the signal to reduce noise and interference; A signal decomposition module is used to perform sparse decomposition on the preprocessed composite signal and extract the direct path signal; The signal optimization module is used to build a signal state model based on the extracted direct path signal and optimize the time, direction and strength of the signal through a dynamic filtering algorithm; A direction determination module is used to perform information entropy analysis on the optimized signal direction distribution and determine the optimal direction; A dynamic correction module is used to update the direction weight according to the deviation between the path direction and the optimal direction, and to correct the direction distribution; A positioning calculation module, used to calculate the positioning result of the terminal by combining the optimal direction, propagation delay and direction information after dynamic correction; A vertical component analysis module is used to determine the vertical angle by combining the received vertical direction signal with the signal analysis of the antenna array to support three-dimensional positioning calculation; The signal decomposition module includes a sparse representation unit and a sparse optimization unit. The sparse representation unit is used to represent the composite signal as a linear combination of sparse basis matrices. The sparse optimization unit extracts the direct path signal by optimizing the absolute value sum of the sparse coefficients. The signal optimization module includes a state modeling unit and a filtering unit. The state modeling unit is used to build a signal state model. The filtering unit optimizes the time, direction and strength information of the signal through a dynamic filtering algorithm of prediction and correction. The direction determination module calculates the information entropy of the normalized probability distribution of the direction signal, and the direction with the minimum entropy corresponds to the optimal direction.

[0083] Specifically, in this embodiment, the modules cooperate through data streams and control signals to complete the efficient operation of the entire system. The signal receiving module is responsible for receiving the composite signal sent by the wireless communication terminal, and preprocessing it, such as filtering and frequency domain analysis, to reduce noise and interference, and passing the preprocessed signal to the signal decomposition module. The signal decomposition module uses sparse decomposition technology to extract the direct path signal, eliminate the interference of the reflected path signal, reconstruct the direct path signal, and then hand over the time, direction and strength parameters of the signal to the signal optimization module. Based on the Kalman filter algorithm, the signal optimization module combines the measurement equation and the state transfer equation to dynamically track and optimize the signal state, correct the time delay, direction distribution and strength information of the signal in real time, and pass the optimized signal direction distribution to the direction determination module. The direction determination module further optimizes the signal direction through information entropy analysis, determines the optimal direction, and sends the optimal direction and its corresponding signal strength result to the dynamic correction module. The dynamic correction module updates the direction weight and corrects the signal distribution according to the deviation between the optimal direction and the path direction, and feeds the correction result back to the direction determination module to complete the final confirmation of the direction. Subsequently, the positioning calculation module combines the optimal direction, signal propagation delay and direction correction results to calculate the distance between the terminal and the base station, and analyzes the vertical component of the signal of the antenna array through the vertical component analysis module to calculate the vertical angle. Finally, the positioning calculation module derives the position coordinates of the terminal in three-dimensional space based on the distance, horizontal angle and vertical angle information to achieve high-precision positioning. During operation, the modules of this device achieve efficient collaboration through the linkage operation of data flow and algorithm, and have good adaptability and robustness.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for using a wireless communication terminal precise positioning device, characterized in that: The following steps are involved: S1. Receive a composite signal sent by a wireless communication terminal through a signal receiving module, and pre-process the received signal, including filtering and frequency domain analysis to reduce noise and interference; S2. Decomposing the preprocessed composite signal, extracting the direct path signal through sparse signal decomposition, and constructing a signal state model based on the extracted direct path signal; S3. Track and optimize the signal state through a dynamic filtering algorithm, obtain the optimized time, direction and strength information, and perform information entropy analysis on the optimized signal direction distribution to determine the optimal direction; S4. Calculate the distance between the terminal and the base station based on the optimal direction and signal propagation delay, and obtain a three-dimensional positioning result of the terminal based on the distance and direction information.

2. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S1, the pre-processing step includes: (1) Bandpass filtering the composite signal to eliminate high-frequency noise and environmental interference; (2) Perform fast Fourier transform on the filtered signal to obtain the frequency domain characteristics of the signal for subsequent signal decomposition.

3. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S2, signal decomposition is achieved by a sparse optimization method, which specifically includes: (1) Sparsely represent the signal based on the sparse basis matrix and decompose the signal into a linear combination of sparse coefficients; (2) Minimize the absolute value sum of the sparse coefficients and extract the high-amplitude part of the sparse coefficients corresponding to the direct path signal; (3) Reconstruct the direct path signal based on the extraction results.

4. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S2, the signal state model includes time state, direction state and signal strength state, and the state change satisfies the following state transition equation and measurement equation: (1) State transfer equation: ； (2) Measurement equation: ； in, is the signal state vector, is the measurement vector, is the state transfer matrix, is the measurement matrix, and are process noise and measurement noise respectively, which satisfy Gaussian distribution.

5. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S3, the dynamic filtering algorithm includes: (1) Predict signal states and state covariance; (2) Update the signal state and covariance matrix based on the received measurement information; (3) Obtain optimized time, direction, and intensity information through the corrected signal state.

6. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S3, the step of performing information entropy analysis on the signal direction distribution includes: (1) Normalize the signal direction distribution and calculate the probability distribution of the normalized signal; (2) Calculate the entropy value of the direction distribution based on the normalized distribution; (3) Determine the optimal direction based on the direction with the smallest directional distribution entropy value.

7. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S4, when calculating the distance between the terminal and the base station, the propagation delay is determined by the arrival time of the direct path signal, and the distance is calculated by the product of the propagation delay and the signal propagation speed, wherein the signal propagation speed is dynamically corrected based on the ambient temperature.

8. The method for using the wireless communication terminal precise positioning device according to claim 1, characterized in that: In the step S4, the three-dimensional positioning result of the terminal is obtained by: (1) The horizontal plane direction is determined by the optimal direction angle; (2) The vertical direction is analyzed by the vertical component signal based on the antenna array to calculate the vertical angle; (3) Calculate the three-dimensional coordinates by combining the horizontal direction, vertical direction and distance information.

9. A wireless communication terminal precise positioning device, a method for using a wireless communication terminal precise positioning device according to any one of claims 1 to 8, characterized in that: include: A signal receiving module, used to receive a composite signal sent by a wireless communication terminal and pre-process the signal, wherein the pre-processing includes filtering and frequency domain analysis of the signal to reduce noise and interference; A signal decomposition module is used to perform sparse decomposition on the preprocessed composite signal and extract the direct path signal; The signal optimization module is used to build a signal state model based on the extracted direct path signal and optimize the time, direction and strength of the signal through a dynamic filtering algorithm; A direction determination module is used to perform information entropy analysis on the optimized signal direction distribution and determine the optimal direction; A dynamic correction module is used to update the direction weight according to the deviation between the path direction and the optimal direction, and to correct the direction distribution; A positioning calculation module, used to calculate the positioning result of the terminal by combining the optimal direction, propagation delay and direction information after dynamic correction; The vertical component analysis module is used to determine the vertical angle by combining the received vertical direction signal with the signal analysis of the antenna array to support three-dimensional positioning calculation.

10. A wireless communication terminal precise positioning device according to claim 9, characterized in that: The signal decomposition module includes a sparse representation unit and a sparse optimization unit, the sparse representation unit is used to represent the composite signal as a linear combination of sparse basis matrices, and the sparse optimization unit extracts the direct path signal by optimizing the absolute value sum of the sparse coefficients; The signal optimization module includes a state modeling unit and a filtering unit, the state modeling unit is used to construct a signal state model, and the filtering unit optimizes the time, direction and strength information of the signal through a dynamic filtering algorithm of prediction and correction; The direction determination module calculates information entropy on the normalized probability distribution of the direction signal, and the direction with the minimum entropy corresponds to the optimal direction.

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