Airport scene target adaptive detection method based on 5G AeroMACS

Abstract

The invention relates to the technical field of airport scene communication and perception integrated systems, in particular to an airport scene target adaptive detection method based on 5GAeroMACS, which comprises the following steps: a base station sends a transmission signal; the echo signals are reflected by an airport scene target, and the antenna array of the base station receives the echo signals; performing time grouping processing on the transmitting signal and the echo signal based on a cyclic cross-correlation method to obtain a distance velocity spectrum; performing Fourier transform for each antenna in the antenna array on a distance velocity spectrum to obtain a distance velocity angle spectrum; performing adaptive threshold detection on the distance velocity angle spectrum to obtain position information and motion information of an airport scene target; adopting an extended Kalman filtering method to predict and obtain future position information and motion information of the airport scene target based on the position information and the motion information of the airport scene target, and obtaining a predicted motion track; according to the invention, the precision of detecting and tracking the target on the airport scene can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of airport surface communication and perception integrated systems, and particularly relates to a method for adaptively detecting airport surface targets based on 5G AeroMACS. Background Art

[0002] With the booming development of the aviation industry, the safety monitoring and management of the airport surface have become particularly important. The complexity of the airport surface is constantly increasing. Traditional monitoring systems, such as solutions based on radar and sensor networks, although providing necessary monitoring data to a certain extent, have obvious limitations in terms of monitoring accuracy, data processing speed, and real-time performance. These limitations not only affect the safety management of the airport surface but also restrict the efficiency and safety of air transportation.

[0003] In the current technical environment, 5G communication technology has brought new opportunities to the field of airport surface communication and perception. With its characteristics of high bandwidth and low latency, 5G technology makes it possible to achieve higher-precision and higher-efficiency monitoring. However, how to effectively integrate 5G technology into the existing airport monitoring system, how to ensure the real-time transmission and processing of monitoring data, and how to improve the accuracy of target recognition are all problems that need to be solved urgently at present.

[0004] The complexity of the airport surface communication and perception system is also constantly increasing. With the expansion of the airport scale and the increase in the number of flights, the amount of data that the monitoring system needs to process has increased sharply, which puts higher requirements on the data transmission and processing system. Traditional monitoring systems often have delays and errors when processing large-scale data, which not only affects the accuracy of the monitoring results but also increases the risk of safety management. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method for adaptively detecting airport surface targets based on 5G AeroMACS, which solves the technical problem of insufficient accuracy in detecting and tracking targets on the airport surface in the prior art.

[0006] The present invention provides a method for adaptively detecting airport surface targets based on 5G AeroMACS, including: Step S1, the base station sends a 5G AeroMACS transmission signal; the 5G AeroMACS transmission signal is reflected by the airport surface target to generate a 5G AeroMACS echo signal, and the antenna array of the base station receives the 5G AeroMACS echo signal;

[0007] Step S2: Perform time grouping processing on the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal based on the cyclic cross-correlation method to obtain the range-velocity spectrum; perform Fourier transform on the range-velocity spectrum for each antenna in the antenna array to obtain the range-velocity-angle spectrum;

[0008] Step S3: Perform adaptive threshold detection on the range-velocity-angle spectrum to obtain the position information and motion information of the airport surface target;

[0009] Step S4: Use the extended Kalman filtering method to predict the future position information and motion information of the airport surface target based on the position information and motion information of the airport surface target, and obtain the predicted motion trajectory.

[0010] Preferably, in step S1, the 5G AeroMACS transmitted signal is an OFDM signal, and the OFDM signal includes a cyclic prefix and a valid OFDM signal. The time length of the cyclic prefix is greater than or equal to the maximum delay spread of the channel, and the time length of the cyclic prefix is greater than or equal to the time corresponding to the maximum range cell. The time corresponding to the maximum range cell refers to the maximum time required for the signal to propagate to the target after transmission.

[0011] Preferably, the specific steps of step S2 include:

[0012] Step S2-1: Obtain the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal, and set the time grouping parameters;

[0013] Step S2-2: Perform sliding time block truncation on the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal according to the time grouping parameters to obtain the sub-block sequences of the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal;

[0014] Step S2-3: After adding a virtual cyclic prefix to the sub-block sequences of the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal, perform cross-correlation calculation on the 5G AeroMACS transmitted signal and the 5G AeroMACS echo signal to obtain the range-velocity spectrum;

[0015] Step S2-4: Perform Fourier transform on the range-velocity spectrum for each antenna in the antenna array to obtain the range-velocity-angle spectrum.

[0016] Preferably, the specific steps of step S2-1 include:

[0017] Obtain the 5G AeroMACS transmission signal TxSignal_cp, obtain the corresponding echo signal RxSignal, and determine the time grouping parameters, including the data volume mildM of each sub-block, the number of overlapping points Qbar between adjacent sub-blocks, and the virtual cyclic prefix length mildQ;

[0018] The specific steps of step S2-2 include:

[0019] Using mildM as the data volume of the sub-block and a step size with a step of mildM-Qbar, perform sliding time block truncation on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal respectively to obtain the sub-block sequence Tx_sub of the 5G AeroMACS transmission signal and the sub-block sequence Rx_sub of the echo signal;

[0020] The dimensions of both Tx_sub and Rx_sub are mildM×mildN, where mildN represents the number of sub-blocks, and the calculation expression is:

[0021]

[0022] where length(·) represents obtaining the signal length, and floor(·) represents rounding down;

[0023] The specific steps of step S2-3 include:

[0024] After adding a virtual cyclic prefix with a length of mildQ to the start position of each sub-block in Tx_sub and Rx_sub, calculate the frequency-domain cross-correlation quantity S of Tx_sub and Rx_sub through the following expression:

[0025] S = fft(Rx_sub).*conj(fft(Tx_sub))

[0026] where fft(·) represents the fast Fourier transform, conj(·) represents taking the conjugate complex number, and.* represents multiplying the values in the matrix point by point;

[0027] Range-velocity spectrum S RV The calculation expression is:

[0028] S RV = fft((ifft(S)) T )

[0029] where (·) T represents matrix transpose, and ifft(·) represents the inverse fast Fourier transform.

[0030] Preferably, the specific steps of step S3 include:

[0031] Step S3-1: Divide the range-velocity-angle spectrum into multiple resolution cells, and the data in the resolution cells includes echo amplitude;

[0032] Step S3-2: Perform adaptive threshold detection based on the echo amplitudes of the respective resolution cells in the range-velocity-angle spectrum, and obtain the range, velocity, and angle of the detected target in the resolution cell as the position information and motion information of the airport surface target.

[0033] Preferably, the expression of the adaptive threshold detection is:

[0034]

[0035] where represents the echo amplitude of the l-th resolution cell in the k-th frame during threshold judgment, ρ k represents the echo signal-to-noise ratio of the k-th frame, P(H seg,0 |Z(k - 1)) represents the false alarm rate of the target point cloud in the (k - 1)-th frame, represents the Gaussian likelihood function, represents the mean value of the predicted trajectory position of the k-th frame based on the (k - 1)-th frame, D k|k-1 represents the predicted covariance matrix of the k-th frame based on the (k - 1)-th frame, represents the predicted distribution of the (k - 1)-th frame, ln[·] represents the natural logarithm, represents the preset false alarm rate, γ BD represents the judgment threshold, H0 represents the absence of a target, and H1 represents the presence of a target.

[0036] Preferably, the specific steps of Step S4 include:

[0037] Step S4-1: Predict the predicted target state and predicted covariance matrix of the k-th frame based on the state and covariance matrix of the (k - 1)-th frame;

[0038] Step S4-2: Use the position information and motion information of the airport surface target as measurement information, and update the target state and covariance matrix of the k-th frame based on the predicted target state and predicted covariance matrix;

[0039] Step S4-3: Accumulate based on the target state of the k-th frame in combination with the time interval to obtain the continuous position of the target as the predicted motion trajectory.

[0040] Preferably, the specific steps of Step S4-1 include:

[0041] The calculation expressions for the predicted target state and predicted covariance matrix are:

[0042]

[0043] Among them, represents the predicted target state of the k-th frame, and x k-1 represents the target state of the (k - 1)-th frame, F is the state transition matrix, I2 represents the two-dimensional identity matrix, represents the Kronecker product, and dt represents the frame interval time;

[0044]

[0045] Among them, represents the predicted covariance matrix of the k-th frame, and P k-1 represents the covariance matrix of the (k - 1)-th frame, and Q represents the covariance matrix of the process noise;

[0046] The specific steps of step S4-2 include:

[0047] The position information and motion information include measurement values of distance, speed, and angle. The measurement values of distance, speed, and angle are converted into measurement values of the target's position coordinates and two-dimensional velocity components. The expression is:

[0048]

[0049] Among them, R, V, and A respectively represent the measurement values of distance, speed, and angle, represents the measurement value of the target's position coordinates, represents the measurement value of the target's two-dimensional velocity components;

[0050] The target state and covariance matrix are updated through the following expressions:

[0051]

[0052]

[0053] Among them, K k represents the Kalman gain of the k-th frame, H = I4 is the four-dimensional identity matrix, R represents the covariance matrix of the measurement noise, and (·) -1 represents matrix inversion; x k represents the target state of the k-th frame, represents the measurement value of the target state of the k-th frame; P k represents the covariance matrix of the k-th frame.

[0054] Compared with the prior art, the present invention has at least the following beneficial effects:

[0055] (1) The present invention performs time grouping processing on the transmitted signal and the echo signal by using the cross-correlation method to generate a range-velocity spectrum, and further obtains a range-velocity-angle spectrum through Fourier transform. It can accurately capture the position information and motion state of the target, providing strong data support for the real-time monitoring of the airport, thereby maintaining high-efficiency monitoring capabilities in a dynamic environment.

[0056] (2) Based on the prior information of the predicted motion trajectory, the present invention performs adaptive threshold detection on the range-velocity-angle spectrum, which can dynamically adjust the detection threshold, improve the detection sensitivity, and better adapt to complex scene changes, thereby improving the overall performance and reliability of target tracking. This process effectively reduces the false alarm rate, ensures the reliability of target detection, and further enhances the ability of airport surface safety management. By obtaining the position information and motion information of the target in real time, the system can quickly respond to potential safety threats, reducing the uncertainty in airport operations.

[0057] (3) Based on the historical motion information of the target, the system can accurately predict the future target position and motion trajectory. This not only improves the continuity and accuracy of target tracking but also provides a guarantee for rapid response in emergency situations. Through efficient target tracking, the system can maintain a high level of safety monitoring in a complex airport environment, ensuring the on-time rate of flights and the overall operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings are only for the purpose of showing specific embodiments and are not considered to be a limitation of the present invention.

[0059] Figure 1 It is a flowchart of the airport surface target adaptive detection method based on 5G AeroMACS provided by the present invention.

[0060] Figure 2 It is a schematic diagram of the 5G AeroMACS transmission frame structure provided by the present invention.

[0061] Figure 3 It is a schematic diagram of the 5G AeroMACS received signal provided by the present invention.

[0062] Figure 4 It is a schematic diagram of the principle of the airport surface target adaptive detection method provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without 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. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0064] The present invention provides an airport surface target adaptive detection method based on 5G AeroMACS. By utilizing the low latency, large bandwidth, and high reliability characteristics of 5G communication, accurate detection and tracking of targets in the airport surface are achieved. The working mechanism of the system includes transmitting 5G AeroMACS signals, which generate echoes after encountering targets and are received by the antenna array of the base station. By processing the position and velocity information contained in the target reflected echoes, accurate detection of the target is realized. The time delay, Doppler frequency shift, and phase difference on different antennas of the echo signal are respectively used to calculate the distance, velocity, and angle of the target, and the integration of this information provides strong data support for the real-time monitoring of the airport surface.

[0065] The present invention is proposed to promote the development of airport surface communication and sensing technologies through technological innovation. By integrating 5G AeroMACS technology, the present invention can not only improve the monitoring ability of the airport surface but also provide more reliable technical support for safety management in the civil aviation field.

[0066] To illustrate the effectiveness of the method proposed by the present invention, the above-mentioned technical solutions of the present invention will be described in detail below through a specific embodiment. A specific embodiment of the present invention is as Figure 1 、 Figure 4 shown, and an airport surface target adaptive detection method based on 5G AeroMACS is disclosed. The specific implementation steps are as follows:

[0067] Step S1: The base station transmits a 5G AeroMACS transmission signal; the 5G AeroMACS transmission signal generates a 5G AeroMACS echo signal after being reflected by the airport surface target, and the antenna array of the base station receives the 5G AeroMACS echo signal;

[0068] 5G AeroMACS is an advanced aviation 5G airport surface communication system that utilizes the characteristics of the 5G communication system to achieve low latency, large bandwidth, and high-reliability communication. The present invention realizes accurate detection and tracking of targets in the airport surface based on 5G AeroMACS signal echoes. The system detects targets through target echoes, generates tracks by combining the distance, velocity, and angle information of the targets, so as to realize the real-time monitoring and management of the airport surface.

[0069] The airport surface base station first sends 5G AeroMACS transmission signals. These signals will generate echo signals after encountering targets, and then the echo signals are received by the antenna array of the base station. The 5G AeroMACS transmission signals involved in the present invention are as Figure 2 , Figure 3 shown. The transmission signal is an OFDM signal. One pulse contains N S OFDM signals. A complete OFDM signal consists of two parts: a cyclic prefix (CP) and a valid OFDM signal. Among them, the time length of the cyclic prefix is represented by T G , and the duration of the entire OFDM signal is represented by T S , and the duration of the valid OFDM signal is represented by T.

[0070] In some embodiments, the length T G of the cyclic prefix is required to satisfy two conditions: on the one hand, T G is greater than or equal to the maximum delay spread of the channel; on the other hand, T G is greater than or equal to the time corresponding to the maximum distance unit in the 5G airport surface communication system. The time corresponding to the maximum distance unit refers to the maximum time required for the signal to propagate to the target after being sent.

[0071] As Figure 3 shown, the echo signals reflected by different targets will have different time delays. The position and velocity information of the target are contained in the echo signals received by the base station. Specifically, the time delay of the echo signal can be used to calculate the distance of the target, the Doppler frequency shift of the echo signal can be used to estimate the velocity of the target, and the phase difference of the echo signal on different antennas can be used to estimate the angle of the target.

[0072] Step S2: Perform time grouping processing on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal based on the cyclic cross-correlation method to obtain a range-velocity spectrum; perform Fourier transform on the range-velocity spectrum for each antenna in the antenna array to obtain a range-velocity-angle spectrum.

[0073] The present invention performs cross-correlation calculation on the transmission signal and the echo signal by time grouping. The specific steps are as follows:

[0074] (1) Obtain the 5G AeroMACS transmission signal TxSignal_cp, obtain the corresponding echo signal RxSignal, and determine the time grouping parameters, including the data volume mildM of each sub-block, the overlapping point number Qbar between adjacent sub-blocks, and the virtual cyclic prefix length mildQ.

[0075] (2) Perform block processing on the transmitted signal TxSignal_cp and the echo signal RxSignal in chronological order, specifically including: taking mildM as the data volume of each sub-block and mildM-Qbar as the step size, performing sliding time block truncation on the transmitted signal and the echo signal respectively to obtain the sub-block sequence Tx_sub of the transmitted signal and the sub-block sequence Rx_sub of the echo signal.

[0076] Both Tx_sub and Rx_sub are of dimension mildM×mildN. mildN represents the number of sub-blocks, and the calculation expression is:

[0077]

[0078] where length(·) represents obtaining the signal length, and floor(·) represents rounding down.

[0079] (3) Add a virtual cyclic prefix with a length of mildQ to the start position of each sub-block in the sub-block sequence Tx_sub of the transmitted signal and the sub-block sequence Rx_sub of the echo signal. The data of the virtual cyclic prefix is copied from the end of the sub-block, which is used to reduce inter-symbol interference.

[0080] Calculate the frequency-domain cross-correlation quantity S of Tx_sub and Rx_sub through the following expression:

[0081] S = fft(Rx_sub).* conj(fft(Tx_sub))

[0082] where fft(·) represents the fast Fourier transform, conj(·) represents taking the conjugate complex number, and.* represents element-wise multiplication of the values in the matrix.

[0083] The range-velocity spectrum S RV The calculation expression is:

[0084] S RV = fft((ifft(S)) T )

[0085] where (·) T represents matrix transpose, and ifft(·) represents the inverse fast Fourier transform.

[0086] (4) After obtaining the range-velocity spectrum S RV , the present invention uses the Fourier transform method for each antenna in the antenna array to process the range-velocity spectrum received by each antenna to analyze the information in different angular directions. By converting the spatial dimension of the antenna array into an angular dimension, the range, velocity, and angle information of the target are obtained, and the range-velocity-angle spectrum S RVA is obtained.

[0087] Range-velocity-angle spectrum S RVA is a three-dimensional tensor of range, velocity, and angle. The values in the tensor represent the target intensity of the airport surface target. The greater the target intensity, the higher the likelihood of the target's appearance.

[0088] Step S3: Perform adaptive threshold detection on the range-velocity-angle spectrum to obtain the position information and motion information of the airport surface target.

[0089] After obtaining the range-velocity-angle spectrum, the present invention detects each data point in the range-velocity-angle spectrum, and can extract the position points with a high likelihood of target appearance. These position points are called the target point cloud. The information carried by the target point cloud includes the position information and motion information of the airport surface target.

[0090] The range-velocity-angle spectrum is divided into multiple resolution cells. The l-th resolution cell represents a position in the range-velocity-angle spectrum tensor. The value range of l is from 1 to Rbinnum×Vbinnum×Abinnum, where Rbinnum, Vbinnum, and Abinnum respectively represent the total number of discrete points of range, velocity, and angle. The data in the resolution cell includes the echo amplitude of the echo signal.

[0091] The method provided by the present invention will continuously obtain the range-velocity-angle spectra arranged in chronological order on the time scale. Each time a range-velocity-angle spectrum is obtained, it is also called obtaining 1 frame.

[0092] In this step, first, the result of the predicted motion trajectory is used as prior information to perform adaptive threshold detection on the range-velocity-angle spectrum. According to the Swerling-I model, the hypothesis model of the target in the l-th resolution cell can be expressed as:

[0093]

[0094] where H0 represents the absence of a target, represents the conditional probability of the echo amplitude in the l-th resolution cell in the k-th frame under the condition of no target presence. exp(·) represents the natural exponential, represents the echo amplitude in the l-th resolution cell in the k-th frame; H1 represents the presence of a target, represents the conditional probability of the echo amplitude in the l-th resolution cell in the k-th frame under the condition of target presence, ρ k represents the echo signal-to-noise ratio in the k-th frame.

[0095] According to the Neyman-Pearson criterion, the adaptive threshold detection of the range-velocity-angle spectrum can be expressed as the following detection problem:

[0096]

[0097] Among them, η is the detection threshold; the above formula means that when the detection value is greater than or equal to η, the result is determined as H1, and when the detection value is less than η, the result is determined as H0.

[0098] Introduce the predicted distribution of the (k - 1)th frame into the detector as the probability distribution of the H1 hypothesis, and at the same time use the false alarm rate of the estimated target point cloud of the (k - 1)th frame as the probability distribution of the H0 hypothesis. The expression is:

[0099]

[0100] Among them, represents the probability distribution of the H0 hypothesis, and P(H seg,0 |Z(k - 1)) represents the false alarm rate of the target point cloud of the (k - 1)th frame. represents the probability distribution of the H1 hypothesis. represents the Gaussian likelihood function. represents the mean value of the motion trajectory position of the kth frame predicted according to the (k - 1)th frame, and D k|k-1 represents the covariance matrix of the motion trajectory of the kth frame predicted according to the (k - 1)th frame. represents the predicted distribution of the (k - 1)th frame.

[0101] Thus, the observation signal model of the lth resolution cell under the two hypotheses can be defined as:

[0102]

[0103] Thus, the threshold judgment expression of the adaptive target detector is obtained:

[0104]

[0105] Among them, represents the echo amplitude of the lth resolution cell in the kth frame during threshold judgment, and ln[·] represents the natural logarithm. represents the preset false alarm rate, and γ BD represents the judgment threshold.

[0106] Through the above process of threshold judgment, the present invention can extract detection points with relatively large energy from the range - velocity - angle spectrum as the target, and obtain the measurement values of the range, velocity, and angle of the target. Take the measurement values of the range, velocity, and angle of the target as the position information and motion information of the airport surface target.

[0107] Step S4: Use the extended Kalman filter method to predict the future position information and motion information of the airport surface target based on the position information and motion information of the airport surface target, and obtain the predicted motion trajectory.

[0108] The extended Kalman filter is a recursive algorithm used to estimate the state of a dynamic system. It consists of two main steps: prediction and update. In the prediction phase, the state of the current frame is predicted based on the system state and motion model of the previous frame. The update phase uses the current observation data to adjust the prediction to more accurately reflect the true state of the system.

[0109] Specifically in the present invention, the system state of the extended Kalman filter is the position information and motion information of the target. The two-dimensional state of the target in the (k - 1)th frame can be represented as x K-1 =[x, v X , y, v y T (k - 1), where x and y represent the position coordinates of the target, and v x , v y represent the two-dimensional velocity components of the target. The track filtering covariance matrix is P k-1 . The specific steps of the extended Kalman filter include:

[0110] (1) Predict the predicted target state and predicted covariance matrix of the kth frame according to the state and covariance matrix of the (k - 1)th frame. The expression is:

[0111]

[0112] where, represents the predicted target state of the kth frame, x k-1 represents the target state of the (k - 1)th frame, F is the state transition matrix, I2 represents the two-dimensional identity matrix, represents the Kronecker product, and dt represents the frame interval time.

[0113]

[0114] where, represents the predicted covariance matrix of the kth frame, P k-1 represents the covariance matrix of the (k - 1)th frame, and Q represents the covariance matrix of the process noise.

[0115] (2) Update the target state and covariance matrix according to the position information and motion information of the airport surface target as measurement information.

[0116] ​The position information and motion information include measured values of distance, speed, and angle. The measured values of distance, speed, and angle are converted to obtain the measured values of the position coordinates and two-dimensional velocity components of the target in the extended Kalman filter method. The expression is:

[0117]

[0118] where R, V, and A respectively represent the measured values of distance, speed, and angle. represents the measured value of the target's position coordinates. represents the measured value of the target's two-dimensional velocity components.

[0119] According to the measurement results, the target state and covariance matrix are updated through the following expressions:

[0120]

[0121] where K k represents the Kalman gain of the k-th frame, H = I4 is a four-dimensional identity matrix, R represents the covariance matrix of the measurement noise, (·) -1 represents matrix inversion; x k represents the target state of the k-th frame. represents the measured value of the target state of the k-th frame; P k represents the covariance matrix of the k-th frame.

[0122] After obtaining the target state of the k-th frame, this information can be used to predict the future motion trajectory. Specifically, by continuously applying the current velocity information to the current position and accumulating it in combination with the time interval, the continuous position of the target at future moments can be obtained, thereby forming the predicted motion trajectory of the airport surface target and completing the adaptive detection of the airport surface target.

[0123] After obtaining the target state of the k-th frame in the present invention, the target state of the k-th frame and the motion trajectory are used as prior information in step S3 for adaptive threshold detection of the distance-velocity-angle spectrum. In this way, the present invention can adaptively and dynamically adjust the detection threshold, improve the detection sensitivity, and better adapt to complex scene changes, thereby improving the overall performance and reliability of target tracking.

[0124] While the specific embodiments of the present invention depict various actions or steps in a particular order, it should be understood that such actions or steps are required to be performed in the specific order shown or in a sequential order, or that all of the illustrated actions or steps should be performed to achieve the desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the foregoing description, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented in combination in a single implementation. Conversely, the various features described in the context of a single implementation may also be implemented separately or in any suitable sub-combination in multiple implementations. As described above, the above are only the preferred specific embodiments 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 those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

[0125] As described above, the above are only the preferred specific embodiments 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 those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. An airport surface target adaptive detection method based on 5G AeroMACS, characterized in that, It includes the following steps: Step S1: The base station transmits a 5G AeroMACS transmission signal; The 5G AeroMACS transmission signal is reflected by the airport surface target to generate a 5G AeroMACS echo signal, and the antenna array of the base station receives the 5G AeroMACS echo signal; Step S2: Perform time grouping processing on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal based on the cyclic cross-correlation method to obtain a range-velocity spectrum; perform Fourier transform on the range-velocity spectrum for each antenna in the antenna array to obtain a range-velocity-angle spectrum; Step S3: Perform adaptive threshold detection on the range-velocity-angle spectrum to obtain the position information and motion information of the airport surface target; Step S4: Adopt the extended Kalman filtering method to predict the position information and motion information of the future airport surface target based on the position information and motion information of the airport surface target, and obtain the predicted motion trajectory.

2. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 1, wherein In the said Step S1: The 5G AeroMACS transmission signal is an OFDM signal, and the OFDM signal includes a cyclic prefix and a valid OFDM signal. The time length of the cyclic prefix is greater than or equal to the maximum delay spread of the channel, and the time length of the cyclic prefix is greater than or equal to the time corresponding to the maximum range cell. The time corresponding to the maximum range cell refers to the maximum time required for the signal to propagate to the target after being sent.

3. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 2, wherein The said Step S2 specifically includes: Step S2-1: Obtain the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal, and set the time grouping parameters; Step S2-2: Perform sliding time block truncation on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal according to the time grouping parameters to obtain a sub-block sequence of the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal; Step S2-3: After adding a virtual cyclic prefix to the sub-block sequence of the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal, perform cross-correlation calculation on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal to obtain a range-velocity spectrum; Step S2-4: Perform Fourier transform on the range-velocity spectrum for each antenna in the antenna array to obtain a range-velocity-angle spectrum.

4. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 3, characterized in that The said Step S2-1 specifically includes: Obtain the 5G AeroMACS transmission signal TxSignal_cp, obtain the corresponding 5G AeroMACS echo signal RxSignal, and determine the time grouping parameters, including the data volume mildM of each sub-block, the overlapping point number Qbar between adjacent sub-blocks, and the virtual cyclic prefix length mildQ; The said Step S2-2 specifically includes: Using the data volume of mildM as sub - blocks and the step size of mildM - Qbar as the step, sliding time - block truncation is performed on the 5G AeroMACS transmission signal and the 5G AeroMACS echo signal respectively to obtain the sub - block sequence Tx_sub of the 5G AeroMACS transmission signal and the sub - block sequence Rx_sub of the 5G AeroMACS echo signal; The dimensions of both Tx_sub and Rx_sub are mildM×mildN, where mildN represents the number of sub - blocks, and the calculation expression is: where length(·) represents obtaining the signal length, and floor(·) represents rounding down; The specific steps of step S2 - 3 include: After adding a virtual cyclic prefix with a length of mildQ to the start position of each sub - block in Tx_sub and Rx_sub, the frequency - domain cross - correlation quantity S of Tx_sub and Rx_sub is calculated through the following expression: S = fft(Rx_sub).* conj(fft(Tx_sub)) where fft(·) represents the fast Fourier transform, conj(·) represents taking the conjugate complex number, and.* represents element - by - element multiplication of the values in the matrix; Distance-velocity spectrum S RV The calculation formula is as follows: S Rv = fft((ifft(S)) T ) where, (·) T denotes matrix transpose, and ifft(·) denotes the inverse fast Fourier transform.

5. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 4, wherein The specific steps of step S3 include: Step S3 - 1: Divide the range - velocity - angle spectrum into multiple resolution cells, and the data in the resolution cells includes the echo amplitude; Step S3 - 2: Based on the echo amplitudes of each resolution cell in the range - velocity - angle spectrum, perform adaptive threshold detection to obtain the range, velocity, and angle of the detected target in the resolution cell, as the position information and motion information of the airport surface target.

6. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 5, wherein, The expression of the adaptive threshold detection is: Among them, represents the echo amplitude of the l-th resolution unit in the k-th frame during threshold judgment, ρ k represents the echo signal-to-noise ratio of the k-th frame, P(H seg,0 |Z(k - 1)) represents the false alarm rate of the target point cloud in the (k - 1)-th frame, represents the Gaussian likelihood function, represents the mean value of the predicted motion trajectory position of the k-th frame predicted from the (k - 1)-th frame, D k|k-1 represents the covariance matrix of the predicted motion trajectory of the k-th frame predicted from the (k - 1)-th frame, represents the predicted distribution of the (k - 1)-th frame, ln[·] represents the natural logarithm, represents the preset false alarm rate, γ BD represents the judgment threshold, H0 represents the absence of a target, and H1 represents the presence of a target.

7. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 6, wherein The specific steps of step S4 include: Step S4 - 1: Predict the predicted target state and predicted covariance matrix of the k - th frame according to the target state and covariance matrix of the (k - 1) - th frame; Step S4 - 2: Use the position information and motion information of the airport surface target as measurement information, and update the target state and covariance matrix of the k - th frame based on the predicted target state and predicted covariance matrix; Step S4 - 3: Based on the target state of the k - th frame and accumulate it over a time interval to obtain the continuous position of the target, as the predicted motion trajectory.

8. The airport surface target adaptive detection method based on 5G AeroMACS according to claim 7, wherein The specific steps of step S4 - 1 include: The calculation expressions of the predicted target state and predicted covariance matrix are: Among them, represents the predicted target state of the k-th frame, and x k-1 represents the target state of the (k - 1)-th frame, F is the state transition matrix, I2 represents the two-dimensional identity matrix, represents the Kronecker product, and dt represents the frame interval time; Among them, represents the prediction covariance matrix of the k-th frame, P k-1 represents the covariance matrix of the (k - 1)-th frame, and Q represents the covariance matrix of the process noise; The specific steps of step S4 - 2 include: The position information and motion information include the measured values of range, velocity, and angle. Convert the measured values of range, velocity, and angle into the measured values of the target's position coordinates and two - dimensional velocity components. The expression is: Among them, R, V, and A respectively represent the measured values of distance, speed, and angle, representing the measured values of the position coordinates of the target, representing the measured values of the two-dimensional velocity components of the target; Update the target state and covariance matrix through the following expression: Among them, K k represents the Kalman gain of the k-th frame, H = I4 is a four-dimensional identity matrix, R represents the covariance matrix of the measurement noise, (·) -1 represents matrix inversion; x k represents the target state of the k-th frame, represents the measured value of the target state of the k-th frame; P k represents the covariance matrix of the k-th frame.

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