Positioning and speed measuring method and system based on MIMO-OFDM communication architecture

Abstract

The invention provides a positioning and speed measuring method and system based on an MIMO-OFDM communication architecture, and the method comprises the steps: enabling each base station to receive a communication signal of a target device, obtaining a plurality of received signals, and enabling each base station to carry out the communication based on the MIMO-OFDM communication architecture; performing time-frequency synchronization and channel estimation on the received receiving signals through each base station to obtain a time-frequency compensation amount and a channel parameter estimation value corresponding to each receiving signal; and inputting each channel parameter estimation value, each time-frequency compensation amount and a first positioning result into a preset positioning model, so that the positioning model performs a plurality of times of weighted iterative updating on the first positioning result based on an iterative optimization algorithm to generate a second positioning result of the target equipment, the first positioning result is obtained through calculation according to preset TDoA, AoA and FDoA positioning methods, so that the accuracy of positioning and speed measurement of the target equipment is improved.

Description

Technical Field

[0001] The present application relates to the field of communication positioning technology, and in particular to a positioning and speed measurement method and system based on a MIMO-OFDM communication architecture. Background Art

[0002] With the rapid growth of positioning needs and the increasing diversification of application scenarios, in some specific scenarios, such as indoor environments or dense urban areas, the Global Positioning System (GPS) is difficult to provide reliable high-precision positioning services because the signal is blocked by buildings. In this context, using the existing cellular mobile communication network to achieve the positioning of the target device has become a feasible alternative: since the received signal is closely related to the relative position and speed of the transmitting and receiving ends, under the premise of knowing the location of the base station, the information related to the location of the target device can be extracted from the received communication signal, thereby achieving high-precision positioning, that is, communication and positioning integration. Communication and positioning integration technology has been widely used in mobile communications. As early as the 4G standard protocol formulated by 3GPP (The Third Generation Partnership Project), the reference signal (Positioning Reference Signal, PRS) for positioning was introduced. However, due to the 20MHz bandwidth and severe multipath effect of the 4G system, its positioning accuracy is only 50 meters at most, which is far lower than the positioning capability of the GPS system. In contrast, the 5G communication system, with the advantages of higher radio frequency, larger bandwidth and large-scale antenna array, not only significantly improves the positioning accuracy, but also brings the ability to measure speed to the communication system. In the 5G system, positioning accuracy can be improved to 0.2 meters, providing strong technical support for high-precision positioning and speed measurement of mobile devices.

[0003] Multiple-Input-Multiple-Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) technology (abbreviated as MIMO-OFDM technology) occupies an important position in the current 4G and 5G communication systems, in which channel estimation is an indispensable step. In MIMO-OFDM systems, since the phase difference of the channel complex gain between adjacent subcarriers, adjacent frames and adjacent antennas is proportional to the transmission delay, Doppler frequency shift and signal angle respectively, by measuring these phase differences from the channel estimation results, the location information and speed parameters of the target device can be accurately extracted, thereby achieving high-precision positioning and speed measurement. Under the ideal condition of perfect time-frequency synchronization between the transmitter and the receiver, parameters such as time-of-arrival (ToA), Doppler frequency shift, transmission angle (AoD) and arrival angle can be directly extracted from the channel. However, in actual communication scenarios, time-frequency synchronization errors are inevitable. This residual time-frequency synchronization error will significantly affect the measurement accuracy of ToA and Doppler frequency shift, thereby reducing the accuracy of positioning and speed measurement. Summary of the invention

[0004] In response to the above technical problems, the present application provides a positioning and speed measurement method and system based on the MIMO-OFDM communication architecture, which improves the accuracy of positioning and speed measurement of the target device under the premise that there is a time and frequency synchronization error between the target device and each base station.

[0005] In a first aspect, an embodiment of the present application provides a positioning and speed measurement method based on a MIMO-OFDM communication architecture, including:

[0006] Receiving a communication signal of a target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on a MIMO-OFDM communication architecture;

[0007] By each of the base stations performing time-frequency synchronization on the received signals, a time-frequency compensation amount corresponding to each of the received signals is obtained;

[0008] By each of the base stations performing channel estimation on the received signals, a channel parameter estimation value corresponding to each of the received signals is obtained;

[0009] Inputting each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result of the target device into a preset positioning model, so that the positioning model performs a weighted iterative update on the first positioning result several times based on an iterative optimization algorithm to generate a second positioning result of the target device, thereby determining the position and speed of the target device;

[0010] The first positioning result is calculated based on each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​using preset TDoA, AoA and FDoA positioning methods.

[0011] The embodiment of the present application provides a positioning and speed measurement method based on a MIMO-OFDM communication architecture. Based on the characteristics of the MIMO-OFDM communication architecture, multiple base stations are used to receive the communication signal of the target device, and the time-frequency compensation amount and channel parameter estimation value corresponding to the received signal are extracted through time-frequency synchronization and channel estimation. Considering that there are inevitable time-frequency synchronization errors between the target device and each base station, these time-frequency synchronization errors will reduce the accuracy of positioning and speed measurement to a certain extent. In response to this problem, the embodiment of the present application is based on the assumption that high-precision time-frequency synchronization can be achieved between base stations. The residual time-frequency synchronization errors between different base stations and the target device can be regarded as consistent, and the residual time-frequency synchronization error has a calculation relationship with the channel parameters, time-frequency compensation amount, clock deviation, and local crystal oscillator deviation. Therefore, the embodiment of the present application integrates TDoA, AoA and FDoA positioning methods, and uses the positioning model to iteratively optimize the positioning results. While calculating the target position and speed, the clock deviation and local crystal oscillator deviation are also solved, which improves the accuracy of time-frequency synchronization, thereby improving the positioning accuracy of the position and speed of the target device, especially in the scenario where there is a time-frequency synchronization error in the base station, which can effectively compensate for the influence of the synchronization deviation on the positioning result.

[0012] In a possible implementation manner, performing channel estimation on the received signals by each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals includes:

[0013] By each of the base stations performing channel estimation on the received signal, respective corresponding channel estimation results are obtained;

[0014] Inputting each of the channel estimation results into a preset channel parameter estimation model, so that the channel parameter estimation model is solved by a multi-dimensional frequency estimation algorithm based on discrete Fourier transform to obtain a channel parameter estimation value corresponding to each of the channel estimation results;

[0015] The channel parameter estimation model is a maximum likelihood estimation model constructed based on the channel estimation results and channel parameters.

[0016] An embodiment of the present application provides a channel estimation method, which adopts a channel parameter estimation model based on maximum likelihood estimation in the channel estimation process, and combines a multi-dimensional frequency estimation algorithm of discrete Fourier transform to estimate the channel parameters of each received signal, thereby improving the accuracy and robustness of the channel parameter estimation and reducing the complexity of the channel parameter estimation, providing a more reliable data basis for subsequent positioning calculations.

[0017] In a possible implementation, the calculating and obtaining a first positioning result of the target device using preset TDoA, AoA, and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0018] According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device;

[0019] According to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position, using the FDoA positioning method to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device;

[0020] The first clock deviation, the first target device position, the first local crystal oscillator deviation and the first target device speed are constructed as the first positioning result.

[0021] The embodiment of the present application provides a positioning method combining TDoA, AoA and FDoA, which realizes the coordinated optimization of multi-source positioning parameters (time, angle, frequency) by calculating the clock deviation, position and local crystal oscillator deviation, and speed step by step, thereby improving the overall positioning efficiency and positioning accuracy, and providing a data basis for the subsequent iterative update of the positioning results.

[0022] Further, the step of calculating and obtaining the first clock deviation and the first target device position of the target device using the TDoA and AoA positioning method according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0023] According to the TDoA positioning method, a TDoA equation is constructed regarding the channel complex gain phase change rate with subcarrier in the channel parameters, the time synchronization compensation amount in the time-frequency compensation amount, the clock deviation and the position of the target device;

[0024] According to the AoA positioning method, an AoA equation is constructed regarding the channel complex gain phase in the channel parameters with the antenna change rate, the antenna array direction vector and the target device position;

[0025] Combining the TDoA equation with the AoA equation, constructing a TDoA-AoA equation about the rate of change of the channel complex gain phase with the subcarrier, the rate of change of the channel complex gain phase with the antenna, the clock deviation, the time synchronization compensation amount, the antenna array direction vector, the target device position and the clock deviation;

[0026] The time synchronization compensation amount in each of the time-frequency compensation amounts, the channel complex gain phase estimation value with subcarrier change rate and the channel complex gain phase estimation value with antenna change rate in each of the channel parameter estimation values ​​are substituted into the TDoA-AoA equation, and the first clock deviation and the first target device position are calculated based on the least squares method.

[0027] In the embodiment of the present application, specific steps for calculating the first clock deviation and the first target device position are further provided. By constructing the TDoA equation, the calculation relationship between the channel complex gain phase with the subcarrier change rate, the time synchronization compensation, the clock deviation and the target device position is established; by constructing the AoA equation, the calculation relationship between the channel complex gain phase with the antenna change rate, the antenna array direction vector and the target device position is established. Then, the TDoA equation and the AoA equation are further combined to construct a comprehensive equation, and the corresponding channel parameter estimation values ​​are substituted and solved based on the least squares method, which solves the interference problem of time-frequency synchronization error and clock deviation on position estimation, improves the initial accuracy of the first positioning result, and provides a more reliable data basis for subsequent positioning calculations.

[0028] Further, the method of calculating and obtaining a first local crystal oscillator deviation and a first target device speed of the target device using the FDoA positioning method according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position includes:

[0029] According to the FDoA positioning method, an FDoA equation is constructed regarding the channel complex gain phase change rate with time slot in the channel parameters, the target device position, the target device speed, the frequency synchronization compensation amount in the time-frequency compensation amount, and the local crystal oscillator deviation;

[0030] Substitute the frequency synchronization compensation amount in each of the time-frequency compensation amounts, the estimated value of the channel complex gain phase change rate with time slot in each of the channel parameter estimation values, and the position of the first target device into the FDoA equation, and calculate the first local crystal oscillator deviation and the first target device speed based on the least squares method.

[0031] In an embodiment of the present application, specific steps for calculating the first local crystal oscillator deviation and the first target device speed are further provided. By constructing the FDoA equation, a calculation relationship is established between the channel complex gain phase change rate with time slot, the target device position, the target device speed, the frequency synchronization compensation amount and the local crystal oscillator deviation. Then, the corresponding channel parameter estimation values ​​are substituted and solved based on the least squares method, thereby achieving high-sensitivity measurement of the target device speed and the local crystal oscillator deviation. This is particularly suitable for high-speed mobile scenarios, enhances the system's ability to track dynamic targets, and provides a more reliable data basis for subsequent positioning calculations.

[0032] In one possible implementation, the positioning model is a weighted least squares estimation model constructed based on each of the channel parameter estimation values, a channel parameter calculation function and a weight matrix, wherein the weight matrix is ​​constructed by the Fisher information matrix of the channel parameter vector and the first positioning result, and the channel parameter calculation function is a function that calculates the channel parameters based on each of the time-frequency compensation amounts, clock deviation, local crystal oscillator deviation, target device position and target device speed.

[0033] In a possible implementation manner, constructing the weight matrix using the Fisher information matrix of the channel parameter vector and the first positioning result includes:

[0034] Construct a first channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, the channel complex gain phase change rate with subcarrier, real channel gain and random phase noise in the channel parameters;

[0035] constructing a log-likelihood function of each of the received signals according to the first channel parameter vector, and then constructing a corresponding Fisher information matrix about the first channel parameter vector according to each of the log-likelihood functions;

[0036] Determine a first Cramer-Rao lower bound function of the first channel parameter vector according to each of the Fisher information matrices;

[0037] Constructing a second channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, and the channel complex gain phase change rate with subcarrier in the channel parameters;

[0038] Determine a second Cramer-Rao lower bound function of the second channel parameter vector according to the first Cramer-Rao lower bound function;

[0039] Substitute the first positioning result into the second Cramer-Rao lower bound function to calculate and obtain the weight matrix.

[0040] The embodiment of the present application provides a method for constructing a weight matrix. In the MIMO-OFDM communication architecture, the signal received by each base station is related to five types of channel parameters, namely, the rate of change of the complex gain phase of the channel with time slots, the rate of change of the complex gain phase of the channel with antennas, the rate of change of the complex gain phase of the channel with subcarriers, real channel gain, and random phase noise. Therefore, the embodiment of the present application constructs these five types of channel parameters as a first channel parameter vector, and constructs each Fisher information matrix corresponding to each received signal according to the first channel parameter vector, and then determines the first Cramer Rao lower bound function of the first channel parameter vector. Since only the residual synchronization error and the signal arrival angle contain effective information related to the position and speed of the target device in the channel parameters, and the residual synchronization error and the signal arrival angle are related to the rate of change of the complex gain phase of the channel with time slots, the rate of change of the complex gain phase of the channel with antennas, and the rate of change of the complex gain phase of the channel with subcarriers, the embodiment of the present application constructs the corresponding second channel parameter vector according to the required channel parameters, and determines the second Cramer Rao lower bound function of the second channel parameter vector based on the first Cramer Rao lower bound function of the first parameter vector, and substitutes the first positioning result to realize the construction of the weight matrix. The embodiment of the present application constructs a weight matrix based on the Fisher information matrix and the Cramer-Rao lower bound function, optimizes the weight allocation strategy of the weighted least squares model, reduces the negative impact of channel parameter noise on the positioning results, improves the model's anti-interference ability, and improves the accuracy of positioning and speed measurement of the target device.

[0041] In a possible implementation, the iterative optimization algorithm is a Gauss-Newton iteration method, and the positioning model performs a number of weighted iterative updates on the first positioning result based on the iterative optimization algorithm to generate a second positioning result of the target device, including:

[0042] In each weighted iterative update process, according to the current positioning result and each of the time-frequency compensation amounts, the channel parameter calculation function is used to calculate and obtain each current channel parameter estimation value;

[0043] Calculating channel parameter difference values ​​between each of the channel parameter estimation values ​​and each of the corresponding current channel parameter estimation values;

[0044] Constructing and obtaining a current weight matrix according to the current positioning result;

[0045] Calculate and obtain a positioning result difference value according to each of the channel parameter difference values, each of the current channel parameter estimation values ​​and the current weight matrix;

[0046] The current positioning result is updated according to the positioning result difference value to obtain an updated positioning result, and the updated positioning result is made to enter the next weighted iterative update process;

[0047] When the number of weighted iterative updates reaches a preset value, the iteration is stopped, and the updated positioning result obtained by the last weighted iterative update is used as the second positioning result.

[0048] The embodiment of the present application provides a method for iteratively updating the positioning result, in which the current channel parameter estimation value, the channel parameter difference value, the current weight matrix and the positioning result difference value are calculated in turn during each iteration, and finally the updated positioning result is obtained according to the positioning result difference value calculation, and the next iteration is entered. The embodiment of the present application adopts the Gauss-Newton iteration method for weighted iterative update, which can quickly converge to a more accurate positioning result, and at the same time, by dynamically adjusting the weight matrix, effectively suppress error accumulation, and improve the efficiency and stability of iterative optimization.

[0049] In the second aspect, accordingly, an embodiment of the present application provides a positioning and speed measurement system based on a MIMO-OFDM communication architecture, including a signal receiving module, a time-frequency synchronization module, a channel estimation module, and a positioning module;

[0050] The signal receiving module is used to receive the communication signal of the target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on the MIMO-OFDM communication architecture;

[0051] The time-frequency synchronization module is used to perform time-frequency synchronization on the received signals through each of the base stations, so as to obtain the time-frequency compensation amount corresponding to each of the received signals;

[0052] The channel estimation module is used to perform channel estimation on the received signals through each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals;

[0053] The positioning module is used to input each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result of the target device into a preset positioning model, so that the positioning model performs several weighted iterative updates on the first positioning result based on an iterative optimization algorithm, generates a second positioning result of the target device, and further determines the position and speed of the target device.

[0054] The first positioning result is calculated based on each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​using preset TDoA, AoA and FDoA positioning methods.

[0055] In a possible implementation, the calculating and obtaining a first positioning result of the target device using preset TDoA, AoA, and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0056] According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device;

[0057] According to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position, using the FDoA positioning method to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device;

[0058] The first clock deviation, the first target device position, the first local crystal oscillator deviation and the first target device speed are constructed as the first positioning result. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 : A flow chart of a positioning and speed measurement method based on a MIMO-OFDM communication architecture provided in an embodiment of the present application.

[0060] Figure 2 : is a schematic diagram of the geometric relationship between the base station and the target device in an embodiment of the present application.

[0061] Figure 3 : A structural diagram of a positioning and speed measurement system based on a MIMO-OFDM communication architecture provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] It should be noted that the step numbers in the text are only for the convenience of explanation of the specific embodiments and do not serve to limit the order in which the steps are executed. In the description of this application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0064] Throughout the specification, the symbols of this application are annotated as follows:

[0065] Bold lowercase letters (e.g., x) denote vectors, bold uppercase letters (e.g., x) denote matrices, and non-bold letters (e.g., x) denote scalars. X[i,:] denotes the i-th row of the matrix X, X[:,j] denotes the j-th column of the matrix X, and X[i1 :i 2 , j 1 :j 2 ] represents the i-th 1 Go to the i-th row 2 row, j 1 Column to j 2 All elements of the column. X H Expressed as the conjugate transpose of matrix X, X T Represented as the transpose of matrix X, X * It is represented as the conjugate of matrix X. DIAG{X} represents the vector composed of the diagonal elements of matrix X, and diag{x} represents the diagonal matrix constructed by the elements of vector x. |·| represents the modulus value, ||·|| 2 Indicates l 2 -norm. I L represents the L-dimensional identity matrix. and represent the domain of complex numbers and the domain of real numbers respectively.

[0066] Embodiment 1:

[0067] like Figure 1 As shown, embodiment 1 provides a positioning and speed measurement method based on MIMO-OFDM communication architecture, including steps S1 to S5:

[0068] Step S1, receiving a communication signal of a target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on a MIMO-OFDM communication architecture;

[0069] Step S2: performing time-frequency synchronization on the received signals by each of the base stations to obtain a time-frequency compensation amount corresponding to each of the received signals;

[0070] Step S3, each of the base stations performs channel parameter estimation on the received signals, to obtain channel parameter estimation values ​​corresponding to each of the received signals;

[0071] Step S4: according to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using preset TDoA, AoA and FDoA positioning methods to calculate and obtain a first positioning result of the target device, wherein the first positioning result includes a first clock deviation, a first local crystal oscillator deviation, a first target device position and a first target device speed;

[0072] Step S5, inputting each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result into a preset positioning model, so that the positioning model performs a weighted iterative update on the first positioning result several times based on an iterative optimization algorithm, generates a second positioning result of the target device, and further determines the position and speed of the target device;

[0073] Among them, the positioning model is a weighted least squares estimation model constructed based on each of the channel parameter estimation values, the channel parameter calculation function and the weight matrix, the weight matrix is ​​constructed by the Fisher information matrix of the channel parameter vector and the first positioning result, and the channel parameter calculation function is a function that calculates the channel parameters based on each of the time-frequency compensation amounts, clock deviation, local crystal oscillator deviation, target device position and target device speed.

[0074] The embodiment of the present application provides a positioning and speed measurement method based on a MIMO-OFDM communication architecture. Based on the characteristics of the MIMO-OFDM communication architecture, multiple base stations are used to receive the communication signal of the target device, and the time-frequency compensation amount and channel parameter estimation value corresponding to the received signal are extracted through time-frequency synchronization and channel estimation. Considering that there are inevitable time-frequency synchronization errors between each base station, these time-frequency synchronization errors will reduce the accuracy of positioning and speed measurement to a certain extent. In response to this problem, the embodiment of the present application is based on the assumption that high-precision time-frequency synchronization can be achieved between base stations. The residual time-frequency synchronization errors between different base stations and target devices can be regarded as consistent, and the residual time-frequency synchronization error has a calculation relationship with the channel parameters, time-frequency compensation amount, clock deviation, and local crystal oscillator deviation. Therefore, the embodiment of the present application integrates TDoA, AoA and FDoA positioning methods, and uses a weighted least squares estimation model for iterative optimization. While calculating the target position and speed, the clock deviation and local crystal oscillator deviation are also solved, which improves the accuracy of time-frequency synchronization, thereby improving the positioning accuracy of the position and speed of the target device, especially in the scenario where there is a time-frequency synchronization error in the base station, which can effectively compensate for the influence of the synchronization deviation on the positioning result.

[0075] In a preferred embodiment, in step S1, the geometric relationship between the base station and the target device is as follows: Figure 2 As shown, p represents the position of the target mobile device; v represents the moving speed of the target mobile device; p q represents the location of the qth base station; u q represents the antenna direction vector of the qth base station, which is used to indicate the orientation of the base station antenna; θ q represents the antenna direction vector of the base station (u q ) and the angle between the relative direction between the base station and the target device; d q represents the distance from the target device to the qth base station; K represents the total number of antennas in the antenna array at the base station.

[0076] Consider the uplink of a MIMO-OFDM cellular mobile communication system, which consists of Q perfectly synchronized base stations and a single-antenna target mobile device, with a bandwidth of B Hz and a time slot length of T = 1 / B seconds. Each base station is equipped with a uniform linear antenna array (ULA) consisting of M antennas, and the distance between adjacent antennas is half of the carrier wavelength. The three-dimensional position coordinates of the qth base station are represented by p q , whose antenna array direction vector is u q , the real-time three-dimensional position coordinates and velocity of the target device are p and v respectively. q Indicates u q and the angle between the unit direction vector from the target device to the base station, so its cosine value can be expressed as

[0077]

[0078] where d q =||p q -p|| 2 is the relative distance between the qth base station and the target device. In addition, the transmission delay and Doppler frequency shift between the qth base station and the target device can be expressed as

[0079]

[0080] It is worth noting that each base station and the target device have the same clock deviation and local crystal oscillator deviation, but since the communication time and frequency synchronization is completed independently on each base station, the residual synchronization offsets (RSOs) of each base station after synchronization are not the same. The RSOs on the qth base station are

[0081] δ t，q =∈ t +τ q -Δt q , δ f，q =∈ f +ν q -Δf q (3)

[0082] Where Δt q and Δf q is the compensation amount after time-frequency synchronization. Since the system has a large-scale antenna array and a large bandwidth, single path and multipath can be considered to be completely distinguished. Therefore, at the nth time slot and the kth subcarrier, the channel complex gain between the qth base station and the target device is

[0083]

[0084] Among them, α q is the real channel gain, φ q is the random phase noise, a(ω)=[1,e -jω ,…,e -j(M-1)ω ] T is the steering vector of the ULA antenna array, and ω a，q ,ω t，q and w f，q are the rates of change of the phase of the channel complex gain with antenna, subcarrier and time slot respectively.

[0085] ω a，q =πcosθ q ,ω t，q =2πBδ t，q ,ω f，q =2πTδ f，q (5)

[0086] Assume that the target device sends a symbol x in the nth time slot and the kth subcarrier n，k , the received signal of the qth base station is

[0087] y q，n，k =h q，n，k x n，k +w q，n，k

[0088] where w q，n，k Represents additive white noise, in which each element has a mean of 0 and a variance of σ 2 Circularly Symmetric Complex Gaussian (CSCG) is a circularly symmetric complex Gaussian random variable.

[0089] In a possible implementation manner, in step S3, performing channel estimation on the received signals by each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals includes:

[0090] By each of the base stations performing channel estimation on the received signal, respective corresponding channel estimation results are obtained;

[0091] Inputting each of the channel estimation results into a preset channel parameter estimation model, so that the channel parameter estimation model is solved by a multi-dimensional frequency estimation algorithm based on discrete Fourier transform to obtain a channel parameter estimation value corresponding to each of the channel estimation results;

[0092] The channel parameter estimation model is a maximum likelihood estimation model constructed based on the channel estimation results and channel parameters.

[0093] An embodiment of the present application provides a channel parameter estimation method. In the channel parameter estimation process, a channel parameter estimation model based on maximum likelihood estimation is adopted, and a multi-dimensional frequency estimation algorithm based on discrete Fourier transform is combined to perform channel parameter estimation on each received signal, thereby improving the accuracy and robustness of the channel parameter estimation and providing a more reliable data basis for subsequent positioning calculations.

[0094] In a preferred embodiment, in step S3, at each base station, after performing time-frequency synchronization on the received signal, channel parameter estimation is performed, and the channel parameter estimation processes at different base stations are independent of each other. Assume that at the nth time slot and the kth subcarrier, the channel estimation result at the qth base station is Construct the maximum likelihood estimation problem of channel parameters:

[0095]

[0096] in This maximum likelihood estimation problem can be equivalent to the following optimization problem:

[0097]

[0098] in for The complex conjugate of . Formula (7) is equivalent to searching for The maximum value of the Fourier transform amplitude. Since the discrete Fourier transform DFT is the sampling of the discrete time Fourier transform (Discrete Time Fourier Transform, DTFT), this optimization problem can be obtained by using the multidimensional frequency estimation algorithm based on DFT. f，q ,ω t，q and ω a，q The approximate solution of and The solution steps are shown in Algorithm 1, which is divided into four steps. The specific steps are:

[0099] The first step is to use the channel estimation result at the qth base station The three-dimensional matrix is ​​expressed as and right Perform discrete Fourier transform and express the result as

[0100] Step 2: Search The peak value of the modulus value, and the seven sub-peaks adjacent to this peak value, the corresponding indexes are represented as (i f，q +o f，q ,i t，q +o t，q ,ia，q +o a，q ), where i f，q ∈{0, 1, ..., N-1}, i t，q ∈{0, 1, …, K-1}, i a，q ∈{0, 1, ..., M-1}, o f，q ∈{0, 1},o t，q ∈{0, 1},o a，q ∈{0, 1}. Since DFT is a sampling of DTFT, there must be β f，q ∈[0,1),β t，q ∈[0,1),β a，q ∈[0, 1] such that

[0101]

[0102] These eight peaks are represented as

[0103]

[0104] In the third step, β can be obtained from these eight peaks. f，q , β t，q and β a，q To estimate β f，q For example:

[0105]

[0106] Similarly, β can be obtained in the same way. t，q and β a，q The estimated values ​​are and

[0107] The fourth step is to obtain ω according to formula (8): f，q ,ω t，q and ω a，q The estimated results

[0108]

[0109]

[0110] In a possible implementation manner, in step S4, the calculating and obtaining a first positioning result of the target device using preset TDoA, AoA, and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0111] According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device;

[0112] The FDoA positioning method is used to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position.

[0113] The embodiment of the present application provides a positioning method combining TDoA, AoA and FDoA, which realizes the coordinated optimization of multi-source positioning parameters (time, angle, frequency) by calculating the clock deviation, position and local crystal oscillator deviation, and speed step by step, thereby improving the overall positioning efficiency and positioning accuracy, and providing a data basis for the subsequent iterative update of the positioning results.

[0114] Further, the step of calculating and obtaining the first clock deviation and the first target device position of the target device using the TDoA and AoA positioning method according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0115] According to the TDoA positioning method, a TDoA equation is constructed regarding the channel complex gain phase change rate with subcarrier in the channel parameters, the time synchronization compensation amount in the time-frequency compensation amount, the clock deviation and the position of the target device;

[0116] According to the AoA positioning method, an AoA equation is constructed regarding the channel complex gain phase in the channel parameters with the antenna change rate, the antenna array direction vector and the target device position;

[0117] Combining the TDoA equation with the AoA equation, constructing a TDoA-AoA equation about the rate of change of the channel complex gain phase with the subcarrier, the rate of change of the channel complex gain phase with the antenna, the clock deviation, the time synchronization compensation amount, the antenna array direction vector, the target device position and the clock deviation;

[0118] The time synchronization compensation amount in each of the time-frequency compensation amounts, the channel complex gain phase estimation value with subcarrier change rate and the channel complex gain phase estimation value with antenna change rate in each of the channel parameter estimation values ​​are substituted into the TDoA-AoA equation, and the first clock deviation and the first target device position are calculated based on the least squares method.

[0119] In the embodiment of the present application, specific steps for calculating the first clock deviation and the first target device position are further provided. By constructing the TDoA equation, the calculation relationship between the channel complex gain phase following the subcarrier change rate, the time synchronization compensation, the clock deviation and the target device position is established; by constructing the AoA equation, the calculation relationship between the channel complex gain phase following the antenna change rate, the antenna array direction vector and the target device position is established. Then, the TDoA equation and the AoA equation are further combined to construct a comprehensive equation, and the corresponding channel parameter estimation values ​​are substituted and solved based on the least squares method, which solves the interference problem of time-frequency synchronization error and clock deviation on position estimation, improves the initial accuracy of the first positioning result, and provides a more reliable data basis for subsequent positioning calculations.

[0120] Further, the method of calculating and obtaining a first local crystal oscillator deviation and a first target device speed of the target device using the FDoA positioning method according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position includes:

[0121] According to the FDoA positioning method, an FDoA equation is constructed regarding the channel complex gain phase change rate with time slot in the channel parameters, the target device position, the target device speed, the frequency synchronization compensation amount in the time-frequency compensation amount, and the local crystal oscillator deviation;

[0122] Substitute the frequency synchronization compensation amount in each of the time-frequency compensation amounts, the estimated value of the channel complex gain phase change rate with time slot in each of the channel parameter estimation values, and the position of the first target device into the FDoA equation, and calculate the first local crystal oscillator deviation and the first target device speed based on the least squares method.

[0123] In an embodiment of the present application, specific steps for calculating the first local crystal oscillator deviation and the first target device speed are further provided. By constructing the FDoA equation, a calculation relationship is established between the channel complex gain phase change rate with time slot, the target device position, the target device speed, the frequency synchronization compensation amount and the local crystal oscillator deviation. Then, the corresponding channel parameter estimation values ​​are substituted and solved based on the least squares method, thereby achieving high-sensitivity measurement of the target device speed and the local crystal oscillator deviation. This is particularly suitable for high-speed mobile scenarios, enhances the system's ability to track dynamic targets, and provides a more reliable data basis for subsequent positioning calculations.

[0124] In a preferred embodiment, based on formulas (1), (2), and (3), the following TDoA equation can be constructed:

[0125]

[0126] Based on formula (1), the distance between the qth base station and the target device can be expressed as a function of AoA:

[0127]

[0128] According to formula (12) and (13), combined with the time synchronization compensation [Δt 1 ,…,Δt Q ] T , channel parameter estimation and The TDoA-AoA equation is as follows:

[0129] A t η t =b t (14)

[0130] where η t =[∈ t , p T ] T ,

[0131]

[0132] Based on formula (14), η t The least squares estimation result of is

[0133]

[0134] Use it as the initial solution of the clock deviation and the target device position, that is, the first clock deviation and the first target device position:

[0135]

[0136] In addition, according to formulas (1), (2), and (3), combined with the frequency synchronization compensation [Δf 1 , …, Δf Q ] T , channel parameter estimation and an initial solution to the target device location The FDoA equation for all Q base stations can also be constructed as follows:

[0137] A f η f =b f (18)

[0138] where η f =[∈ f , v T ] T ,

[0139]

[0140] Based on formula (18), η v The least squares estimation result of is

[0141]

[0142] Use it as the initial solution of the local crystal oscillator deviation and the target device speed, that is, the first local crystal oscillator deviation and the first target device speed:

[0143]

[0144] Combining formulas (17) and (21), the initial solution of η can be obtained, that is, the first positioning result:

[0145]

[0146] In a possible implementation manner, constructing the weight matrix using the Fisher information matrix of the channel parameter vector and the first positioning result includes:

[0147] Construct a first channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, the channel complex gain phase change rate with subcarrier, real channel gain and random phase noise in the channel parameters;

[0148] constructing a log-likelihood function of each of the received signals according to the first channel parameter vector, and then constructing a corresponding Fisher information matrix about the first channel parameter vector according to each of the log-likelihood functions;

[0149] Determine a first Cramer-Rao lower bound function of the first channel parameter vector according to each of the Fisher information matrices;

[0150] Constructing a second channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, and the channel complex gain phase change rate with subcarrier in the channel parameters;

[0151] Determine a second Cramer-Rao lower bound function of the second channel parameter vector according to the first Cramer-Rao lower bound function;

[0152] Substitute the first positioning result into the second Cramer-Rao lower bound function to calculate and obtain the weight matrix.

[0153] The embodiment of the present application provides a method for constructing a weight matrix. In the MIMO-OFDM communication architecture, the signal received by each base station is related to five types of channel parameters, namely, the rate of change of the complex gain phase of the channel with time slots, the rate of change of the complex gain phase of the channel with antennas, the rate of change of the complex gain phase of the channel with subcarriers, real channel gain, and random phase noise. Therefore, the embodiment of the present application constructs these five types of channel parameters as a first channel parameter vector, and constructs each Fisher information matrix corresponding to each received signal according to the first channel parameter vector, and then determines the first Cramer Rao lower bound function of the first channel parameter vector. Since only the residual synchronization error and the signal arrival angle contain effective information related to the position and speed of the target device in the channel parameters, and the residual synchronization error and the signal arrival angle are related to the rate of change of the complex gain phase of the channel with time slots, the rate of change of the complex gain phase of the channel with antennas, and the rate of change of the complex gain phase of the channel with subcarriers, the embodiment of the present application constructs the corresponding second channel parameter vector according to the required channel parameters, and determines the second Cramer Rao lower bound function of the second channel parameter vector based on the first Cramer Rao lower bound function of the first parameter vector, and substitutes the first positioning result to realize the construction of the weight matrix. The embodiment of the present application constructs a weight matrix based on the Fisher information matrix and the Cramer-Rao lower bound function, optimizes the weight allocation strategy of the weighted least squares model, reduces the negative impact of channel parameter noise on the positioning results, improves the model's anti-interference ability, and improves the accuracy of positioning and speed measurement of the target device.

[0154] In a preferred embodiment, according to the channel model of formula (4), the signal received by each base station is related to five types of channel parameters. Therefore, this system has a total of 5Q parameters, and the following five parameter vectors are defined:

[0155] ω t =[ω t，1 ,…,ω t，Q ] T ,ω a =[ω a，1 ,…,ω a，Q ] T ,ω f =[ω f，1 ,…,ω f，Q ] T ,φ=[φ 1 ,…,φ Q ] T , α=[α 1 , …α Q ] T

[0156] We further define the channel parameter vector as γ = [ω t T ,ω a T ,ωf T ,φ T , α T ] T , the likelihood function of the received signal is:

[0157]

[0158] Its log-likelihood function is l q，n，k (y q，n，k ; γ) = lnp q，n，k (y q，n，k ; γ), then the Fisher Information Matrix (FIM) about γ is:

[0159]

[0160] in For y q，n，k Find the expectation. Based on the assumption that the noise is uncorrelated, the sum of the Fisher information matrices about γ is

[0161]

[0162] Therefore, the Cramer-Rao lower bound of γ is R(γ)=F -1 According to the above analysis, only the residual synchronization error RSOs and the signal arrival angle contain effective information related to the position and speed of the target device. Therefore, the vector composed of these three types of parameters is redefined as ζ=[ω t T ,ω a T ,ω f T ] T , and the Cramer-Rao lower bound of ζ is:

[0163] R(ζ)=R(γ)[1:3Q,1:3Q] (23)

[0164] The initial solution of η obtained by formula (26) Substituting into R(ζ) we get the weighted matrix w, namely:

[0165]

[0166] Furthermore, the estimated results of the channel parameters related to the position and speed between all base stations and the target device can form a vector in and In addition, the time-frequency synchronization compensation [Δt 1 ,…,Δt Q ] Tand [Δf 1 ,…,Δf Q ] T According to formula (1), (2), (3), the channel parameter calculation function of these three types of channel parameters with respect to η is defined as Construct a weighted least squares estimation problem about clock deviation, local crystal deviation, target device position and speed, that is, the positioning model:

[0167]

[0168] Where w is the weighting matrix calculated according to formula (24).

[0169] In a possible implementation, in step S5, the iterative optimization algorithm is a Gauss-Newton iteration method, and the positioning model performs a number of weighted iterative updates on the first positioning result based on the iterative optimization algorithm to generate a second positioning result of the target device, including:

[0170] In each weighted iterative update process, according to the current positioning result and each of the time-frequency compensation amounts, the channel parameter calculation function is used to calculate and obtain each current channel parameter estimation value;

[0171] Calculating channel parameter difference values ​​between each of the channel parameter estimation values ​​and each of the corresponding current channel parameter estimation values;

[0172] Constructing and obtaining a current weight matrix according to the current positioning result;

[0173] Calculate and obtain a positioning result difference value according to each of the channel parameter difference values, each of the current channel parameter estimation values ​​and the current weight matrix;

[0174] The current positioning result is updated according to the positioning result difference value to obtain an updated positioning result, and the updated positioning result is made to enter the next weighted iterative update process;

[0175] When the number of weighted iterative updates reaches a preset value, the iteration is stopped, and the updated positioning result obtained by the last weighted iterative update is used as the second positioning result.

[0176] The embodiment of the present application provides a method for iteratively updating the positioning result, in which the current channel parameter estimation value, the channel parameter difference value, the current weight matrix and the positioning result difference value are calculated in turn during each iteration, and finally the updated positioning result is obtained according to the positioning result difference value calculation, and the next iteration is entered. The embodiment of the present application adopts the Gauss-Newton iteration method for weighted iterative update, which can quickly converge to a more accurate positioning result, and at the same time, by dynamically adjusting the weight matrix, effectively suppress error accumulation, and improve the efficiency and stability of iterative optimization.

[0177] In a preferred embodiment, since the weighted least squares estimation problem of formula (25) is highly non-convex, the first positioning result can be The Gauss-Newton method is used to iteratively optimize the approximate solution of η. That is, the second positioning result. Wherein I is the total number of iterations. The algorithm flow (ie, step S4-step S5) of the embodiment of the present application for positioning and measuring the speed of the target device according to the channel parameter estimation value is shown in Algorithm 2, where i represents the index of each iteration.

[0178]

[0179] Experimental results show that, compared with the existing integrated communication and positioning framework that does not consider the communication timing-frequency synchronization error, the positioning accuracy and speed measurement accuracy of the embodiment of the present application can be improved by up to 95% and 86%; compared with the existing time-frequency synchronization algorithm, the clock synchronization and frequency synchronization accuracy can be improved by 99% and 97%.

[0180] Embodiment 2:

[0181] like Figure 3 As shown, embodiment 2 provides a positioning and speed measurement system based on MIMO-OFDM communication architecture, including a signal receiving module 10, a time-frequency synchronization module 20, a channel estimation module 30, a first positioning module 40 and a second positioning module 50;

[0182] The signal receiving module 10 is used to receive the communication signal of the target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on the MIMO-OFDM communication architecture;

[0183] The time-frequency synchronization module 20 is used to perform time-frequency synchronization on the received signals through each of the base stations, so as to obtain the time-frequency compensation amount corresponding to each of the received signals;

[0184] The channel estimation module 30 is used to perform channel estimation on the received signals through each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals;

[0185] The first positioning module 40 is used to calculate and obtain a first positioning result of the target device according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​using preset TDoA, AoA and FDoA positioning methods, wherein the first positioning result includes a first clock deviation, a first local crystal oscillator deviation, a first target device position and a first target device speed;

[0186] The second positioning module 50 is used to input each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result into a preset positioning model, so that the positioning model performs a number of weighted iterative updates on the first positioning result based on an iterative optimization algorithm to generate a second positioning result of the target device, thereby determining the position and speed of the target device;

[0187] Among them, the positioning model is a weighted least squares estimation model constructed based on each of the channel parameter estimation values, the channel parameter calculation function and the weight matrix, the weight matrix is ​​constructed by the Fisher information matrix of the channel parameter vector and the first positioning result, and the channel parameter calculation function is a function that calculates the channel parameters based on each of the time-frequency compensation amounts, clock deviation, local crystal oscillator deviation, target device position and target device speed.

[0188] In a possible implementation, the channel estimation module 30 performs channel estimation on the received signals through each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals, including:

[0189] By each of the base stations performing channel estimation on the received signal, respective corresponding channel estimation results are obtained;

[0190] Inputting each of the channel estimation results into a preset channel parameter estimation model, so that the channel parameter estimation model is solved by a multi-dimensional frequency estimation algorithm based on discrete Fourier transform to obtain a channel parameter estimation value corresponding to each of the channel estimation results;

[0191] The channel parameter estimation model is a maximum likelihood estimation model constructed based on the channel estimation results and channel parameters.

[0192] In a possible implementation, the first positioning module 40 calculates and obtains a first positioning result of the target device using preset TDoA, AoA, and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values, including:

[0193] According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device;

[0194] The FDoA positioning method is used to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position.

[0195] Further, the step of calculating and obtaining the first clock deviation and the first target device position of the target device using the TDoA and AoA positioning method according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes:

[0196] According to the TDoA positioning method, a TDoA equation is constructed regarding the channel complex gain phase change rate with subcarrier in the channel parameters, the time synchronization compensation amount in the time-frequency compensation amount, the clock deviation and the position of the target device;

[0197] According to the AoA positioning method, an AoA equation is constructed regarding the channel complex gain phase in the channel parameters with the antenna change rate, the antenna array direction vector and the target device position;

[0198] Combining the TDoA equation with the AoA equation, constructing a TDoA-AoA equation about the rate of change of the channel complex gain phase with the subcarrier, the rate of change of the channel complex gain phase with the antenna, the clock deviation, the time synchronization compensation amount, the antenna array direction vector, the target device position and the clock deviation;

[0199] The time synchronization compensation amount in each of the time-frequency compensation amounts, the channel complex gain phase estimation value with subcarrier change rate and the channel complex gain phase estimation value with antenna change rate in each of the channel parameter estimation values ​​are substituted into the TDoA-AoA equation, and the first clock deviation and the first target device position are calculated based on the least squares method.

[0200] Further, the method of calculating and obtaining a first local crystal oscillator deviation and a first target device speed of the target device using the FDoA positioning method according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position includes:

[0201] According to the FDoA positioning method, an FDoA equation is constructed regarding the channel complex gain phase change rate with time slot in the channel parameters, the target device position, the target device speed, the frequency synchronization compensation amount in the time-frequency compensation amount, and the local crystal oscillator deviation;

[0202] Substitute the frequency synchronization compensation amount in each of the time-frequency compensation amounts, the estimated value of the channel complex gain phase change rate with time slot in each of the channel parameter estimation values, and the position of the first target device into the FDoA equation, and calculate the first local crystal oscillator deviation and the first target device speed based on the least squares method.

[0203] In a possible implementation manner, constructing the weight matrix using the Fisher information matrix of the channel parameter vector and the first positioning result includes:

[0204] Construct a first channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, the channel complex gain phase change rate with subcarrier, real channel gain and random phase noise in the channel parameters;

[0205] constructing a log-likelihood function of each of the received signals according to the first channel parameter vector, and then constructing a corresponding Fisher information matrix about the first channel parameter vector according to each of the log-likelihood functions;

[0206] Determine a first Cramer-Rao lower bound function of the first channel parameter vector according to each of the Fisher information matrices;

[0207] Constructing a second channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, and the channel complex gain phase change rate with subcarrier in the channel parameters;

[0208] Determine a second Cramer-Rao lower bound function of the second channel parameter vector according to the first Cramer-Rao lower bound function;

[0209] Substitute the first positioning result into the second Cramer-Rao lower bound function to calculate and obtain the weight matrix.

[0210] In a possible implementation, the iterative optimization algorithm is a Gauss-Newton iteration method, and the positioning model performs a number of weighted iterative updates on the first positioning result based on the iterative optimization algorithm to generate a second positioning result of the target device, including:

[0211] In each weighted iterative update process, according to the current positioning result and each of the time-frequency compensation amounts, the channel parameter calculation function is used to calculate and obtain each current channel parameter estimation value;

[0212] Calculating channel parameter difference values ​​between each of the channel parameter estimation values ​​and each of the corresponding current channel parameter estimation values;

[0213] Constructing and obtaining a current weight matrix according to the current positioning result;

[0214] Calculate and obtain a positioning result difference value according to each of the channel parameter difference values, each of the current channel parameter estimation values ​​and the current weight matrix;

[0215] The current positioning result is updated according to the positioning result difference value to obtain an updated positioning result, and the updated positioning result is made to enter the next weighted iterative update process;

[0216] When the number of weighted iterative updates reaches a preset value, the iteration is stopped, and the updated positioning result obtained by the last weighted iterative update is used as the second positioning result.

[0217] The embodiment of the present application provides a positioning and speed measurement system based on a MIMO-OFDM communication architecture. Based on the characteristics of the MIMO-OFDM communication architecture, multiple base stations are used to receive the communication signal of the target device, and the time-frequency compensation amount and channel parameter estimation value corresponding to the received signal are extracted through time-frequency synchronization and channel estimation. Considering the inevitable time-frequency synchronization errors between the base stations, these time-frequency synchronization errors will reduce the accuracy of positioning and speed measurement to a certain extent. In response to this problem, the embodiment of the present application is based on the assumption that high-precision time-frequency synchronization can be achieved between base stations. The residual time-frequency synchronization errors between different base stations and the target device can be regarded as consistent, and the residual time-frequency synchronization error has a calculation relationship with the channel parameters, time-frequency compensation amount, clock deviation, and local crystal oscillator deviation. Therefore, the embodiment of the present application integrates the TDoA, AoA and FDoA positioning methods, and uses the weighted least squares estimation model for iterative optimization. While calculating the target position and speed, it also solves the clock deviation and local crystal oscillator deviation, thereby improving the accuracy of time-frequency synchronization, and thus improving the positioning accuracy of the target device position and speed. In particular, in the scenario where there is a time-frequency synchronization error between the target device and the base station, it can effectively compensate for the impact of the synchronization deviation on the positioning result.

[0218] The more detailed working principle and step flow of this embodiment can refer to, but are not limited to, the relevant records of Embodiment 1.

[0219] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A positioning and speed measurement method based on MIMO-OFDM communication architecture, characterized in that: include: Receiving a communication signal of a target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on a MIMO-OFDM communication architecture; By each of the base stations performing time-frequency synchronization on the received signals, a time-frequency compensation amount corresponding to each of the received signals is obtained; By each of the base stations performing channel estimation on the received signals, a channel parameter estimation value corresponding to each of the received signals is obtained; Inputting each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result of the target device into a preset positioning model, so that the positioning model performs a weighted iterative update on the first positioning result several times based on an iterative optimization algorithm to generate a second positioning result of the target device, thereby determining the position and speed of the target device; The first positioning result is calculated based on each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​using preset TDoA, AoA and FDoA positioning methods.

2. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 1, characterized in that: The performing channel estimation on the received signals by each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals includes: By each of the base stations performing channel estimation on the received signal, respective corresponding channel estimation results are obtained; Inputting each of the channel estimation results into a preset channel parameter estimation model, so that the channel parameter estimation model is solved by a multi-dimensional frequency estimation algorithm based on discrete Fourier transform to obtain a channel parameter estimation value corresponding to each of the channel estimation results; The channel parameter estimation model is a maximum likelihood estimation model constructed based on the channel estimation results and channel parameters.

3. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 1, characterized in that: The step of calculating and obtaining a first positioning result of the target device using preset TDoA, AoA and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes: According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device; According to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position, using the FDoA positioning method to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device; The first clock deviation, the first target device position, the first local crystal oscillator deviation and the first target device speed are constructed as the first positioning result.

4. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 3, characterized in that: The step of calculating and obtaining the first clock deviation and the first target device position of the target device using the TDoA and AoA positioning method according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes: According to the TDoA positioning method, a TDoA equation is constructed regarding the channel complex gain phase change rate with subcarrier in the channel parameters, the time synchronization compensation amount in the time-frequency compensation amount, the clock deviation and the position of the target device; According to the AoA positioning method, an AoA equation is constructed regarding the channel complex gain phase in the channel parameters with the antenna change rate, the antenna array direction vector and the target device position; Combining the TDoA equation with the AoA equation, constructing a TDoA-AoA equation about the rate of change of the channel complex gain phase with the subcarrier, the rate of change of the channel complex gain phase with the antenna, the clock deviation, the time synchronization compensation amount, the antenna array direction vector, the target device position and the clock deviation; The time synchronization compensation amount in each of the time-frequency compensation amounts, the channel complex gain phase estimation value with subcarrier change rate and the channel complex gain phase estimation value with antenna change rate in each of the channel parameter estimation values ​​are substituted into the TDoA-AoA equation, and the first clock deviation and the first target device position are calculated based on the least squares method.

5. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 3, characterized in that: The method of calculating and obtaining a first local crystal oscillator deviation and a first target device speed of the target device using the FDoA positioning method according to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position includes: According to the FDoA positioning method, an FDoA equation is constructed regarding the channel complex gain phase change rate with time slot in the channel parameters, the target device position, the target device speed, the frequency synchronization compensation amount in the time-frequency compensation amount, and the local crystal oscillator deviation; Substitute the frequency synchronization compensation amount in each of the time-frequency compensation amounts, the estimated value of the channel complex gain phase change rate with time slot in each of the channel parameter estimation values, and the position of the first target device into the FDoA equation, and calculate the first local crystal oscillator deviation and the first target device speed based on the least squares method.

6. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 1, characterized in that: The positioning model is a weighted least squares estimation model constructed based on each of the channel parameter estimation values, a channel parameter calculation function and a weight matrix. The weight matrix is ​​constructed by the Fisher information matrix of the channel parameter vector and the first positioning result. The channel parameter calculation function is a function that calculates the channel parameters based on each of the time-frequency compensation amounts, clock deviation, local crystal oscillator deviation, target device position and target device speed.

7. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 6, characterized in that: The step of constructing the weight matrix using the Fisher information matrix of the channel parameter vector and the first positioning result includes: Construct a first channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, the channel complex gain phase change rate with subcarrier, real channel gain and random phase noise in the channel parameters; constructing a log-likelihood function of each of the received signals according to the first channel parameter vector, and then constructing a corresponding Fisher information matrix about the first channel parameter vector according to each of the log-likelihood functions; Determine a first Cramer-Rao lower bound function of the first channel parameter vector according to each of the Fisher information matrices; Constructing a second channel parameter vector according to the channel complex gain phase change rate with time slot, the channel complex gain phase change rate with antenna, and the channel complex gain phase change rate with subcarrier in the channel parameters; Determine a second Cramer-Rao lower bound function of the second channel parameter vector according to the first Cramer-Rao lower bound function; Substitute the first positioning result into the second Cramer-Rao lower bound function to calculate and obtain the weight matrix.

8. A positioning and speed measurement method based on MIMO-OFDM communication architecture as claimed in claim 6, characterized in that: The iterative optimization algorithm is a Gauss-Newton iterative method, and the positioning model performs a number of weighted iterative updates on the first positioning result based on the iterative optimization algorithm to generate a second positioning result of the target device, including: In each weighted iterative update process, according to the current positioning result and each of the time-frequency compensation amounts, the channel parameter calculation function is used to calculate and obtain each current channel parameter estimation value; Calculating channel parameter difference values ​​between each of the channel parameter estimation values ​​and each of the corresponding current channel parameter estimation values; Constructing and obtaining a current weight matrix according to the current positioning result; Calculate and obtain a positioning result difference value according to each of the channel parameter difference values, each of the current channel parameter estimation values ​​and the current weight matrix; The current positioning result is updated according to the positioning result difference value to obtain an updated positioning result, and the updated positioning result is made to enter the next weighted iterative update process; When the number of weighted iterative updates reaches a preset value, the iteration is stopped, and the updated positioning result obtained by the last weighted iterative update is used as the second positioning result.

9. A positioning and speed measurement system based on MIMO-OFDM communication architecture, characterized in that: It includes a signal receiving module, a time-frequency synchronization module, a channel estimation module and a positioning module; The signal receiving module is used to receive the communication signal of the target device through each base station to obtain a plurality of received signals, wherein each base station communicates based on the MIMO-OFDM communication architecture; The time-frequency synchronization module is used to perform time-frequency synchronization on the received signals through each of the base stations, so as to obtain the time-frequency compensation amount corresponding to each of the received signals; The channel estimation module is used to perform channel estimation on the received signals through each of the base stations to obtain channel parameter estimation values ​​corresponding to each of the received signals; The positioning module is used to input each of the channel parameter estimation values, each of the time-frequency compensation amounts and the first positioning result of the target device into a preset positioning model, so that the positioning model performs a weighted iterative update on the first positioning result several times based on an iterative optimization algorithm to generate a second positioning result of the target device, thereby determining the position and speed of the target device; The first positioning result is calculated based on each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​using preset TDoA, AoA and FDoA positioning methods.

10. A positioning and speed measurement system based on MIMO-OFDM communication architecture as claimed in claim 9, characterized in that: The step of calculating and obtaining a first positioning result of the target device using preset TDoA, AoA and FDoA positioning methods according to each of the time-frequency compensation amounts and each of the channel parameter estimation values ​​includes: According to each of the time-frequency compensation amounts and each of the channel parameter estimation values, using the TDoA and AoA positioning method to calculate and obtain a first clock deviation and a first target device position of the target device; According to each of the time-frequency compensation amounts, each of the channel parameter estimation values ​​and the first target device position, using the FDoA positioning method to calculate and obtain a first local crystal oscillator deviation and a first target device speed of the target device; The first clock deviation, the first target device position, the first local crystal oscillator deviation and the first target device speed are constructed as the first positioning result.

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