5G AeroMACS base station communication and positioning integrated method

Abstract

The technical field of airport wireless communication specifically relates to a 5G AeroMACS base station communication and positioning integration method, comprising: establishing an airport scene communication and navigation integration system, including a 5G AeroMACS multi-antenna base station, a plurality of single-antenna communication terminals and a plurality of mobile terminals to be positioned; obtaining a perception positioning parameter estimation amount of the mobile terminal to be positioned according to an echo signal; obtaining an estimation error lower bound of the perception positioning parameter based on the perception positioning parameter estimation amount; minimizing and optimizing the estimation error lower bound based on the channel characteristics of the communication between the 5G AeroMACS multi-antenna base station and the single-antenna communication terminal to obtain an optimal beamforming parameter; using the optimal beamforming parameter to control the 5G AeroMACS multi-antenna base station to send a signal, and completing the communication and positioning between the 5G AeroMACS multi-antenna base station and the terminal; the present invention can realize high-precision real-time navigation and efficient communication of high-dynamic terminals on the airport scene.

Description

Technical Field

[0001] The present invention relates to the technical field of airport wireless communications, and in particular to a 5G AeroMACS base station communication and positioning integrated method. Background Art

[0002] 5G AeroMACS is an aviation 5G airport broadband mobile communication system. In recent years, 5G array communication technology represented by beamforming has achieved a significant improvement in the signal strength at the receiving end, improving the quality of communication services. At the same time, in the airport system, due to the rapid movement of communication terminals and the complex and changing environment, the system needs to accurately perceive the location and motion status of the target in real time while ensuring the quality of communication. Therefore, accurate navigation services are the basic capability to ensure the efficient operation of the airport.

[0003] Integrated communication and navigation technology is a cutting-edge technology in modern wireless communication systems. In airport surface systems, the simultaneous realization of communication and navigation functions can significantly improve the overall performance and resource utilization of the system. However, the difficulty of integrated communication and navigation technology lies in how to achieve high-precision real-time navigation and efficient communication in a highly dynamic environment. The main problems in related technical research are reflected in the following two aspects: On the one hand, due to the different needs of different terminal users in the airport for communication and guidance, the 5G AeroMACS base station needs to balance the channel capacity of the communication terminal and the accuracy of estimating the unknown parameters of the target under limited power and functional conditions. On the other hand, the rapid movement of airport terminal users and the complex changes in the environment require the system to have extremely high real-time processing capabilities to ensure the synchronization of navigation and communication. This places extremely high demands on the computational complexity and processing efficiency of the algorithm. Summary of the invention

[0004] In view of the above problems, the present invention provides a 5G AeroMACS base station communication and positioning integrated method, which solves the technical problem in the prior art that it is difficult to efficiently monitor and track the position and motion status of high-dynamic terminals on the airport scene.

[0005] The present invention provides a 5G AeroMACS base station communication and positioning integrated method, comprising the following steps:

[0006] Step S1, establishing an airport surface communication and navigation integrated system, including a 5G AeroMACS multi-antenna base station, a plurality of single-antenna communication terminals and a plurality of mobile terminals to be located; the 5G AeroMACS multi-antenna base station communicates in real time with the single-antenna communication terminal and the mobile terminal to be located respectively;

[0007] Step S2, the 5G AeroMACS multi-antenna base station receives the echo signal sent by the mobile terminal to be located; obtains the perceived positioning parameter estimation of the mobile terminal to be located according to the echo signal; the perceived positioning parameter includes a complex channel gain coefficient and angle ;

[0008] Step S3: constructing a Fisher information matrix based on the perception positioning parameter estimation, and obtaining the estimation error lower bound of the perception positioning parameter from the Fisher information matrix;

[0009] Step S4: based on the channel characteristics of the communication between the 5G AeroMACS multi-antenna base station and the single-antenna communication terminal, the lower bound of the estimation error is minimized to obtain the optimal beamforming parameters;

[0010] Step S5: Use the optimal beamforming parameters to control the 5G AeroMACS multi-antenna base station to send signals, so as to complete the communication and positioning between the 5G AeroMACS base station and the terminal.

[0011] Preferably, step S2 specifically includes:

[0012] Step S2-1, the 5G AeroMACS multi-antenna base station receives an echo signal sent by the mobile terminal to be located from an echo channel;

[0013] Step S2-2: determining an initial value of the perception positioning parameter based on a Capon beamforming power spectrum; and determining a likelihood function based on the initial value of the perception positioning parameter;

[0014] Step S2-3: maximizing and optimizing the likelihood function by gradient descent to obtain an optimal perceptual positioning parameter as an estimate of the perceptual positioning parameter.

[0015] Preferably, in step S2-1, the echo signal expression is:

[0016]

[0017] in, The first signal received by the 5G AeroMACS multi-antenna base station A signal, The first 5G AeroMACS multi-antenna base station A signal, is the symbol index, , is the total number of symbols in the communication symbol period, For 5G AeroMACS multi-antenna base station and mobile terminal to be positioned The echo channel gain between The index of the mobile terminal to be located , , is the total number of mobile terminals to be located, For the Additive white Gaussian noise of the signal.

[0018] Preferably, step S2-2 specifically includes: for each possible target angle, calculating the Capon power spectrum, and the angle corresponding to the peak of the Capon power spectrum is used as the initial angle , and select the initial complex channel coefficients ;

[0019] based on and , the likelihood function is calculated according to the following expression:

[0020]

[0021] in, express Observe the received signal The likelihood function of Indicates the mobile terminal to be located The complex channel gain coefficient is and They are the transmit and receive steering vectors at the 5G AeroMACS multi-antenna base station antennas, for The conjugate transpose of For the mobile terminal to be located Angle, It represents the exponential calculation of the natural constant e. is the L2 norm, is the noise variance at the receiving antenna.

[0022] Preferably, step S2-3 specifically includes: performing gradient descent using the following expression to iteratively update the perception positioning parameters:

[0023]

[0024]

[0025]

[0026]

[0027] in, , Respectively Iteration and , For the mobile terminal to be located Angle, , Respectively Iteration and , For the mobile terminal to be located The complex channel gain coefficient is , Respectively Iteration and , and They are and The iteration step length is represents the calculation of the real part, Express Find partial derivatives;

[0028] During the iteration process, if the change in the updated perception positioning parameter is less than the preset threshold, the estimated value of the perception positioning parameter is output .

[0029] Preferably, step S3 specifically includes: calculating the Fisher information matrix corresponding to the complex channel gain coefficient and angle of the target based on the estimated amount of the perception positioning parameter, and the expression is as follows:

[0030]

[0031] in, and are the antenna transmit and receive steering matrices, respectively, for The complex conjugate matrix of for The conjugate transpose of is the sending signal matrix, is the complex channel gain coefficient matrix, for and The parameter matrix to be estimated is composed of The parameter to be estimated The value of the element in the i-th row and j-th column of the Fisher information matrix, where i and j are matrix indices. represents the conjugate transpose of the matrix. , They are The i-th row and j-th column of the matrix; represents the calculation of the real part; Indicates vectorizing the matrix;

[0032] Then the lower bound expression of the estimation error of the perception positioning parameters is:

[0033]

[0034] in, The emission covariance matrix, To calculate the lower bound of the estimation error, represents the trace of the matrix, is the inverse matrix of the Fisher information matrix.

[0035] Preferably, step S4 specifically includes:

[0036] Step S4-1, determining the constraint conditions for minimizing the lower bound of the estimation error;

[0037] Step S4-2: Determine the initial point of the precoding vector that satisfies the maximum multicast rate based on semi-positive relaxation and Gaussian randomization method;

[0038] Step S4-3: iteratively update the initial point of the precoding vector based on the constraint condition; when the iteration end condition is met, end the iteration and use the precoding vector after the iteration as the optimal beamforming parameter.

[0039] Preferably, step S4-1 specifically includes: determining an expression of a constraint condition for minimizing the lower bound of the estimation error:

[0040]

[0041]

[0042]

[0043]

[0044] in, express Optimize to the minimum value under any condition. for The inverse matrix of Indicates the conditions that are subject to constraints. is the precoding vector of the 5G AeroMACS multi-antenna base station, for The conjugate transpose of is the maximum transmit power, For single antenna communication terminal The channel gain, for The conjugate transpose of is the single-antenna communication terminal index, , is the number of the single-antenna communication terminals, for The precoding vector of the iteration, for The conjugate transpose of , is the noise variance at the single-antenna communication terminal, is the minimum communication rate of a single-antenna communication terminal, Indicates any choice.

[0045] Preferably, step S4-2 specifically comprises: using a semi-positive definite relaxation method to process the constraints of the minimization optimization to obtain a semi-positive definite matrix, performing eigendecomposition on the semi-positive definite matrix, and selecting Satisfy ,in, is the eigenvector matrix obtained by the semidefinite relaxation method, is a random vector, The elements of are uniformly distributed on the unit circle of the complex plane; for , find the vector that satisfies the transmission communication beamforming to achieve the maximum multicast rate, and use the precoding vector that achieves the maximum multicast rate as the initial point .

[0046] Preferably, step S4-3 specifically includes: using a numerical optimization tool CVX to iteratively optimize the constraint conditions, so as to As the initial point, multiple iterations are performed. Indicates The precoding vector of the optimization problem in the iteration The solution is and When the change of is less than the preset threshold, the iteration ends and the optimal precoding vector is obtained. as the optimal beamforming parameters.

[0047] Compared with the prior art, the present invention has at least the following beneficial effects:

[0048] (1) The method provided by the present invention can ensure the minimum rate requirement of information multicast while ensuring the accuracy of multi-target perception by balancing the lower bound of the multi-target perception positioning estimation error CRB with the capacity of the information multicast channel. This balancing mechanism enables the system to achieve better performance in both the perception positioning and communication of the airport base station.

[0049] (2) The method provided by the present invention enables the system to achieve optimal perception accuracy on all targets by minimizing the total CRB of multi-target parameters, significantly reducing perception errors and improving the estimation accuracy of target parameters. This improves the reliability and effectiveness of the system in complex airport scenes that require precise positioning.

[0050] (3) The present invention adopts the SCA method for the non-convex quadratic constraints involved in the optimization problem, converting the complex non-convex optimization problem into a more manageable convex function form, simplifying the problem-solving process while ensuring the convergence and stability of the optimization algorithm. The use of SCA technology in the solution process can quickly converge to a highly reliable beamforming solution, ensuring operability in practical applications and improving computational efficiency. While ensuring the total communication rate of the communication terminal, the estimation accuracy of the multi-objective unknown parameters is improved by minimizing the multi-objective CRB.

[0051] (4) The CAML method provided by the present invention optimizes the estimation of target parameters by combining MLE, so that CAML can accurately distinguish the target direction in a multi-target environment, even if the angles between the targets are very close. At the same time, the maximum likelihood estimation has statistical optimality and can provide minimum variance unbiased estimation under a known noise model, ensuring the reliability and accuracy of the estimation under given measured echo data. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0053] Figure 1 This is a flow chart of the 5G AeroMACS base station communication and positioning integrated method provided by the present invention.

[0054] Figure 2 Schematic diagram of the 5G AeroMACS base station communication and positioning integrated system provided by the present invention.

[0055] Figure 3 The present invention provides a flow chart of the SCA-based beamforming design algorithm.

[0056] Figure 4 This is a flow chart of the CAML estimation algorithm provided by the present invention.

[0057] Reference numerals: taxiing aircraft-10, moving vehicle-20, ground base station-30, prohibited area-40. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0059] The present invention provides a beamforming optimization method for 5G AeroMACS base stations that integrate communication and perception functions under limited power and functional conditions, and weighs the basic performance between the lower bound of multi-objective parameter estimation error, namely the Cramér-Rao Bound (CRB), and the information multicast channel capacity. Specifically, a communication and navigation integration model for airport scenes is first constructed, which organically combines communication and navigation functions and improves the overall performance and efficiency of the system by sharing spectrum resources. Based on this model, the statistical characteristics of the echo signal are established, and then the Fisher information matrix is ​​used to quantify the amount of information of the perception target position parameters in the echo data, and the expression of CRB is obtained as the variance lower bound of the multi-objective parameter estimation. By minimizing the CRB of the multi-objective parameters, the continuous convex approximation (SCA) technology is used to obtain the beamforming solution, which ensures the minimum multicast communication rate requirement and the maximum transmission power limit while achieving the optimization goal. As for how to estimate the position parameters of the perceived target, the present invention provides a Capon Approximate Maximum Likelihood (CAML) estimation algorithm to estimate unknown parameters such as the complex channel gain coefficient and angle of the target. Through the integrated communication and navigation mechanism, the system can perceive the airport environment while performing communication tasks, thereby effectively monitoring and tracking the position and motion status of high-dynamic terminals on the airport scene.

[0060] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. Figure 1 As shown, a 5G AeroMACS base station communication and positioning integrated method is disclosed, and the specific implementation steps are as follows:

[0061] Step S1, establishing an airport surface communication and navigation integrated system, including a 5G AeroMACS multi-antenna base station, a plurality of single-antenna communication terminals and a plurality of mobile terminals to be located; the 5G AeroMACS multi-antenna base station communicates in real time with the single-antenna communication terminal and the mobile terminal to be located respectively.

[0062] For an integrated multi-antenna communication and navigation system for information multicasting and multi-target perception for airport surface management, the system consists of a 5G AeroMACS multi-antenna base station (BS) with integrated sensing and communication (ISAC) capabilities, K single-antenna communication terminals, and M mobile terminals to be located. The 5G AeroMACS multi-antenna base station is equipped with two rectangular uniform linear antenna arrays, which are divided into a transmitting signal antenna and a receiving signal antenna, respectively. and The 5G AeroMACS multi-antenna base station sends public information signals to all users at the airport and uses the received echo signals to simultaneously locate M mobile terminals to be located. In this system, all user terminals can be set as single-antenna communication terminals. Figure 2 As shown in the figure, the 5G AeroMACS multi-antenna base station senses the positions of various mobile terminals to be located at the airport (such as moving support vehicles and aircraft) and provides them with real-time direction guidance to prevent the target from approaching or entering illegal areas. At the same time, the 5G AeroMACS multi-antenna base station also communicates in real time with single-antenna communication terminals such as other vehicles on the airport.

[0063] In order to maintain generality, the communication environment of the present invention is a quasi-static environment, that is, the communication and perception channels remain unchanged within a time symbol period. A symbol period consists of L symbols, which represents a time period in which the system operates in a quasi-static environment. For a symbol period, the corresponding symbol duration is ,in is the signal bandwidth. A signal, , the expression is:

[0064]

[0065] in, The first 5G AeroMACS multi-antenna base station A signal, is the precoding vector of the 5G AeroMACS multi-antenna base station, and the precoding vector size is , For the A communication signal to be sent with a mean of 0 and a variance of 1. For the A dedicated sensor signal is a pseudo-random vector with a mean of zero, and its covariance matrix is The above expression indicates that the phase and amplitude of the signal to be transmitted are adjusted by the precoding vector so that the signal is enhanced in a specific direction, thereby improving the signal coverage and reception quality.

[0066] The transmission covariance matrix expression corresponding to the transmission signal is:

[0067]

[0068] in, is the precoding vector The conjugate transpose of is the covariance matrix of the dedicated sensor signal.

[0069] The communication channel between the 5G AeroMACS multi-antenna base station and the single-antenna communication terminal is expressed by the following formula:

[0070]

[0071] in, For single antenna communication terminal The channel gain, is the single-antenna communication terminal index, , 5G AeroMACS multi-antenna base station to single-antenna communication terminal The complex channel gain, Indicates channel The spatial response vector of the direction, For single antenna communication terminal direction;

[0072] At this time, the single antenna communication terminal The first Signal for:

[0073]

[0074] in, , No. The noise signal received by a single-antenna communication terminal has a mean of 0 and a variance of Additive white Gaussian noise.

[0075] According to the received signal in expression (4), at a single-antenna communication terminal The received signal-to-noise ratio at is:

[0076]

[0077] in, Indicates The received signal-to-noise ratio of a single-antenna terminal is represents the computational expectation, for The conjugate transpose of Represents the calculation modulus of a complex number.

[0078] The communication rate expression of 5G AeroMACS multi-antenna base station and single-antenna communication terminal is:

[0079]

[0080] in, No. The communication rate of a single-antenna terminal.

[0081] Expressions (5) and (6) describe the signal-to-noise ratio and communication rate performance indicators of the airport surface communication and navigation integrated system provided by the present invention.

[0082] Step S2, the multi-antenna 5G AeroMACS multi-antenna base station receives the echo signal sent by the mobile terminal to be located; obtains the perceived positioning parameter estimation of the mobile terminal to be located according to the echo signal; the perceived positioning parameter includes the complex signal gain coefficient and angle .

[0083] Assumptions represents the transmitted signal over a symbol period consisting of L symbols. Assuming L is large enough, the sample covariance matrix of X can be approximated as the statistical covariance matrix ,Right now:

[0084]

[0085] The 5G AeroMACS multi-antenna base station senses the mobile terminal to be positioned. The sensing echo channel characteristics between the 5G AeroMACS multi-antenna base station and the mobile terminal to be positioned are expressed as follows:

[0086]

[0087] in, For 5G AeroMACS multi-antenna base station and mobile terminal to be positioned The echo channel gain between The index of the mobile terminal to be located , represents the complex channel gain coefficient of the reflection channel, and They are the transmit and receive steering vectors at the 5G AeroMACS multi-antenna base station antennas, for The conjugate transpose of For the mobile terminal to be located angle.

[0088] For symbols Target echo signal received at the 5G AeroMACS multi-antenna base station It is expressed as:

[0089]

[0090] in, ,express is the additive white Gaussian noise at the receiving antenna, and the noise power is .

[0091] In order to facilitate the derivation of the CRB matrix for estimating the target parameters, equation (9) is redefined as follows:

[0092]

[0093] in, , , the antenna steering vector is , for The complex conjugate matrix of for The conjugate transpose of is the received signal matrix, is the noise matrix, is the sending signal matrix, is the complex channel gain coefficient matrix, , , It means constructing a diagonal matrix.

[0094] The target complex channel gain coefficient is estimated and target angle As unknown parameters. M targets are tracked by estimating the complex channel gain coefficient and angle of the target. For the estimation of multi-target parameters, the present invention provides a Capon Approximate Maximum Likelihood (CAML) estimation method to estimate the unknown parameters such as the complex channel gain coefficient and angle of the target. By combining the high angular resolution characteristics of Capon beamforming and the statistical optimization capability of MLE, accurate estimation of multi-target parameters is achieved. The specific process is:

[0095] Assuming that the received signal is a superposition of M targets, its signal model is shown in equation (9), and Capon beamforming is used to estimate the target direction information in the signal.

[0096] For each possible target angle, the calculation formula of Capon beamforming power spectrum is:

[0097]

[0098] in, Target angle The beamforming power spectrum of Represents the covariance matrix of the received signal The inverse matrix of , Indicates the target angle The launch steering vector, for The conjugate transpose of . After obtaining the initial estimate of the Capon power spectrum, the likelihood function of the target parameter is constructed. The MLE goal is to maximize the likelihood function. Since the noise is Gaussian distributed, the likelihood function is:

[0099]

[0100] in, Representation parameters Observe the received signal The likelihood function of Indicates the mobile terminal to be located The complex channel gain coefficient is It represents the exponential calculation of the natural constant e. is the L2 norm.

[0101] like Figure 4 As shown in Figure 1, the process of CAML estimation combined with Capon beamforming and MLE for parameter optimization is mainly divided into the following three steps:

[0102] (1) Perform an initial estimate, using the peak position of the Capon power spectrum as the initial angle , and select the initial complex channel gain coefficient ;

[0103] (2) for each initial estimated angle and initial complex channel gain coefficient, determining a likelihood function of the complex channel gain coefficient and the angle;

[0104] (3) The likelihood function is maximized and optimized by gradient descent to obtain the optimal perception positioning parameters In each iteration, the gradient of the current perceptual positioning parameter estimate is calculated to update the target parameter, where the complex channel gain coefficient The gradient of is:

[0105]

[0106] in, , Respectively Iteration and , and The mobile terminals to be located are and the mobile terminal to be located Angle, is the cumulative index, is the number of iterations.

[0107] Due to the nonlinear nature of the direction vector, the angle gradient is approximately calculated. The gradient of is as follows:

[0108]

[0109]

[0110] in, , Respectively Iteration and , and The mobile terminals to be located are and the mobile terminal to be located The complex channel gain coefficient is Express Find the partial derivative.

[0111] The gradient descent method is used to update the estimated values ​​of iterative perceptual positioning parameters using the following formula:

[0112]

[0113]

[0114] in, , Respectively Iteration and , and They are and During the iteration process, if the change in the updated perception positioning parameter is less than the preset threshold, the iteration converges and the estimated value of the perception positioning parameter is output. .

[0115] By optimizing the parameters in CAML estimation using the gradient descent method, high-precision target parameter estimation can be achieved in complex multi-target perception scenarios. This process takes advantage of the initial directional sensitivity of Capon beamforming and accurately optimizes the parameters through iterative updates, thereby enhancing the perception performance of the system. Finally, after obtaining the estimated values ​​of the target parameters, the perception capability of this estimator is evaluated by comparing the RMSE of these estimates with the CRB.

[0116] Step S3: construct a Fisher information matrix based on the perception positioning parameter estimate, and obtain the lower bound of the estimation error of the perception positioning parameter from the Fisher information matrix.

[0117] After obtaining the complex channel gain coefficient and angle of the estimated target, the CRB matrix corresponding to the complex channel gain coefficient and angle of the target is obtained, and the Fisher information matrix corresponding to the complex channel gain coefficient and angle of the target is first calculated. According to the definition of the Fisher information matrix, the calculation formula of the element in the i-th row and j-th column of the matrix is:

[0118]

[0119] in, The parameter to be estimated The value of the element in the i-th row and j-th column of the Fisher information matrix, where i and j are matrix indices. represents the conjugate transpose of the matrix. , They are The i-th row and j-th column of the matrix.

[0120] The corresponding estimate The CRB matrix is ， express The inverse matrix of . The trace of this matrix is ​​used as the lower bound of the estimation error in the present invention, and its formula is expressed as:

[0121]

[0122] in, To calculate the lower bound of the estimation error, Represents the trace of the matrix.

[0123] Through the above process, the present invention constructs a Fisher information matrix for the parameters that need to be estimated in the echo obtained by the 5G AeroMACS multi-antenna base station and inverts it to finally obtain the CRB of the parameters that need to be estimated.

[0124] Step S4: Based on the channel characteristics of the communication between the 5G AeroMACS multi-antenna base station and the single-antenna communication terminal, the lower bound of the estimation error is minimized to obtain the optimal beamforming parameters.

[0125] In the previous step, the airport scene communication and navigation integrated system of the present invention obtains the multi-target perception positioning parameter estimation using the echo obtained by the 5G AeroMACS multi-antenna base station, and then obtains the corresponding estimation error lower bound CRB, which can realize the simultaneous integration of communication and perception functions and complete beamforming.

[0126] In this step, the CRB error lower bound is minimized and optimized, and the following optimization problem is constructed:

[0127]

[0128]

[0129]

[0130] in, express Under the condition of optimization to the minimum value, Indicates the conditions that are subject to constraints. is the minimum communication rate of a single-antenna communication terminal, The maximum transmit power.

[0131] On this basis, in order to solve the non-convexity of the function, by substituting And introduce , the optimization problem can be equivalently reformulated as:

[0132]

[0133]

[0134]

[0135]

[0136] in, for The inverse matrix of express Optimize to the minimum value under any condition.

[0137] At this time, since there are still non-convex quadratic constraints in the optimization problem, the reformulated problem is still non-convex. For this problem, the present invention provides an algorithm based on SCA, such as Figure 3As shown, the solution to this problem is found in an iterative way, and the original problem is approximated as a series of convex problems. The specific steps are:

[0138] Let the number of iterations be , and Respectively and The local solution point in the optimization process is The optimal solution obtained in the iteration. Based on the first-order Taylor expansion, it can be concluded that:

[0139]

[0140] Substituting this formula into the formula of the optimization problem, the non-convex quadratic constraint in the original constraint can be approximated as:

[0141]

[0142] in, Indicates any choice.

[0143] At this point, the form of the optimization problem is rewritten as:

[0144]

[0145]

[0146]

[0147]

[0148] In this case, the optimization problem is a convex function. For this optimization problem, numerical optimization tools such as CVX can be used to find the optimal solution. In each iteration, the optimal solution obtained will be used as the starting point for the next iteration.

[0149] Therefore, in order to implement the SCA-based algorithm, it is necessary to find a feasible initial point. The feasible initial point is obtained by using the semidefinite relaxation (Semidefinite Relaxation, SDR) and Gaussian randomization algorithm, and the semidefinite matrix obtained by minimizing the optimization constraints is processed by the semidefinite relaxation method, and the semidefinite matrix is ​​eigen-decomposed to select the communication precoding vector for beamforming. Satisfy ,in, is the eigenvector matrix obtained by SDR, is a random vector, The elements of are uniformly distributed on the unit circle in the complex plane. , find the vector that satisfies the transmission communication beamforming to achieve the maximum multicast rate, and use the precoding vector that achieves the maximum multicast rate as the initial point of the SCA algorithm .

[0150] set up Indicates The precoding vector of the optimization problem in the iteration The optimal solution is As an initial point, multiple iterations are performed, and when the end condition of the SCA algorithm is met, the iteration is completed.

[0151] In some embodiments, the termination condition of the SCA algorithm may be one or more of the following: the change in the value of the parameter to be estimated is less than a preset threshold, the norm of the gradient is less than a preset threshold, or the maximum number of iterations is reached.

[0152] Step S5: Use the optimal beamforming parameters to control the 5G AeroMACS multi-antenna base station to send signals, so as to complete the communication and positioning between the 5G AeroMACS multi-antenna base station and the terminal.

[0153] The optimal solution As the optimal beamforming precoding vector, it controls the phase and amplitude of the signal to be transmitted by the antenna of the 5G AeroMACS multi-antenna base station, so that the signal is enhanced in a specific direction, thereby improving the signal coverage and reception quality. Ultimately, efficient wireless communication and accurate navigation guidance are achieved at the airport.

[0154] Although the specific embodiments of the present invention depict various actions or steps in a specific order, this should be understood as requiring such actions or steps to be performed in the specific order shown or in a sequential order, or requiring all illustrated actions or steps to be performed to obtain the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

[0155] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A 5G AeroMACS base station communication and positioning integrated method, characterized in that: The following steps are involved: Step S1, establishing an airport surface communication and navigation integrated system, including a 5G AeroMACS multi-antenna base station, a plurality of single-antenna communication terminals and a plurality of mobile terminals to be located; the 5G AeroMACS multi-antenna base station communicates in real time with the single-antenna communication terminal and the mobile terminal to be located respectively; Step S2, the 5G AeroMACS multi-antenna base station receives the echo signal sent by the mobile terminal to be located; obtains an estimate of the perceived positioning parameter of the mobile terminal to be located according to the echo signal; the perceived positioning parameter includes a complex channel gain coefficient and angle ; Step S3: constructing a Fisher information matrix based on the perception positioning parameter estimation, and obtaining the estimation error lower bound of the perception positioning parameter from the Fisher information matrix; Step S4, based on the channel characteristics of the communication between the 5G AeroMACS multi-antenna base station and the single-antenna communication terminal, minimizing and optimizing the lower bound of the estimation error to obtain the optimal beamforming parameters; comprising: Step S4-1, determining the constraint conditions for minimizing the lower bound of the estimation error, specifically including: Determine the expression of the constraint condition for minimizing the lower bound of the estimation error: in, The emission covariance matrix, represents the calculation of the real part, To calculate the lower bound of the estimation error, express Optimize to the minimum value under any condition. for The inverse matrix of Indicates the conditions that are subject to constraints. is the precoding vector of 5G AeroMACS multi-antenna base station, for The conjugate transpose of is the maximum transmit power, For single antenna communication terminal The channel gain, for The conjugate transpose of is the single-antenna communication terminal index, , is the number of the single-antenna communication terminals, for The precoding vector of the iteration, for The conjugate transpose of , is the noise variance at the single-antenna communication terminal, is the minimum communication rate of a single-antenna communication terminal, It means to take any; Step S5: Use the optimal beamforming parameters to control the 5G AeroMACS multi-antenna base station to send signals, so as to complete the communication and positioning between the 5G AeroMACS multi-antenna base station and the terminal.

2. The 5G AeroMACS base station communication and positioning integrated method according to claim 1, characterized in that: Step S2 specifically includes: Step S2-1, the 5G AeroMACS multi-antenna base station receives an echo signal sent by the mobile terminal to be located from an echo channel; Step S2-2: determining an initial value of the perception positioning parameter based on a Capon beamforming power spectrum; and determining a likelihood function based on the initial value of the perception positioning parameter; Step S2-3: maximizing and optimizing the likelihood function by gradient descent to obtain optimal perceptual positioning parameters as estimators of perceptual positioning parameters.

3. The 5G AeroMACS base station communication and positioning integrated method according to claim 2, characterized in that: In step S2-1, the echo signal expression is: in, The first signal received by the 5G AeroMACS multi-antenna base station A signal, The first 5G AeroMACS multi-antenna base station A signal, is the symbol index, , is the total number of symbols in the communication symbol period, 5G AeroMACS multi-antenna base station and the mobile terminal to be located The echo channel gain between The index of the mobile terminal to be located , , is the total number of mobile terminals to be located, For the Additive white Gaussian noise of the signal.

4. The 5G AeroMACS base station communication and positioning integrated method according to claim 3, characterized in that: Step S2-2 specifically includes: For each possible target angle, the Capon power spectrum is calculated, and the angle corresponding to the peak of the Capon power spectrum is used as the initial angle , and select the initial complex channel gain coefficient ; based on and , the likelihood function is calculated according to the following expression: in, express Observe the received signal The likelihood function of Indicates the mobile terminal to be located The complex channel gain coefficient is and They are the transmit and receive steering vectors at the 5G AeroMACS multi-antenna base station antennas, for The conjugate transpose of For the mobile terminal to be located Angle, It represents the exponential calculation of the natural constant e. is the L2 norm, is the noise power at the receiving antenna.

5. The 5G AeroMACS base station communication and positioning integrated method according to claim 4, characterized in that: Step S2-3 specifically includes: Use the following expression for gradient descent to iteratively update the perception positioning parameters: in, , Respectively Iteration and , For the mobile terminal to be located Angle, , Respectively Iteration and , For the mobile terminal to be located The complex channel gain coefficient is , Respectively Iteration and , and They are and The iteration step length is represents the calculation of the real part, Express Find partial derivatives; During the iteration process, if the change in the updated perception positioning parameter is less than the preset threshold, the estimated value of the perception positioning parameter is output .

6. The 5G AeroMACS base station communication and positioning integrated method according to claim 5, characterized in that: Step S3 specifically includes: Based on the estimation of the perception positioning parameters, the complex channel gain coefficient of the target and the Fisher information matrix corresponding to the angle are calculated, and the expression is as follows: in, and are the antenna transmit and receive steering matrices, respectively, for The complex conjugate matrix of for The conjugate transpose of is the sending signal matrix, is the complex channel gain coefficient matrix, for and The parameter matrix to be estimated is composed of for The Fisher information matrix i Line j The value of the column element, i , j is the matrix index, represents the conjugate transpose, , They are No. i Row and j List; Indicates vectorizing the matrix; The Fisher information matrix i Line j The value of the column element is composed of , then the lower bound expression of the estimation error of the perception positioning parameters is: in, The emission covariance matrix, To calculate the lower bound of the estimation error, represents the trace of the matrix, is the inverse matrix of the Fisher information matrix.

7. The 5G AeroMACS base station communication and positioning integrated method according to claim 6, characterized in that: Step S4 also includes: Step S4-2: Determine the initial point of the precoding vector that satisfies the maximum multicast rate based on semi-positive relaxation and Gaussian randomization method; Step S4-3: based on the constraint condition, iteratively update the initial point of the precoding vector; when the iteration end condition is met, end the iteration and use the precoding vector after the iteration as the optimal beamforming parameter.

8. The 5G AeroMACS base station communication and positioning integrated method according to claim 7, characterized in that: Step S4-2 specifically includes: The semi-positive definite matrix obtained by processing the constraints of the minimization optimization by the semi-positive definite relaxation method is subjected to eigendecomposition and the semi-positive definite matrix is ​​selected. Satisfy ,in, is the eigenvector matrix obtained by the semidefinite relaxation method, is a random vector, The elements of are uniformly distributed on the unit circle of the complex plane; for , find the vector that satisfies the transmission communication beamforming to achieve the maximum multicast rate, and use the precoding vector that achieves the maximum multicast rate as the initial point .

9. The 5G AeroMACS base station communication and positioning integrated method according to claim 8, characterized in that: Step S4-3 specifically includes: The numerical optimization tool CVX is used to iteratively optimize the constraints. As the initial point, multiple iterations are performed. Indicates The precoding vector of the optimization problem in the iteration The solution is and When the change of is less than the preset threshold, the iteration ends and the optimal precoding vector is obtained. as the optimal beamforming parameters.

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