Multi-base station 5G AeroMACS-oriented communication and sensing integrated beam scheduling method and system

Abstract

The invention relates to a multi-base station 5G AeroMACS-oriented communication and sensing integrated beam scheduling method and system, belongs to the technical field of airport scene monitoring, solves the problem of real-time and accurate monitoring of a sensing target of an airport scene in the prior art, and comprises the following steps: step S0, providing cooperative vehicles on the airport scene, a 5G AeroMACS transceiving array antenna, a satellite positioning system and a self-speed sensing system are arranged for the cooperative vehicle, and a non-cooperative vehicle is used as a sensing target; the method comprises the following steps: S1, establishing an airport scene 5G AeroMACS communication and sensing integrated communication network; s2, establishing a measurement model; s3, constructing a track spatio-temporal evolution model; s4, obtaining a sensing target allocated to each cooperative vehicle at the current frame moment; s5, constructing a communication channel model and a sensing channel model; s6, designing a receiving and transmitting combined directional diagram of the MIMO array for each pair of cooperative vehicle and sensing target obtained through matching; and S7, realizing signal receiving and transmitting beamforming of the cooperative vehicle.

Description

Technical Field

[0001] This invention relates to the field of airport surface monitoring technology, specifically to a sensing-integrated beam scheduling method and system for multi-base station 5G AeroMACS. Background Technology

[0002] With the rapid development of the air transport industry, airport operations are expanding and flight density is increasing, making airport surface vehicle management increasingly important. Traditional monitoring systems, while providing basic monitoring data, are insufficient in terms of real-time data delivery, processing speed, and monitoring accuracy, limiting airport surface operational efficiency and safety. In particular, achieving precise vehicle location, tracking, and identification in airport surface vehicle management has become crucial for improving airport operational efficiency and ensuring safety.

[0003] Chinese invention patent application, publication number CN116113050A, entitled "Dynamic Beam Scheduling Method and Apparatus", discloses a beam scheduling method that uses a satellite communication system to execute beam scheduling commands. However, this method does not solve the problem of monitoring moving targets on the airport surface.

[0004] Chinese invention patent application, publication number CN114173417A, entitled "A Satellite Beam Scheduling Method", discloses a satellite beam scheduling method, but this method also fails to solve the problem of monitoring moving targets on the airport surface.

[0005] Therefore, this technical field needs an airport surface monitoring system that can utilize the high bandwidth and low latency characteristics provided by 5G communication technology, and achieve real-time and accurate monitoring of targets such as vehicles and aircraft on the airport surface. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a sensing-integrated beam scheduling method and system for multi-base station 5G AeroMACS. This system, through advanced waveform design and signal processing techniques, enables efficient sensing of multiple cooperative vehicles and target objects within an airport surface. The system design considers the complex and dynamic environment of the airport surface, improving robustness and reliability through optimized signal transmission and processing strategies. The key innovations of the system lie in its multi-base station architecture and sensing-integrated beam scheduling algorithm framework. This design allows the system to acquire information such as the relative distance, relative velocity, and relative angle between the antenna array and the target, thereby achieving precise positioning and tracking.

[0007] According to one embodiment of the present invention, a method for integrated sensing and beam scheduling for multi-base station 5G AeroMACS is provided, comprising the following steps: Step S0: Provide cooperative vehicles at the airport scene, equip the cooperative vehicles with 5G AeroMACS transceiver array antennas, satellite positioning systems and their own speed sensing systems, and use non-cooperative vehicles as sensing targets. Step S1: Establish an airport surface 5G AeroMACS integrated sensing communication network based on the cooperative vehicles and sensing targets, wherein the targets include cooperative vehicles and sensing targets. Step S2: Establish a measurement model, including establishing an active perception model, a satellite positioning model of the cooperative vehicle, and a speed model of the cooperative vehicle itself; perform measurements and obtain the measurement set at the current frame time. Step S3: Construct a trajectory spatiotemporal evolution model based on the Kalman filter algorithm to characterize the motion state of cooperative vehicles and perceived targets in the real coordinate system; Step S4: Match the cooperative vehicles with each target to obtain the target assigned to each cooperative vehicle at the current frame time. Step S5: In the scenario of airport surface networking, construct the communication channel model and the sensing channel model based on geometric relationships. Step S6: For each pair of cooperative vehicles and targets matched in step S4, design the transmit and receive joint pattern of the MIMO array, solve the transmit and receive joint pattern, and obtain the transmit precoding matrix of the communication data stream, the transmit precoding matrix of the auxiliary sensing data stream, the communication receive weighting vector, and the value of the sensing receive weighting vector. Step S7: Based on the results obtained in step S6, signal transmission and reception beamforming of the cooperative vehicle is implemented to achieve real-time monitoring of targets on the scene; and it is confirmed whether a stop command has been received. If so, the program ends; otherwise, it returns to step S2 and obtains the measurement result based on the reflected echo of the target received by the signal transmission and reception beamforming of the cooperative vehicle, and substitutes it into the next round of perception matching.

[0008] Optionally, in step S0: the absolute position information of the cooperative vehicle is obtained through a satellite positioning system, and the speed sensing system includes a speedometer integrated into the control system of the cooperative vehicle.

[0009] Optionally, in step S1: the number of cooperative vehicles is set to... The number of perceived targets is In the current frame of the current round At time , the target's true state vector is:

[0010] in, Indicates the index of the target. Indicates the first The horizontal position of the target Indicates the first The vertical position of the target Indicates the first The horizontal velocity of the target Indicates the first The vertical velocity of each target is obtained, and ( As the first The true location of each target, ( As the first The true speed of each target.

[0011] Optionally, step S2 specifically includes the following steps: Step S2.1, construct the active perception model; Step S2.2: Based on the satellite positioning system equipped in the cooperative vehicles, construct a satellite positioning model for the cooperative vehicles; Step S2.3: Based on the speed sensing system of the cooperative vehicle, construct the speed model of the cooperative vehicle. Step S2.4: Based on the target reflected echo of the sensing beam emitted in the previous iteration, perform corresponding signal processing and target parameter estimation to obtain the measured values ​​of the position and velocity of the sensing target at the current frame time of the current iteration. For the first iteration, preset the measured values ​​of the position and velocity of the sensing target. Step S2.5: Obtain measurement results through the constructed active perception model, the satellite positioning model of the cooperative vehicle, and the self-velocity model of the cooperative vehicle to obtain the measurement set at the current frame time, including the measurement values ​​of the relative distance, relative speed, and relative angle between the cooperative vehicle and the target at the current frame time, and the measurement results of the target; wherein, the measurement results of the target are the measurement values ​​of the target's position and speed, including the measurement values ​​of the position and speed of the cooperative vehicle, as well as the measurement values ​​of the perceived target's position and speed.

[0012] Optionally, the trajectory spatiotemporal evolution model in step S3 includes: a Kalman prediction model, a trajectory filtering model, and a trajectory prediction model.

[0013] Optionally, step S3 specifically includes the following steps: Step S3.1: For all targets, construct a Kalman prediction model based on the target's trajectory state prediction and trajectory prediction covariance matrix from the first two rounds, and obtain the target's one-step trajectory state prediction and trajectory prediction covariance matrix. Step S3.2: Based on the measurement association results with the minimum Mahalanobis distance, and by constructing a track filtering model through the track update process, the target's track state estimate and track filtering covariance matrix are obtained. Step S3.3: Based on the target's trajectory state estimation and trajectory filtering covariance matrix, construct a trajectory prediction model and obtain the target's trajectory state prediction and trajectory prediction covariance matrix for the current round. Step S3.4: Judge the detection status of all targets. Compare the number of consecutive undetected frames for each target with a preset exit frame threshold. If there is a target whose number of consecutive undetected frames is greater than or equal to the preset exit frame threshold, it is determined that the target has disappeared and the target exits the track filtering loop. For targets that meet the condition that the number of consecutive undetected frames is less than the preset exit frame threshold, save the track state prediction and track prediction covariance matrix of the current round as the track filtering result and track prediction result of the target.

[0014] Optionally, step S4 specifically includes the following steps: Step S4.1: Based on the target trajectory state prediction and trajectory prediction covariance matrix obtained in step S3, sample the trajectory state estimation of each cooperative vehicle and each perceived target, sample a set number of sigma points respectively, and obtain the state vector and position of each sampled sigma point. Step S4.2: Based on the positions of the sigma points of each cooperative vehicle and the positions of the sigma points of each perceived target, obtain the filtering distance and filtering angle of the sigma points of the cooperative vehicles and the sigma points of the targets. Step S4.3: Calculate the Cramer-Rao lower bound of the sigma points of the cooperative vehicles and the sigma point pairs of the targets, and obtain the weighted objective function of each target relative to each cooperative vehicle. ,in, Indicates the index of the target. An index indicating the vehicles used in cooperation; Step S4.4: To achieve perception of all targets, a perception target matching optimization problem is constructed. A 0-1 integer programming problem model is adopted, and the optimization variable is defined as the matching matrix. When the matching items in the matching matrix are... Time cooperation vehicles Towards the target Beam pointing The time indicates that no beam pointing is formed; To minimize the total perception cost, and with constraints ensuring that each target can be perceived by the cooperating vehicles, the objective function is established as follows:

[0015]

[0016]

[0017] Solving the above objective function yields the objective assigned to each cooperative vehicle at the current frame time of the previous round.

[0018] Optionally, step S5 specifically includes the following steps: Step S5.1: Based on the target trajectory prediction covariance matrix obtained in step S3, the target position error prediction covariance matrix is ​​obtained. Step S5.2: Obtain the upper bound of the communication channel uncertainty; Step S5.3: Obtain the upper bound of the perceived channel uncertainty; Step S5.4: Construct the communication channel model and the sensing channel model respectively to obtain the communication channel matrix and the sensing channel matrix.

[0019] Optionally, step S6 specifically includes the following steps: Step S6.1, for each pair of cooperating vehicles forming the beam direction in step S4 With the goal The transmit and receive joint pattern of the MIMO array is designed, and the transmit signal waveform after transmit precoding is obtained as follows:

[0020] in, The emission precoding matrix for auxiliary sensing data stream, To assist in sensing data streams, For the transmission precoding matrix of the communication data stream, This refers to communication data streams, including auxiliary sensing data streams. and communication data stream This is the default value; Furthermore, the transmitted signal waveform satisfies:

[0021] in, The maximum total transmit power of the array antenna is determined by the configuration of the 5G AeroMACS transceiver antenna array assembled on the cooperative vehicle. Step S6.2, construct the signal-to-interference-plus-noise ratio (SNR) for communication and the signal-to-noise ratio (SNR) for sensing, respectively:

[0022]

[0023] in, The signal-to-interference-plus-noise ratio (SIR / NOT) for communication. The perceived signal-to-noise ratio. This represents the complex conjugate transpose of the communication reception weighted vector. This represents the complex conjugate transpose of the sensing and receiving weighted vector. Indicates communication reception noise. Indicates perceived received noise. For communication channel matrix, The channel matrix for perception; Step S6.3, the design of the joint transmit and receive pattern for communication and sensing is constructed as the following optimization problem:

[0024]

[0025] in, The weighting coefficients and ; Step S6.4: Solve the optimization problem to obtain the transmission precoding matrix of the communication data stream, the transmission precoding matrix of the auxiliary sensing data stream, the communication reception weighting vector, and the value of the sensing reception weighting vector.

[0026] According to another embodiment of the present invention, a sensing-integrated beam scheduling system for multi-base station 5G AeroMACS is provided, comprising: The cooperative vehicle used at the airport is equipped with a 5G AeroMACS transceiver array antenna, a satellite positioning system, and its own speed sensing system. The satellite positioning system acquires the vehicle's absolute position information, and the speed sensing system includes a speedometer integrated into the vehicle's control system. Targets to be perceived, including non-cooperative vehicles and aircraft in the airport scene; The cooperative vehicles and the sensing targets constitute the airport surface 5G AeroMACS integrated sensing communication network. The cooperative vehicle is equipped with a measurement module, a trajectory spatiotemporal evolution module, and a perception target matching module.

[0027] Compared with the prior art, the integrated sensing beam scheduling method and system for multi-base station 5G AeroMACS provided according to the embodiments of the present invention has at least the following beneficial effects.

[0028] (1) An innovative solution for airport surface safety management and operation is provided through innovative beam scheduling method and perception cost matrix design.

[0029] (2) It has improved the accuracy and efficiency of monitoring, provided strong technical support for the monitoring and management of airport surfaces, and has significant social and economic value.

[0030] (3) It provides more efficient and intelligent monitoring and management of the airport, providing a solid guarantee for the safety and efficiency of airport operations. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the accompanying drawings. The accompanying drawings are schematic and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of a communication network for a multi-base station 5G AeroMACS integrated beam scheduling method provided according to the first embodiment of the present invention.

[0033] Figure 2 A flowchart of a sensing-integrated beam scheduling method for multi-base station 5G AeroMACS provided according to a first embodiment of the present invention.

[0034] Figure 3 A scheduling algorithm framework diagram for a multi-base station 5G AeroMACS integrated beam scheduling method provided according to the first embodiment of the present invention.

[0035] Figure 4 A scenario diagram illustrating an embodiment of the inductive beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention.

[0036] Figure 5 A schematic diagram of the spatiotemporal evolution results of a trajectory in an embodiment of the integrated beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention.

[0037] Figure 6a The sensing target matching result is shown in an embodiment of the sensing-integrated beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention.

[0038] Figure 6b The sensing target matching result is shown in an embodiment of the sensing-integrated beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention.

[0039] Figure 7 The beam pattern of the cooperative vehicle 1 in an embodiment of the integrated beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention.

[0040] Figure 8 The beam pattern of the cooperative vehicle 2 in an embodiment of the integrated beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention. Detailed Implementation

[0041] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein. Therefore, the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0043] The following describes in detail, with reference to the accompanying drawings, an integrated sensing beam scheduling method for multi-base station 5G AeroMACS according to an embodiment of the present invention.

[0044] Symbol explanation: Indicates the number of vehicles involved in the cooperation; Indicates the number of perceived targets; Indicates the index of the target, and ; It is the frame number and represents the current frame of the current round; Indicates the first The true state vector of each target; Indicates the first The horizontal position of each target; Indicates the first The vertical position of the target; Indicates the first The horizontal velocity of the target; Indicates the first The vertical velocity of the target; Indicating cooperative vehicles With the goal The measured value of the relative distance; Indicates the first The horizontal position of the cooperative vehicles; Indicates the first The horizontal position of a perceived target; Indicates the first The vertical position of each cooperating vehicle; Indicates the first The vertical position of a perceived target; Indicating cooperative vehicles With the goal The measured value of the relative velocity; Indicates the first The horizontal speed of the cooperative vehicles; Indicates the first The horizontal velocity of the perceived target; Indicates the first Vertical speed of each cooperating vehicle; Indicates the first The vertical velocity of the perceived target; Indicating cooperative vehicles With the goal The measured value of the relative angle; Indicates the index of the cooperating vehicles, and ; The index represents the perceived target, and ; Indicates measurement noise; Indicates the number obtained by the satellite system The horizontal position of each cooperative vehicle; Indicates the number obtained by the satellite system Vertical position of each cooperative vehicle; Indicates the first The speedometers of the cooperative vehicles display their speeds; Represents the state transition matrix; Indicates the first The first goal Frame-based trajectory state prediction; Indicates the first The first goal The covariance matrix for trajectory prediction in frames; Indicates the first The first goal The trajectory status of a frame is predicted in one step; Indicates the first The first goal The covariance matrix of the frame's trajectory is predicted in one step; Represents the identity matrix; Let be the covariance matrix of the process noise; For frame interval; Represents the Kalman gain matrix; Indicates the first The first goal The trajectory filtering covariance matrix of the frame; Indicates the first The first goal Frame track state estimation; Indicates the first The first goal Frame measurement results; For the first The covariance matrix of the measurement noise of each target; For measuring all state quantities in the model The Jacobian matrix; Indicates the first The index of the sigma points sampled for the trajectory state estimation of each cooperative vehicle and ; Indicates the first The index of the sigma points sampled for the trajectory state estimation of each perceived target and ; Indicates the first The first cooperative vehicle The horizontal position of each sigma point; Indicates the first The first cooperative vehicle The state vector of sigma points; Indicates the first The first cooperative vehicle Vertical position of each sigma point; Indicates the first The first perceived target The horizontal position of each sigma point; Indicates the first The first perceived target The state vector of sigma points; Indicates the first The first perceived target Vertical position of each sigma point; Indicates the first The first perceived target The state vector of sigma points; Indicates the first The first cooperative vehicle The state vector of a frame; Indicates the first The first cooperative vehicle The covariance matrix for trajectory prediction in frames; Indicates the first The first perception target The state vector of a frame; Indicates the first The first perception target The covariance matrix for trajectory prediction in frames; This represents the sampling count term at the sigma point, and ; Indicates the first The first goal The sigma point at the th Horizontal position of the frame; Indicates the first The first goal The sigma point at the th The vertical position of the frame; Indicates the first The index of the sigma point sampled for the trajectory state estimation of each target and ; Indicates the first The first cooperative vehicle The sigma point and the first The first goal The filtering distance of sigma points; Indicates the first The first cooperative vehicle The sigma point and the first The first goal The filtering angle at sigma points; Indicates the first The and the first The lower bound of Clamerlow's distance from each sampling point; Indicates the first The and the first The Cramero lower bound of the radial velocity at each sampling point; Indicates the first The and the first The lower bound of Clamerlow's angle at each sampling point; The lower bound of Cramérod represents the distance; Cramer-Rao lower bound for radial velocity; Cramérod's lower bound for angles; This represents the distance sensing factor defined by the signal waveform; The speed sensing factor represents the signal waveform definition; The angle perception factor represents the definition of the signal waveform; This refers to the transmission power. Represents the perception cost matrix; Indicate target Compared to cooperative vehicles The weighted objective function; Represents the weighting coefficients for distance; Weighting coefficients representing radial velocity; Weighting coefficients representing angles; Represents the matching matrix; This represents the matching items in the matching matrix; Indicates the waveform of the transmitted signal; For communication data stream; For auxiliary sensing data streams; The precoding matrix for transmitting communication data streams; The transmission precoding matrix for auxiliary sensing data streams; This represents the maximum total transmit power of the array antenna; This refers to the channel matrix for communication. The channel matrix for perception; This represents the mean of the communication channel matrix. The mean of the perceived channel matrix; Represents the error of the communication channel matrix; This represents the error in the sensing channel matrix; This represents the weighted parameter matrix representing the upper bound of communication channel uncertainty; This represents the weighted parameter matrix representing the upper bound of the uncertainty of the sensing channel; This is the upper bound of the channel uncertainty in communication; This is the upper bound of the perceived channel uncertainty; Indicate target Position error prediction covariance matrix; Represents the complex conjugate transpose of the communication reception weighted vector; This represents the complex conjugate transpose of the sensing and receiving weighted vector; The signal-to-interference-plus-noise ratio (SINR) for communication; The perceived signal-to-noise ratio; Indicates received noise in communication; Indicates perceived received noise; The weighting coefficients and .

[0045] like Figure 1As shown, the communication network constructed by the integrated sensing beam scheduling method for multi-base station 5G AeroMACS according to the first embodiment of the present invention includes multiple 5G AeroMACS mobile base stations equipped with 5G AeroMACS transceiver antenna arrays provided on the airport surface. In this embodiment, cooperative vehicles are equipped with 5G AeroMACS transceiver antenna arrays and satellite positioning systems. The cooperative vehicles serve as 5G AeroMACS mobile base stations, and non-cooperative vehicles, aircraft, etc., are used as sensing targets. The cooperative vehicles and sensing targets are used as targets. In the communication network constructed by the integrated sensing beam scheduling method for multi-base station 5G AeroMACS of this embodiment, advanced waveform design technology is used to form an integrated sensing beam (i.e., an integrated sensing beam) pointing towards the cooperative vehicles and sensing targets, thereby achieving efficient sensing of multiple cooperative vehicles and sensing targets on the airport surface. Each cooperative vehicle can transmit and receive the integrated sensing beam, and through precise signal processing, the positioning, identification, and tracking of targets can be achieved. The targets may include cooperative vehicles and sensing targets. The design of this communication network takes into account the complex and dynamic environment of airports, improving system robustness and reliability through optimized signal transmission and processing strategies. Furthermore, the network exhibits excellent scalability, allowing for flexible addition or adjustment of the number and location of 5G AeroMACS mobile base stations (i.e., cooperative vehicles) to meet the sensing requirements of different scenarios, based on the actual needs of the airport. This integrated sensing and beamschewing method for multi-base station 5G AeroMACS provides an innovative solution for airport safety management and operation.

[0046] like Figure 2 and Figure 3As shown, the scheduling algorithm framework in the sensing-integrated beam scheduling method for multi-base station 5G AeroMACS provided in the first embodiment of the present invention, when the sensing-integrated network of 5G AeroMACS at the airport surface serves the communication and perception of surface vehicles, since each sensing target is in real-time motion, this embodiment comprehensively considers multiple cooperating vehicles acquiring their own and the sensing targets' position and speed measurements in parallel: Based on the active sensing function of the 5G AeroMACS sensing-integrated mobile base station's Multiple Input Multiple Output (MIMO) wireless system, information such as the relative distance, relative speed, and relative angle between the 5G AeroMACS transceiver antenna array equipped on the cooperating vehicle and the sensing target can be obtained. The relative distance is estimated through signal transmission and reception delay, the relative speed through Doppler frequency offset of the received signal, and the relative angle through phase difference estimation of the received signal reaching the 5G AeroMACS transceiver array antenna. The satellite positioning system equipped on the cooperating vehicles is a vehicle-mounted satellite positioning system, which acquires the absolute position information of the cooperating vehicles by capturing navigation signals broadcast in real time by satellites. The cooperative vehicles can also be equipped with their own speed sensing system, which uses wheel speed sensors and other devices integrated into the control system of the cooperative vehicles to obtain the absolute speed information of the cooperative vehicles in real time.

[0047] refer to Figures 1 to 3 The integrated sensing beam scheduling method for multi-base station 5G AeroMACS provided by the first embodiment of the present invention includes the following steps.

[0048] Step S0 involves providing a cooperative vehicle at the airport, equipped with a 5G AeroMACS transceiver array antenna (hereinafter referred to as the array antenna), a satellite positioning system, and its own speed sensing system, with non-cooperative vehicles as the sensing targets. The absolute position information of the cooperative vehicle is obtained through the satellite positioning system, and the speed sensing system includes a speedometer integrated into the control system of the cooperative vehicle. This speedometer can be a wheel speed sensor. Optionally, the sensing targets may also include aircraft at the airport. The cooperative vehicle configured as described above serves as a 5G AeroMACS mobile base station.

[0049] Step S1: Establish an airport surface 5G AeroMACS integrated sensing communication network based on the cooperative vehicles and sensing targets. The targets include the cooperative vehicles and sensing targets. The number of cooperative vehicles is set to [number missing]. The number of perceived targets is In the current frame of the current round At that moment, the target The true state vector is:

[0050] in, Indicates the index of the target and , Indicates the first The horizontal position of the target Indicates the first The vertical position of the target Indicates the first The horizontal velocity of the target Indicates the first The vertical velocity of the target. Here, the horizontal and vertical directions are mutually perpendicular directions on the plane of the target's motion. The horizontal direction can be, for example... Figure 4 and Figure 5 The x-axis direction in the diagram; the vertical direction can be, for example, Figure 4 and Figure 5 The y-axis direction in the equation. Therefore, we can obtain that, ( As the first The true location of each target, ( As the first The true speed of the target This is the frame number, representing the current frame of the current round. In this implementation, each frame represents a moment in time, and the frame interval can be set as needed. For example, in one example, the first frame can be set. Corresponding to time 0.1s, the second frame Corresponding to time 0.2s, the third frame This corresponds to the 0.3s mark, and so on.

[0051] More specifically, the true state vector of the target can be represented as the true state vector of the cooperative vehicle and the true state vector of the perceived target, respectively. Indicates the index of the cooperating vehicles, and , Indicates the first The horizontal position of the cooperative vehicles Indicates the first Vertical position of each cooperating vehicle Indicates the first The horizontal speed of the cooperative vehicles Indicates the first The vertical speed of the cooperating vehicles, ( As the first The actual location of each cooperating vehicle, As the first The actual speed of each cooperative vehicle; The index represents the perceived target, and , Indicates the first The horizontal position of a perceived target Indicates the first The vertical position of a perceived target. Indicates the first The horizontal velocity of a perceived target Indicates the first The vertical velocity of the perceived target, ( As the first The true location of a perceived target, ( As the first The actual speed of each perceived target. Targets include cooperative vehicles and perceived targets, in... At that time, the first The first goal and the first One corresponding cooperative vehicle; in At that time, the first The first goal and the first Each perception target corresponds to one.

[0052] The above processing yields the true state vectors of all targets at the current frame time.

[0053] Step S2 involves establishing a measurement model, including an active sensing model, a satellite positioning model for the cooperative vehicles, and a speed model for the cooperative vehicles themselves. Measurements are then taken to obtain the measurement set for the current frame. Specifically, a cooperative vehicle's array antenna transmits a signal while simultaneously acquiring reflected echoes from the target (including both the cooperative vehicle and the sensing target). After sampling and downsampling to baseband, the relative distance, speed, and angle of the cooperative vehicle relative to the target are estimated using a transmit / receive correlation method, thus establishing the active sensing model. The cooperative vehicle uses its equipped satellite positioning module to obtain its own position estimate, thereby establishing its satellite positioning model. Finally, the cooperative vehicle uses its speedometer to obtain the displayed speed, establishing its own speed model. Step S2 specifically includes the following steps.

[0054] Step S2.1, Construct an active perception model:

[0055]

[0056]

[0057] in, Indicates the index of the cooperative vehicle and ,symbol Represents the measured value. Indicating cooperative vehicles With the goal The measured value of the relative distance, Indicates the first The horizontal position of the cooperative vehicles Indicates the first Vertical position of each cooperating vehicle Indicating cooperative vehicles With the goal The measured value of the relative velocity, Indicates the first The horizontal speed of the cooperative vehicles Indicates the first The vertical speed of each cooperating vehicle Indicating cooperative vehicles With the goal The measured value of the relative angle, This represents measurement noise.

[0058] Step S2.2: Based on the satellite positioning system equipped in the cooperative vehicles, construct a satellite positioning model for the cooperative vehicles:

[0059]

[0060] in, The number obtained by the satellite positioning system The horizontal position of each cooperative vehicle. The number obtained by the satellite positioning system The vertical positions of the cooperative vehicles. Therefore, the measured values ​​of the positions of the cooperative vehicles can be obtained as ( , ).

[0061] Step S2.3: Based on the speed sensing system of the cooperating vehicle, construct the speed model of the cooperating vehicle:

[0062] in, Indicates the first The speedometers of the cooperating vehicles display the speed. Therefore, the measured speed value can be obtained. .

[0063] Step S2.4: Based on the target reflected echo of the sensing beam emitted in the previous iteration, perform corresponding signal processing and target parameter estimation to obtain the target parameters of each sensing target in the current iteration. The frame contains measurements of position and velocity. For the first iteration, the measured values ​​of position and velocity of the target can be preset.

[0064] Step S2.5: Measurement results are obtained through the constructed active perception model, the satellite positioning model of the cooperative vehicle, and the self-velocity model of the cooperative vehicle. This yields the measurement set for the current frame, including measurements of the relative distance, relative velocity, and relative angle between the cooperative vehicle and the target, the position and velocity of the cooperative vehicle, and the position and velocity of the perceived target. Specifically, based on the position and velocity measurements of the cooperative vehicle and the perceived target, the measurement results for each target are obtained, denoted as the [missing value]. The first goal Frame measurement results , including the The first goal in The measurements of the frame's position and velocity.

[0065] Through the above steps, we obtain the position and velocity measurements of all targets at the current frame time, as well as the relative distance, relative velocity, and relative angle measurements between each cooperating vehicle and all targets.

[0066] Step S3: Construct a trajectory spatiotemporal evolution model based on the Kalman filter algorithm to characterize the motion state of the cooperative vehicle or the sensed target in the real coordinate system. The trajectory spatiotemporal evolution model is implemented based on the Kalman filter algorithm and includes two steps: trajectory filtering and trajectory prediction, obtaining the trajectory filtering result and the trajectory prediction result of the motion state of the cooperative vehicle or the sensed target. The trajectory filtering is based on the Kalman filter algorithm... Frame track state estimation and the Frame measurement results Output the first Frame track state estimation The process. This first... Frame measurement results The target obtained in the previous step S2 (i.e., the cooperative vehicle or the perceived target) in the [missing information] phase... The measurement results of the frame, including the target (i.e., the cooperative vehicle or the perceived target) obtained in step S2.4, are in the frame. The position and velocity measurements of the frame. Track prediction is based on the first frame. Frame track state estimation Output the first Frame track status The process. Step S3 specifically includes the following steps.

[0067] Step S3.1: For all targets, construct a Kalman prediction model based on the target's trajectory state prediction and trajectory prediction covariance matrix from the previous two rounds, and obtain the target's one-step trajectory state prediction and trajectory one-step prediction covariance matrix. The Kalman prediction model is constructed as follows, based on the current frame... The Kalman prediction model is constructed according to one of the following three methods to make the judgment.

[0068] Method 1, when In the first iteration, based on the target's initial state and the preset target initial trajectory filter covariance matrix, the Kalman prediction model is constructed as follows:

[0069]

[0070] in, Indicates the first The first goal The trajectory state of a frame is predicted in one step. Indicates the first The first goal The covariance matrix of the frame's trajectory is predicted in one step. For the first The initial state of each target and , For the first The initial state of each cooperative vehicle and , For the first Initialization trajectory filtering covariance matrix for each target.

[0071] Method 2, when At that time, based on the target's initial state and the preset target initial trajectory filter covariance matrix, the Kalman prediction model is constructed as follows:

[0072]

[0073]

[0074] In this second iteration, the first... Initialized trajectory filtering covariance matrix for each target Set as a unit array, For the preset first The covariance matrix of the noise in the target process For frame interval, Represents the state transition matrix. express The identity matrix. In the first iteration, the th... Initialized trajectory filtering covariance matrix for each target Set as a unit array. The frame interval is... It can be set as needed, for example, it can be set to: 0.1 seconds, 1 second, 2 seconds, 3 seconds, etc.

[0075] Method 3, when At that time, based on the target's trajectory state estimate and trajectory filtering covariance matrix obtained from the first two iterations, the Kalman prediction model is constructed as follows:

[0076]

[0077]

[0078] in, Indicates the first The first goal The trajectory state of a frame is predicted in one step. Indicates the first The first goal The covariance matrix of the frame's trajectory is predicted in one step. For the first The first goal Frame track state estimation, For the first The first goal The covariance matrix of the frame's trajectory filtering.

[0079] Using this Kalman prediction model, based on the first two rounds (i.e., the ( ) round before last), The first frame obtained through iteration The first goal Frame track state estimation , and the The first goal Frame trajectory filtering covariance matrix The first iteration of this round was obtained. The first goal One-step prediction of the trajectory state of a frame , and the The first goal The covariance matrix of the frame's trajectory is predicted in one step. .

[0080] Step S3.2: Based on the measurement association results with the minimum Mahalanobis distance, and using the target track filtering estimate obtained from the first two iterations, as well as the target track state one-step prediction and track one-step prediction covariance matrix obtained from step S3.1, the track filtering model is constructed through the following track update process:

[0081]

[0082]

[0083] in, Indicates the first The first goal The Kalman gain matrix of the frame. Indicates the first The first goal The covariance matrix of the frame's trajectory is predicted in one step. Indicates the first The first goal The trajectory state of a frame is predicted in one step. Indicates the first The first goal Frame measurement results, For the first The covariance matrix of the measurement noise of each target For measuring all state quantities in the model Jacobian matrix, express The transpose of the matrix, This represents the identity matrix. It can be a preset value. It can be calculated using Jacobi.

[0084] Using this trajectory filtering model, based on the results obtained from the first two iterations... The first goal Frame track filtering estimation The first step obtained in step S3.1 of this iteration The first goal One-step prediction of the trajectory state of a frame , and the The first goal The covariance matrix of the frame's trajectory is predicted in one step. And the first step obtained in step S2 of the previous round The first goal Frame measurement results , obtained the The first goal Frame track state estimation , and the The first goal Frame trajectory filtering covariance matrix .

[0085] In the first iteration, namely At that time, the first The first goal Frame track filtering estimation , using the The initial state of each target and , For the first The initial state of each cooperative vehicle and ; and using the preset first Initial measurement results of each target Similar to the first iteration, in the second iteration, i.e. At that time, the first The first goal Frame track filtering estimation , using the The initial state of each target and , For the first The initial state of each cooperative vehicle and ; where the first iteration yielded the ; Measurement results of the first frame of each target In the third iteration, that is... At that time, the first The first goal Frame track filtering estimation , using the The track filtering estimate of each target is obtained in the first iteration, and so on in subsequent iterations.

[0086] Step S3.3: Based on the target's trajectory state estimate and trajectory filtering covariance matrix obtained in step S3.2, the trajectory prediction model is constructed as follows:

[0087]

[0088] in, Indicates the first The first goal Frame trajectory state prediction, Indicates the first The first goal The covariance matrix for trajectory prediction of frames.

[0089] Based on the trajectory prediction model, the first iteration obtained in step S3.2 of this iteration... The first goal Frame track state estimation , and the The first goal Frame trajectory filtering covariance matrix , obtained the The first goal Frame track state prediction , and the The first goal Frame trajectory prediction covariance matrix This serves as the result of this iteration. Thus, the goal achieved in one iteration is... The frame's trajectory state prediction and the trajectory prediction covariance matrix.

[0090] Step S3.4: Assess the detection status of all targets by comparing the number of consecutive undetected frames for each target with a preset exit frame threshold. Targets with an undetected frame count greater than or equal to the preset exit frame threshold are considered eliminated and exit the trajectory filtering loop. For targets whose undetected frame count is less than the preset exit frame threshold, save the current round's trajectory state prediction and trajectory prediction covariance matrix as the target's trajectory filtering and prediction results. For example, if the... If the number of consecutive frames in which a target is not detected is greater than or equal to a preset exit frame threshold, then the target is considered to have left the detection range, its data is not saved, and it will no longer be detected, thus exiting the detection phase. The trajectory filtering loop of the target is cycled, and it is determined that the target is eliminated; otherwise, if the target is eliminated... If the number of consecutive frames in which a target is not detected is less than a preset exit frame threshold, the resulting [frame number] will be [the next frame]. The first goal Frame track state prediction , and the The first goal Frame trajectory prediction covariance matrix Save the obtained trajectory filtering results and trajectory prediction results of the current target.

[0091] The Kalman prediction model, trajectory filtering model, and trajectory prediction model constructed in this step together constitute the trajectory spatiotemporal evolution model. This trajectory spatiotemporal evolution model provides trajectory filtering and trajectory prediction results for integrated beam scheduling of multi-base station 5G AeroMACS.

[0092] Step S4: Match the cooperative vehicles with the known targets to obtain the target assigned to each cooperative vehicle at the current frame time. Due to the existence of [unclear - possibly related to airport surface 5G AeroMACS sensor-integrated communication network]... One cooperative vehicle and To simplify the algorithm's complexity, cooperative vehicles can be matched with known targets to ensure that cooperative vehicles at the airport can detect targets. Step S4 specifically includes the following steps.

[0093] Step S4.1: In actual calculation, since the true distance and angle are unknown, the perceived target obtained in step S3 is used. and cooperative vehicles The predicted trajectory results are used to approximate the actual distance and angle. First, the trajectory state estimates of each cooperative vehicle and each perceived target are sampled. Several sigma points are used to approximate the posterior distribution. The state vector and position of each sampled sigma point are obtained using the following formula:

[0094] , ,

[0095] , , in, Indicates the first The index of the sigma points sampled for the trajectory state estimation of each cooperative vehicle and , Indicates the first The first cooperative vehicle The sigma point at the th Horizontal position of the frame Indicates the first The first cooperative vehicle The sigma point at the th The state vector of a frame. Indicates the first The first cooperative vehicle The sigma point at the th The vertical position of the frame. Indicates the first The index of the sigma points sampled for the trajectory state estimation of each perceived target and , Indicates the first The first perceived target The horizontal position of each sigma point Indicates the first The first perceived target The sigma point at the th The state vector of a frame. Indicates the first The first perceived target The sigma point at the th The vertical position of the frame. Indicates the first The first perceived target The sigma point at the th The state vector of a frame. Indicates the first The first cooperative vehicle The state vector of a frame. Indicates the first The first cooperative vehicle The covariance matrix of the trajectory prediction of the frame. Indicates the first The first perception target The state vector of a frame. Indicates the first The first perception target The trajectory prediction covariance matrix of the frame. Among them, the first... The covariance matrix for trajectory prediction of each cooperative vehicle , and the The covariance matrix of trajectory prediction for each perceived target Based on the objective of step S3.3, the first Frame trajectory prediction covariance matrix Obtained. And among them, the first The state vector of each cooperative vehicle , and the The state vector of the sensing target Based on step S3.3 The first goal Frame track state prediction get.

[0096] Based on the above results, we obtain the first... The first goal The sigma point at the th Horizontal position of the frame: ; No. The first goal The sigma point at the th Vertical position of the frame: ; in, Indicates the index of the target and , Indicates the first The index of the sigma point sampled for the trajectory state estimation of each target and .

[0097] Step S4.2, the The first cooperative vehicle The sigma point and the first The first goal Filtering distance at sigma points and filtering angle for:

[0098]

[0099] in, This indicates the calculation of the arctangent value.

[0100] Step S4.3, calculate the first... The first cooperative vehicle The sigma point and the first The first goal Clameros lower bound for each sigma point:

[0101]

[0102]

[0103] in, The lower bound of Crameros represents the distance. The lower bound of Cramer-Rao for radial velocity. The lower bound of Cramérod for angles. The distance sensing factor represents the signal waveform definition. The speed sensing factor represents the signal waveform definition. The angle perception factor represents the definition of the signal waveform. This is the preset transmit power of the array antenna. Among them, These are hyperparameters or parameters related to system configuration. Radial is the straight line from the target to the cooperating vehicle, and radial velocity is the velocity projection from the target to the cooperating vehicle.

[0104] Next, calculate The expectation of the lower bound of Cramer-Rao at each sampled sigma point:

[0105]

[0106]

[0107] in, The lower bound of Crameros represents the distance. The lower bound of Cramer-Rao for radial velocity, The lower bound of Cramérod for angles. The Cramérault lower bound function represents the distance. Cramer-Rao lower bound function representing radial velocity, Cramer-Rao lower bound function representing angle, This indicates the mathematical expectation calculation.

[0108] To describe the overall sensing performance for distance, velocity, and angle, this implementation uses a sensing cost matrix. The target is obtained by setting it as a weighted form of the perceived Cramer-Rao Bound (CRB). Compared to cooperative vehicles Weighted objective function:

[0109] in, Indicate target Compared to cooperative vehicles The weighted objective function is the perception cost matrix. Item, This represents the weighting coefficient for the preset distance. This represents the weighting factor for the preset radial velocity. This represents the weighting coefficient for the preset angle.

[0110] Step S4.4: To achieve perception of all targets, a perception target matching optimization problem is constructed, using a 0-1 integer programming problem model, with the optimization variable defined as the matching matrix. When the matching item in the matching matrix Time cooperation vehicles Towards the target Beam pointing The time indicates that no beam pointing is formed.

[0111] To minimize the total perception cost, and with constraints ensuring that each target can be perceived by the cooperating vehicles, the objective function is established as follows:

[0112]

[0113]

[0114] Solving the above objective function yields the objective assigned to each cooperative vehicle at the current frame time.

[0115] Step S5: In the scenario of airport surface networking, a communication channel model and a sensing channel model can be constructed based on geometric relationships. Similar to the process of matching the target to be sensed, the channel model needs to be based on the posterior distribution estimation of the track filtering, thus introducing a certain degree of uncertainty. Step S5 specifically includes the following steps.

[0116] Step S5.1: Based on the target trajectory prediction covariance matrix obtained in step S3, the target is obtained. Position error prediction covariance matrix: + .

[0117] Optionally, The first one that can be obtained from step S3 The first goal Frame trajectory prediction covariance matrix Obtained. Among them, Indicating cooperative vehicles The Frame trajectory prediction covariance matrix Indicate target The Frame trajectory prediction covariance matrix.

[0118] Step S5.2, obtain the upper bound of the communication channel uncertainty:

[0119] in, This represents the weighted parameter matrix of the upper bound of the communication channel uncertainty.

[0120] Step S5.3, obtain the upper bound of the perceived channel uncertainty:

[0121] in, This represents the weighted parameter matrix of the upper bound of the uncertainty of the sensing channel.

[0122] Step S5.4, construct the communication channel model and the sensing channel model respectively:

[0123]

[0124] in, For communication channel matrix, For the perceived channel matrix, The mean of the communication channel matrix. The mean of the perceived channel matrix. This represents the error in the communication channel matrix. This represents the error in the sensing channel matrix.

[0125] Step S6: After completing the target matching, for each pair of cooperative vehicles and targets whose beam pointing was obtained in Step S4, design the joint transmit and receive pattern of the MIMO array, and solve the joint transmit and receive pattern to obtain the transmit precoding matrix of the communication data stream, the transmit precoding matrix of the auxiliary sensing data stream, the communication receive weighting vector, and the value of the sensing receive weighting vector. This step S6 is performed by the 5G AeroMACS transceiver array antenna equipped on the cooperative vehicle, and specifically includes the following steps.

[0126] Step S6.1, for each pair of cooperating vehicles forming the beam direction in step S4 With the goal Design the joint transmit and receive radiation pattern of a MIMO array. When the communication data follows an independent Gaussian distribution, the joint transmit and receive radiation pattern is directly related to the transmit precoding matrix and the receive weighting vector. The transmit signal waveform after transmit precoding is as follows:

[0127] in, The emission precoding matrix for auxiliary sensing data stream, To assist in sensing data streams, For the transmission precoding matrix of the communication data stream, This refers to communication data streams. Among them, the auxiliary sensing data stream... and communication data stream This is the default value.

[0128] Furthermore, the transmitted signal waveform satisfies:

[0129] in, This represents the maximum total transmit power of the array antenna. The maximum total transmit power of this array antenna is determined by the configuration of the 5G AeroMACS transceiver antenna array assembled on the cooperative vehicle.

[0130] Step S6.2, construct the signal-to-interference-plus-noise ratio (SNR) for communication and the signal-to-noise ratio (SNR) for sensing, respectively:

[0131]

[0132] in, The signal-to-interference-plus-noise ratio (SIR / NOT) for communication. The perceived signal-to-noise ratio. This represents the complex conjugate transpose of the communication reception weighted vector. This represents the complex conjugate transpose of the sensing and receiving weighted vector. Indicates communication reception noise. This indicates perceived received noise.

[0133] Step S6.3, the design of the joint transmit and receive pattern for communication and sensing is constructed as the following optimization problem:

[0134] in, The weighting coefficients and .

[0135] Step S6.4: Solve the constructed optimization problem to obtain the transmit precoding matrix of the communication data stream, the transmit precoding matrix of the auxiliary sensing data stream, the communication receive weighting vector, and the values ​​of the sensing receive weighting vector. The optimization variables involved in the optimization problem include the transmit precoding matrix of the communication data stream. and auxiliary sensing data stream emission precoding matrix Channel uncertainty makes this an uncertainty optimization problem. Robust optimization is performed based on an upper bound on the uncertainty, and an iterative solution using a genetic algorithm is employed to obtain the transmit precoding matrix of the communication data stream, the transmit precoding matrix of the auxiliary sensing data stream, the communication reception weighting vector, and the values ​​of the sensing reception weighting vector. The iterative solution using the genetic algorithm includes: first, constructing an initial population of all unknown variables; establishing a fitness function with the objective function as its value; and using iterative operations of selection, crossover, and mutation to achieve the growth and convergence of the fitness function, thereby obtaining the transmit precoding matrix of the communication data stream in the above optimization problem. and auxiliary sensing data stream emission precoding matrix The solution.

[0136] Step S7: By obtaining the transmission precoding matrix of the communication data stream, the transmission precoding matrix of the auxiliary sensing data stream, the communication receiving weighting vector, and the sensing receiving weighting vector, signal transceiver beamforming for the cooperating vehicle is achieved, thereby enabling real-time monitoring of targets on the scene. Based on the instruction, the process returns to step S2 for the next round of sensing matching. The instruction here can be set such that if a stop instruction is received, all processes end; otherwise, if no instruction is received, the process automatically returns to step S2 to execute the next round of sensing matching, and the measurement result is obtained based on the reflected echo from the target received through signal transceiver beamforming of the cooperating vehicle. This provides step S3 for the next round.

[0137] For all 5G AeroMACS mobile base stations, according to the designed transmit precoding matrix The system transmits sensing signals, enabling simultaneous communication and the perception of all targets on the airport surface. Cooperating vehicles receive the reflected echoes from these targets and obtain measurement results. The measurement results will be used in the next time cycle. Step S3 provides the spatiotemporal evolution of the trajectory of the perceived target, step S4 provides the target matching, step S5 establishes the communication and perception channel model, and finally step S6 provides the waveform design for the next time cycle.

[0138] A second embodiment of the present invention provides a sensing-integrated beam scheduling system for multi-base station 5G AeroMACS, comprising: cooperative vehicles on an airport surface, each equipped with a 5G AeroMACS transceiver array antenna (hereinafter referred to as the array antenna), a satellite positioning system, and a self-speed sensing system, wherein the absolute position information of the cooperative vehicles is obtained through the satellite positioning system, and the self-speed sensing system includes a speedometer integrated into the control system of the cooperative vehicles; and sensing targets, including non-cooperative vehicles and aircraft on the airport surface. The speedometer may be a wheel speed sensor. The cooperative vehicles and sensing targets constitute an airport surface 5G AeroMACS sensing-integrated communication network.

[0139] The cooperative vehicles in this embodiment are further equipped with a measurement module, a trajectory spatiotemporal evolution module, and a target matching module. The measurement module includes an active perception model, a satellite positioning model, and a self-velocity model for the cooperative vehicles. It can obtain the current state measurements of the cooperative vehicles and perceived targets based on information provided by the satellite positioning system and the self-velocity sensing system. The trajectory spatiotemporal evolution module includes a Kalman prediction model, a trajectory filtering model, and a trajectory prediction model. Based on the obtained current state measurements of the cooperative vehicles and perceived targets, it obtains the trajectory filtering and prediction results for the next moment. The target matching module matches the perceived targets and cooperative vehicles based on the current state measurements of the cooperative vehicles and perceived targets and the trajectory filtering and prediction results for the next moment. It assigns a perceived target to each cooperative vehicle at the current moment and ensures that each perceived target is detected. The array antennas on each cooperative vehicle perform perceptual beamforming according to the matching results of the target matching module, enabling real-time monitoring of the perceived targets on the scene.

[0140] Example 1: The following is for reference Figures 4 to 8 An exemplary embodiment of the inductive beam scheduling method for multi-base station 5GAeroMACS provided by an embodiment of the present invention will be described.

[0141] In this embodiment 1, two cooperative vehicles are set to operate simultaneously on the airport surface, with their operating directions being horizontal and vertical, respectively (i.e., Figure 4 and Figure 5The x and y directions are set to a velocity of 20 m / s, and a fixed target is set in the airport scene. The frame interval is set to 1 second. The scene setup is as follows: Figure 4 As shown, each cooperative vehicle is equipped with a transceiver array with 16 antennas for the integrated sensing and communication beam scheduling in this embodiment.

[0142] At each moment, the cooperating vehicle, based on the collected sensor echoes and combining satellite positioning and speed sensing information, performs trajectory spatiotemporal evolution simulation based on steps S2 and S3 of the integrated sensing beam scheduling method for multi-base station 5G AeroMACS provided in this invention. Example results are shown below. Figure 5 As shown. The covariance matrix of the process noise is a diagonal matrix with elements of 1, the covariance matrix of the measurement noise is a diagonal matrix with elements of 0 and 1, and the covariance matrix of the cooperative vehicles is a diagonal matrix with elements of 1.

[0143] Subsequently, the sensing target matching module matches the cooperative vehicles with the sensing targets. Based on step S4 of the sensing-integrated beam scheduling method for multi-base station 5G AeroMACS provided by the embodiments of the present invention, the matching result is as follows: Figure 6a and Figure 6b As shown, target perception is performed by cooperative vehicle 1 for the first 11 time points and by cooperative vehicle 2 for the last 9 time points. The weighting coefficients for distance, radial velocity, angle, distance perception factor, velocity perception factor, and angle perception factor are all 1.

[0144] Subsequently, the cooperating vehicles conduct joint transmit and receive pattern design, based on steps S5 and S6 of the integrated sensing and beam scheduling method for multi-base station 5G AeroMACS provided by the embodiments of the present invention. Figure 7 and Figure 8 The beam pattern of cooperative vehicle 1 and cooperative vehicle 2 at time 5 and time 15 are given respectively. It can be seen that the cooperative vehicles realize the beam towards the sensing target and the communication beam towards the cooperative vehicle according to the result of the sensing target matching.

[0145] The integrated sensing and beam scheduling method and system for multi-base station 5G AeroMACS provided by the embodiments of the present invention not only enhances the monitoring capabilities of airport surfaces, but also provides more reliable technical support for safety management in the civil aviation field. It can provide a more efficient and intelligent way of monitoring and managing airport surfaces, and provides a solid guarantee for the operational safety and efficiency of airports.

[0146] According to embodiments of the present invention, a sensing-integrated beam scheduling system for multi-base station 5G AeroMACS is provided. By integrating advanced waveform design and signal processing technologies, it provides a comprehensive and efficient monitoring solution for airport surfaces. It fully considers the complexity and dynamism of airport surfaces and significantly improves the accuracy and reliability of monitoring through optimized signal transmission and processing strategies.

[0147] The sensing-integrated beam scheduling method and system for multi-base station 5G AeroMACS provided by embodiments of the present invention, through a multi-base station architecture, enables each base station to transmit and receive sensing-integrated waveforms, achieving target localization, identification, and tracking through precise signal processing. This multi-base station design not only improves the system's coverage but also enhances its adaptability to complex environments and dynamic changes. The system can monitor multiple cooperative vehicles and sensed targets within the airport surface in real time, ensuring the accuracy and real-time nature of the monitoring data. The sensing-integrated beam scheduling algorithm framework comprehensively considers the parallel acquisition of position and velocity measurements of cooperative vehicles and sensed targets by multiple base stations, enabling the acquisition of information such as relative distance, relative velocity, and relative angle between the antenna array and the detected target. Cooperative vehicles are equipped with onboard satellite positioning systems and their own velocity sensing systems, which provide absolute position and velocity information of the cooperative vehicles, further enhancing the system's sensing capabilities. Through the integration of these systems, comprehensive monitoring of cooperative vehicles and sensed targets can be achieved, providing more comprehensive data support.

[0148] The sensing-integrated beam scheduling system for multi-base station 5G AeroMACS provided according to an embodiment of the present invention also includes a sensing cost matrix, used to describe the comprehensive sensing performance for distance, speed, and angle. Through the sensing cost matrix, sensing target matching can be performed more effectively, ensuring that each sensing target can be sensed by cooperative vehicles. To solve the sensing target matching problem, the system adopts a 0-1 integer programming problem model and solves it using a programming method. Finally, joint optimization of transmit and receive radiation patterns is achieved under resource-constrained conditions.

[0149] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0150] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0151] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A sensing-integrated beam scheduling method for multi-base station 5G AeroMACS, characterized in that, Includes the following steps: Step S0: Provide cooperative vehicles at the airport scene, equip the cooperative vehicles with 5G AeroMACS transceiver array antennas, satellite positioning systems and their own speed sensing systems, and use non-cooperative vehicles as sensing targets. Step S1: Establish an airport surface 5G AeroMACS integrated sensing communication network based on the cooperative vehicles and sensing targets, wherein the targets include cooperative vehicles and sensing targets. Step S2: Establish a measurement model, including establishing an active perception model, a satellite positioning model of the cooperative vehicle, and a speed model of the cooperative vehicle itself; perform measurements and obtain the measurement set at the current frame time. Step S3: Construct a trajectory spatiotemporal evolution model based on the Kalman filter algorithm to characterize the motion state of cooperative vehicles and perceived targets in the real coordinate system; Step S4: Match the cooperative vehicles with each target to obtain the target assigned to each cooperative vehicle at the current frame time. Step S5: In the scenario of airport surface networking, construct the communication channel model and the sensing channel model based on geometric relationships. Step S6: For each pair of cooperative vehicles and targets matched in step S4, design the transmit and receive joint pattern of the MIMO array, solve the transmit and receive joint pattern, and obtain the transmit precoding matrix of the communication data stream, the transmit precoding matrix of the auxiliary sensing data stream, the communication receive weighting vector, and the value of the sensing receive weighting vector. Step S7: Based on the results obtained in step S6, signal transmission and reception beamforming for the cooperative vehicles is implemented, thereby enabling real-time monitoring of targets on the scene; and confirming whether a stop command has been received, if so, ending the program. Otherwise, return to step S2, and obtain the measurement result based on the signal transceiver beamforming of the cooperative vehicle to receive the reflected echo of the target, and substitute it into the next round of perception matching.

2. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 1, characterized in that, In step S0: The absolute position information of the cooperating vehicles is obtained through a satellite positioning system, and the speed sensing system includes a speedometer integrated into the control system of the cooperating vehicles.

3. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 1, characterized in that, In step S1: The number of cooperative vehicles is set as follows The number of perceived targets is In the current frame of the current round At time , the target's true state vector is: in, Indicates the index of the target. Indicates the first The horizontal position of the target Indicates the first The vertical position of the target Indicates the first The horizontal velocity of the target Indicates the first The vertical velocity of each target is obtained, and ( As the first The true location of each target, ( As the first The true speed of each target.

4. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 3, characterized in that, Step S2 specifically includes the following steps: Step S2.1, construct the active perception model; Step S2.2: Based on the satellite positioning system equipped in the cooperative vehicles, construct a satellite positioning model for the cooperative vehicles; Step S2.3: Based on the speed sensing system of the cooperative vehicle, construct the speed model of the cooperative vehicle. Step S2.4: Based on the target reflected echo of the sensing beam emitted in the previous iteration, perform corresponding signal processing and target parameter estimation to obtain the measured values ​​of the position and velocity of the sensing target at the current frame time of the current iteration. For the first iteration, preset the measured values ​​of the position and velocity of the sensing target. Step S2.5: Obtain measurement results through the constructed active perception model, the satellite positioning model of the cooperative vehicle, and the self-velocity model of the cooperative vehicle to obtain the measurement set at the current frame time, including the measurement values ​​of the relative distance, relative speed, and relative angle between the cooperative vehicle and the target at the current frame time, and the measurement results of the target; wherein, the measurement results of the target are the measurement values ​​of the target's position and speed, including the measurement values ​​of the position and speed of the cooperative vehicle, as well as the measurement values ​​of the perceived target's position and speed.

5. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 4, characterized in that, The trajectory spatiotemporal evolution model in step S3 includes: Kalman prediction model, trajectory filtering model, and trajectory prediction model.

6. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 5, characterized in that, Step S3 specifically includes the following steps: Step S3.1: For all targets, construct a Kalman prediction model based on the target's trajectory state prediction and trajectory prediction covariance matrix from the first two rounds, and obtain the target's one-step trajectory state prediction and trajectory prediction covariance matrix. Step S3.2: Based on the measurement association results with the minimum Mahalanobis distance, and by constructing a track filtering model through the track update process, the target's track state estimate and track filtering covariance matrix are obtained. Step S3.3: Based on the target's trajectory state estimation and trajectory filtering covariance matrix, construct a trajectory prediction model and obtain the target's trajectory state prediction and trajectory prediction covariance matrix for the current round. Step S3.4: Judge the detection status of all targets. Compare the number of consecutive undetected frames for each target with a preset exit frame threshold. If there is a target whose number of consecutive undetected frames is greater than or equal to the preset exit frame threshold, it is determined that the target has disappeared and the target exits the track filtering loop. For targets that meet the condition that the number of consecutive undetected frames is less than the preset exit frame threshold, save the track state prediction and track prediction covariance matrix of the current round as the track filtering result and track prediction result of the target.

7. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 6, characterized in that, Step S4 specifically includes the following steps: Step S4.1: Based on the target trajectory state prediction and trajectory prediction covariance matrix obtained in step S3, sample the trajectory state estimation of each cooperative vehicle and each perceived target, sample a set number of sigma points respectively, and obtain the state vector and position of each sampled sigma point. Step S4.2: Based on the positions of the sigma points of each cooperative vehicle and the positions of the sigma points of each perceived target, obtain the filtering distance and filtering angle of the sigma points of the cooperative vehicles and the sigma points of the targets. Step S4.3: Calculate the Cramer-Rao lower bound of the sigma points of the cooperative vehicles and the sigma point pairs of the targets, and obtain the weighted objective function of each target relative to each cooperative vehicle. ,in, Indicates the index of the target. An index indicating the vehicles used in cooperation; Step S4.4: To achieve perception of all targets, a perception target matching optimization problem is constructed. A 0-1 integer programming problem model is adopted, and the optimization variable is defined as the matching matrix. When the matching items in the matching matrix are... Time cooperation vehicles Towards the target Beam pointing The time indicates that no beam pointing is formed; To minimize the total perception cost, and with constraints ensuring that each target can be perceived by the cooperating vehicles, the objective function is established as follows: Solving the above objective function yields the objective assigned to each cooperative vehicle at the current frame time of the current round.

8. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 7, characterized in that, Step S5 specifically includes the following steps: Step S5.1: Based on the target trajectory prediction covariance matrix obtained in step S3, the target position error prediction covariance matrix is ​​obtained. Step S5.2: Obtain the upper bound of the communication channel uncertainty; Step S5.3: Obtain the upper bound of the perceived channel uncertainty; Step S5.4: Construct the communication channel model and the sensing channel model respectively to obtain the communication channel matrix and the sensing channel matrix.

9. The integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to claim 8, characterized in that, Step S6 specifically includes the following steps: Step S6.1, for each pair of cooperating vehicles forming the beam direction in step S4 With the goal The transmit and receive joint pattern of the MIMO array is designed, and the transmit signal waveform after transmit precoding is obtained as follows: in, The emission precoding matrix for auxiliary sensing data stream, To assist in sensing data streams, For the transmission precoding matrix of the communication data stream, This refers to communication data streams, including auxiliary sensing data streams. and communication data stream This is the default value; Furthermore, the transmitted signal waveform satisfies: in, The maximum total transmit power of the array antenna is determined by the configuration of the 5G AeroMACS transceiver antenna array assembled on the cooperative vehicle. Step S6.2, construct the signal-to-interference-plus-noise ratio (SNR) for communication and the signal-to-noise ratio (SNR) for sensing, respectively: in, The signal-to-interference-plus-noise ratio (SIR) for communication. The perceived signal-to-noise ratio. This represents the complex conjugate transpose of the communication reception weighted vector. This represents the complex conjugate transpose of the sensing and receiving weighted vector. Indicates communication reception noise. Indicates perceived received noise. For communication channel matrix, The channel matrix for perception; Step S6.3, the design of the joint transmit and receive pattern for communication and sensing is constructed as the following optimization problem: in, The weighting coefficients and ; Step S6.4: Solve the optimization problem to obtain the transmission precoding matrix of the communication data stream, the transmission precoding matrix of the auxiliary sensing data stream, the communication reception weighting vector, and the value of the sensing reception weighting vector.

10. A system for implementing the integrated sensing and beam scheduling method for multi-base station 5G AeroMACS according to any one of claims 1-9, characterized in that, include: The cooperative vehicle used at the airport is equipped with a 5G AeroMACS transceiver array antenna, a satellite positioning system, and its own speed sensing system. The satellite positioning system acquires the vehicle's absolute position information, and the speed sensing system includes a speedometer integrated into the vehicle's control system. Targets to be perceived, including non-cooperative vehicles and aircraft in the airport scene; The cooperative vehicles and the sensing targets constitute the airport surface 5G AeroMACS integrated sensing communication network. The cooperative vehicle is equipped with an approximate posterior distribution measurement module, a trajectory spatiotemporal evolution module, and a perception target matching module.

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