A Multidimensional Super-Resolution Target Perception Method Based on 5G AeroMACS

Abstract

The present invention belongs to the integration of communication and sensing, and proposes a multi-dimensional super-resolution target sensing method based on 5G AeroMACS. By combining 5G communication technology and the integration technology of communication and sensing, when the signal is transmitted, 5G AeroMACS is used to modulate the data to be transmitted into multiple OFDM symbols and then transmit them. When the signal is received, the OFDM symbols are received through multiple receiving antennas, the data is restored and stacked into a three-dimensional data tensor, and stored in a three-dimensional complex matrix. Based on the three-dimensional complex matrix within the time window length, the dimension of the signal subspace is determined, and the signal subspace, noise subspace and three-dimensional spectral peak search function are constructed to obtain a multi-dimensional super-resolution spectrogram. When target detection is performed, a three-dimensional constant false alarm detection algorithm is used to obtain the target sensing result. The present invention realizes the super-resolution detection of multi-dimensional targets at the airport, accurately monitors and identifies moving targets, prevents potential safety risks, and improves the airport operation efficiency and flight safety at the same time.

Description

Technical Field

[0001] The present invention belongs to the integration of communication and sensing, and particularly relates to a multi-dimensional super-resolution target sensing method based on 5G AeroMACS. Background Technique

[0002] The rapid growth of the aviation industry has made airport security supervision crucial. A key task of airport security monitoring is to achieve high-definition monitoring and identification of moving targets such as aircraft, vehicles, and personnel within the airport. This high-definition monitoring plays a decisive role in preventing runway incursions, improving airport operation efficiency, and ensuring flight safety. Although traditional monitoring technologies, such as radar and sensor networks, can provide monitoring data, they have deficiencies in monitoring accuracy, data processing speed, and real-time response. These deficiencies pose challenges to the safe operation of airports and also limit the efficiency and safety of air transportation.

[0003] As an emerging aviation broadband communication technology, 5G AeroMACS technology can safely and efficiently transmit operation service information within civil aviation airports using dedicated frequencies. Combined with Beidou high-precision positioning technology, 5G AeroMACS technology can significantly improve the communication efficiency among airports, air traffic management, and airlines, and enhance the perception ability of the airport operation situation. The application of this technology is of great significance for preventing vehicle incursions onto the runway and can achieve the coordinated operation of aircraft, vehicles, runways, and facilities, thereby improving the safe operation level of airports.

[0004] The integration technology of communication and sensing has opened up a new way to improve airport monitoring capabilities. The highly integrated system is based on the simultaneous and co-beam regime, mainly including linear frequency modulation wave, signal separation, and orthogonal frequency division multiplexing technology. By modulating the linear frequency modulation wave to load communication information, this integrated signal can simultaneously achieve radar detection and communication functions, but the challenge is that the communication rate is low and it is difficult to meet the actual communication needs. The signal separation technology fuses radar signals and communication signals at the transmitting end and separates and processes them at the receiving end, which is divided into two ways: additive and multiplicative signal separation. The integration degree of this method is limited, and the communication signal will occupy part of the transmitting power of the radar signal, affecting the detection performance of the radar. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a multi-dimensional super-resolution target perception method based on 5G AeroMACS. This method is based on the 5G AeroMACS OFDM communication system, realizing the super-resolution detection of airport multi-dimensional targets, which not only helps to achieve super-resolution perception, but also significantly improves the airport's safety monitoring ability. Applying this method, the airport can more accurately monitor and identify moving targets, prevent potential safety risks, and at the same time improve the airport's operation efficiency and flight safety. In addition, it can also provide richer data support for the airport, helping airport managers make more accurate decisions, optimize resource allocation, improve service quality, and ultimately realize the intelligence and automation of airport operations.

[0006] The technical solution of the present invention is as follows:

[0007] A multi-dimensional super-resolution target perception method based on 5G AeroMACS, comprising the following steps:

[0008] Step S1: Modulate the data to be transmitted into multiple OFDM symbols using 5G AeroMACS and transmit them.

[0009] Step S2: Multiple receiving antennas receive the OFDM symbols in the air, restore the data and stack them into a three-dimensional data tensor, and store them in a three-dimensional complex matrix.

[0010] Step S3: Based on the three-dimensional complex matrix, construct a signal subspace, a noise subspace, and a three-dimensional spectral peak search function to obtain a multi-dimensional super-resolution spectrogram.

[0011] Step S4: Use a three-dimensional constant false alarm detection algorithm to obtain the final target perception result.

[0012] Preferably, the step S1 specifically includes:

[0013] Step S1-1: Data preparation: Map the data to be transmitted to M OFDM symbols using 5G AeroMACS.

[0014] Step S1-2: IFFT operation: Perform an IFFT operation on each OFDM symbol to convert the frequency-domain signal into a time-domain signal.

[0015] Step S1-3: Add a cyclic prefix: Add a cyclic prefix to the tail of each OFDM symbol.

[0016] Step S1-4: Signal transmission: Transmit the modulated OFDM symbols into the air through the transmitting antenna.

[0017] Preferably, each OFDM symbol in the step S1-1 contains N subcarriers, and the subcarriers are within the aviation dedicated frequency range of 5091 - 5150 MHz.

[0018] Preferably, step S2 specifically includes:

[0019] Step S2-1: Each of the H receiving antennas receives the OFDM symbols in the air.

[0020] Step S2-2: Successively intercept, reshape, remove the cyclic prefix, matrixize, perform FFT transformation, and perform matched filtering on the OFDM symbols of each receiving antenna.

[0021] Step S2-3: Store the result after matched filtering into a three-dimensional complex matrix with a dimension of H×N×M.

[0022] Preferably, step S2-2 specifically includes:

[0023] (1) Intercept the first F samples with the same length as the transmitted OFDM symbols from the OFDM symbols of the receiving antenna and store them in the variable Rx0.

[0024] (2) Reshape the variable Rx0 into a matrix of (N, -1), and then transpose it to obtain the variable Rx1, where -1 represents the automatically calculated matrix dimension to ensure that the total number of elements remains unchanged after reshaping.

[0025] (3) Remove the cyclic prefix from the variable Rx1 and reshape it into a matrix Rx with a dimension of (N - CPsize, -1), where CPsize is the size of the cyclic prefix.

[0026] (4) Perform FFT transformation on the matrix Rx to obtain a complex matrix Rx_dem with a dimension of (N - CPsize, M).

[0027] (5) Perform element-wise multiplication of the complex matrix Rx_dem with the conjugate complex number of the transmitted OFDM symbols to eliminate the influence of 5G AeroMACS transmission.

[0028] Preferably, step S3 specifically includes:

[0029] Step S3-1: Divide the three-dimensional complex matrix into sub-tensors with a size of , where , , ;

[0030] Step S3-2: Expand and merge each sub-tensor by columns to obtain column vectors;

[0031] Step S3-3: Perform covariance matrix calculation.

[0032] Step S3-4: Estimate the number of signal subspace dimensions P using a deep learning model based on the covariance matrix with a time window length Q;

[0033] Step S3-5: Perform eigenvalue decomposition on the covariance matrix, and construct the signal subspace using the eigenvectors corresponding to the largest P eigenvalues. , and use the remaining eigenvectors to construct the noise subspace ;

[0034] Step S3-6: Construct a three-dimensional spectral peak search function to obtain the RVA spectrogram.

[0035] Preferably, the expression of the three-dimensional spectral peak search function in Step S3-6 is as follows:

[0036]

[0037] where represents the time delay of the target, represents the Doppler frequency shift of the target, represents the angle of the target, represents the three-dimensional time delay Doppler search column vector;

[0038]

[0039]

[0040]

[0041]

[0042] where represents the imaginary unit, represents the element spacing of the antenna array, represents the wavelength of the 5G AeroMACS signal.

[0043] Preferably, Step S4 specifically includes:

[0044] Step S4-1: Set the false alarm probability ;

[0045] Step S4-2: Preprocess the RVA spectrogram;

[0046] Step S4-3: Determine the current detection area, including the central detection unit, the protection area, and the reference area: For each detection unit in the RVA spectrogram, set widths of , , The protected area is symmetrically set in the dimensions of distance, speed, and angle; the side lengths of the reference area in the dimensions of distance, speed, and angle are respectively , , ;

[0047] Step S4-4, noise amplitude estimation:

[0048] Calculate the weighted value of the signal intensities of all cells within the current detection area during the time window length Q as the average background clutter level Z ;

[0049] Step S4-5, determine the threshold factor according to the false alarm probability , the volume of the reference area, and the time window length Q;

[0050] Step S4-6, for each detection cell, use the threshold factor and the average background clutter level Z to calculate the detection threshold ;

[0051] Step S4-7, target detection: Compare the signal intensity of each detection cell with the detection threshold . If the signal intensity of the detection cell exceeds the detection threshold , it is considered that there is a target in the detection cell, and record the position of the detection cell as the target position.

[0052] Preferably, the expression of the average background clutter level Z is as follows:

[0053]

[0054] Among them, represents the time-weighted forgetting factor, represents the q th moment, the signal intensity of the cell with the distance dimension index i , speed dimension index j , and angle dimension index k within the current detection area, represents the weight value of whether the cell with the distance dimension index i , speed dimension index j , and angle dimension index k within the current detection area is located in the reference area. When the cell is a detection cell, , when the cell is located within the protected area, ; when the cell is located within the reference area, , represents the distance-weighted standard deviation, represents the speed-weighted standard deviation, Represents the angular weighted standard deviation;

[0055]

[0056]

[0057]

[0058] Wherein, Represents the speed of light, Represents the subcarrier spacing of the 5G AeroMACS signal, Represents the OFDM symbol time of the 5G AeroMACS signal.

[0059] Preferably, the threshold factor The expression is:

[0060]

[0061] Wherein, Represents the expectation of calculating the weight value tensor.

[0062] Preferably, the detection threshold The expression is as follows:

[0063] .

[0064] Compared with the prior art, the present invention has the following advantages:

[0065] 1. A multi-dimensional super-resolution target perception method based on 5G AeroMACS proposed by the present invention applies 5G AeroMACS technology, OFDM technology, and the characteristics of communication and sensing integration. Utilizing the high-bandwidth and low-latency data transmission capabilities provided by 5G AeroMACS ensures the real-time transmission and processing of monitoring data. By mapping the data to be transmitted onto OFDM symbols and using subcarriers within the dedicated aviation frequency range of 5091 - 5150 MHz, performing IFFT operations to convert the frequency-domain signal into a time-domain signal, and adding a cyclic prefix to prevent inter-symbol interference caused by multipath effects. Finally, the modulated OFDM symbols are transmitted into the air through the transmitting antenna, making full use of the characteristics of 5G technology such as low latency, high reliability, and large bandwidth.

[0066] 2. A multi-dimensional super-resolution target perception method based on 5G AeroMACS proposed by the present invention divides a three-dimensional complex matrix into multiple sub-tensors, calculates the covariance matrix and performs eigenvalue decomposition to construct a signal subspace and a noise subspace. By constructing a three-dimensional time-delay Doppler search column vector and a three-dimensional spectral peak search function, precise positioning of the target is achieved. In the target detection stage, a three-dimensional constant false alarm detection algorithm is adopted. By setting the false alarm probability, preprocessing the RVA spectrogram, determining the protection area and the reference area, estimating the noise amplitude, and calculating the detection threshold and other steps, high-accuracy detection of the target is realized.

[0067] 3. A multi-dimensional super-resolution target perception method based on 5G AeroMACS proposed by the present invention improves the accuracy and reliability of target perception through multi-dimensional data processing and super-resolution technology. At the same time, by optimizing the signal transmission and reception process and adopting advanced signal processing algorithms, the present invention can achieve fast and precise detection of targets in a complex airport environment, providing strong technical support for airport safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. By referring to the drawings, the features and advantages of the present invention can be more clearly understood. The drawings are schematic and should not be construed as limiting the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0069] Figure 1 is a flowchart of the multi-dimensional super-resolution target perception method based on 5G AeroMACS of the present invention.

[0070] Figure 2 is a deep learning model diagram for estimating the dimension number of the signal subspace in the present invention.

[0071] Figure 3 is a schematic diagram of the three-dimensional complex matrix and the three-dimensional sub-tensor in the present invention.

[0072] Figure 4 is a schematic diagram of the division of the data tensor detection area, protection area, and reference area in the present invention.

[0073] Figure 5 is an estimated result diagram of setting a single-target scenario (900m, -20m / s, -32 degrees).

[0074] Figure 6It is a graph of the estimated results for setting a dual - target scenario (900m, - 20m / s, - 32 degrees; 600m, - 30m / s, 9 degrees; 500m, 40m / s, 20 degrees; 300m, 50m / s, 38 degrees). Detailed implementation manner

[0075] 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 implementation manners. 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.

[0076] In the following description, many specific details are set forth in order to fully understand the present invention. However, 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.

[0077] Due to the large difference in the sizes of the targets on the airport surface, it is particularly necessary to adopt super - resolution technology. It is necessary to jointly identify the targets from multiple dimensions such as distance, speed, angle, etc. As Figure 1 shown, in 5G AeroMACS, both the signal transmission and reception processes follow the physical - layer signal transmission process of 5G communication, and at the same time, the characteristics of OFDM technology and communication - sensing integration are combined.

[0078] 1. 5G AeroMACS signal transmission

[0079] (1) Data preparation: In 5G AeroMACS, the data - preparation stage involves mapping the data to be transmitted (such as high - precision airport digital maps, runway occupancy situations, real - time positions of aircraft, etc.) onto M OFDM symbols. Each OFDM symbol contains N sub - carriers, and these sub - carriers are within the aviation - dedicated frequency range of 5091 - 5150 MHz.

[0080] (2) IFFT operation: Perform an IFFT operation on each OFDM symbol to convert the frequency - domain signal into a time - domain signal. The output of the IFFT operation is a time - domain OFDM symbol with a length of N.

[0081] (3) Adding a cyclic prefix (CP): To prevent inter - symbol interference (ISI) caused by multipath effects, a cyclic prefix (CP) is added to the tail of each OFDM symbol.

[0082] (4) Signal transmission: Transmit the modulated OFDM symbols into the air through the transmitting antenna, taking advantage of the characteristics of 5G technology, namely low latency, high reliability, and large bandwidth.

[0083] 2. 5G AeroMACS signal reception

[0084] Signal reception is performed using H receiving antennas. Due to the multipath effect, the signals received by each receiving antenna may be different.

[0085] Each receiving antenna will obtain a data matrix of dimension N×M, which contains the data of all subcarriers received by this receiving antenna on M OFDM symbols. Stack all H N×M data matrices to form a three-dimensional data tensor with dimensions H×N×M.

[0086] Where, H: the number of receiving antennas; N: the number of subcarriers in each OFDM symbol; M: the number of OFDM symbols.

[0087] (1) Initialize the three-dimensional complex matrix CIM_3dfft:

[0088] Create a three-dimensional complex matrix CIM_3dfft with dimensions (H, M, N).

[0089] The real part of the three-dimensional complex matrix CIM_3dfft is initialized to 0, and the imaginary part is also initialized to 0, and then added with the imaginary unit 1j, so that each element in the three-dimensional complex matrix CIM_3dfft is a complex number 0.

[0090] (2) Traverse each receiving antenna:

[0091] Use a loop for ii in range(H): to traverse each receiving antenna. For each receiving antenna, perform the following steps.

[0092] a) Intercept the received signal of the same length as the transmitted signal:

[0093] Intercept the first TxSignal_cp.shape[0] samples from the received signal RxSignal[ii] of the ii-th receiving antenna. This part is the same length as the transmitted signal TxSignal_cp and is stored in the variable Rx0.

[0094] b) Reshape the received signal:

[0095] Reshape the intercepted variable Rx0 into a matrix of (N, -1), and then transpose it to get the variable Rx1. Here, -1 represents the automatically calculated matrix dimension to ensure that the total number of elements remains unchanged after reshaping.

[0096] c) Remove the cyclic prefix (CP) and reshape it into a matrix:

[0097] Remove the cyclic prefix (CP) from the variable Rx1 to combat the multipath effect. After removing the cyclic prefix (CP), reshape the signal into a matrix Rx with dimensions (N - CPsize, -1), where CPsize is the size of the cyclic prefix.

[0098] d) Perform FFT transformation on the signal after removing the cyclic prefix (CP):

[0099] Perform FFT transformation on the matrix Rx to obtain a complex matrix Rx_dem with dimensions (N - CPsize, M). The FFT transformation converts the time-domain signal into a frequency-domain signal for the next step of processing.

[0100] e) Transmitted information cancellation (matched filtering):

[0101] Perform element-wise multiplication of the complex matrix Rx_dem with the conjugate complex number np.conj(TxData) of the transmitted signal to eliminate the influence of 5G AeroMACS transmission.

[0102] (3) Put the processed data into the corresponding layer of the 3D matrix:

[0103] Put the result CIM_2dfft after matched filtering for each receiving antenna into the three-dimensional complex matrix CIM_3dfft, where the result of the ii-th receiving antenna is correspondingly stored in the ii-th layer of the three-dimensional complex matrix CIM_3dfft. In this way, the processing results of each receiving antenna are stored in the three-dimensional complex matrix CIM_3dfft, facilitating subsequent analysis and processing.

[0104] 3. Multi-dimensional super-resolution spectrogram estimation

[0105] (1) As Figures 2 - 3 shown, divide the three-dimensional complex matrix CIM_3dfft into sub-tensors of size . Among them, the number of rows , the number of columns , and the number of layers . If C, S, and D are larger, the sub-tensor is larger and the number of them is smaller. The number of sub-tensors is .

[0106] The division process starts from the first layer and the first row, shifts to the right along the column, and when reaching the rightmost end, changes to the second row for shifting. When a certain layer is completed, start the shifting of the next layer. According to this process, all sub-tensors are divided.

[0107] Among them, the first sub-tensor unfolded by layer can be expressed as:

[0108]

[0109] Among them, represents the data in the -th layer of the first sub-tensor , , is defined as:

[0110]

[0111] Among them, represents the data value in the c th row, s th column, and d th layer of the data tensor.

[0112] Among them, the second sub-tensor unfolded by layer can be expressed as:

[0113]

[0114] Among them, represents the data of the second sub-tensor in the th layer and is defined as:

[0115]

[0116] The last sub-tensor unfolded by layer can be expressed as:

[0117]

[0118] Among them, represents the data of the last sub-tensor in the th layer and is defined as:

[0119]

[0120] (2) Combine all sub-tensors by column, that is, convert the sub-tensors into a column vector.

[0121]

[0122]

[0123] (3) Therefore, the result of covariance matrix calculation can be obtained as:

[0124]

[0125] Among them, R represents the covariance matrix, represents the column vector obtained by converting the data value in the c th row, s th column, and d th layer of the data tensor.

[0126] (4) Estimate the signal subspace dimension number P based on the deep learning model.

[0127] The covariance matrix of the continuous time window length Q is considered as the input for the deep learning model. For the covariance matrix input at each moment, the features of the input covariance matrix are extracted through convolution - activation function - batch normalization operations, the low - dimensional features are converted into vectors through a flattening operation, and the vectors at each moment are processed and fused through a long short - term memory network layer, and finally the directly corresponding signal subspace dimension number P is output.

[0128] The deep learning model needs to be pre - trained. The dataset required for pre - training is generated based on different signal - to - noise ratios and different numbers of targets, or based on the actual 5G AeroMACS integrated communication and sensing echo of the airport surface.

[0129] (5) Perform eigenvalue decomposition on the correlation matrix:

[0130]

[0131] Among them, represents the eigenvector matrix, represents the eigenvalue diagonal matrix.

[0132] Use the eigenvectors corresponding to the largest P eigenvalues to construct the signal subspace , and use the remaining other eigenvectors to construct the corresponding noise subspace .

[0133] (6) Construct a three - dimensional spectral peak search function. First, a corresponding three - dimensional time - delay Doppler search column vector needs to be constructed, which can be expressed as:

[0134]

[0135] Among them,

[0136]

[0137]

[0138]

[0139] Thus, the three - dimensional spectral peak search function can be written as:

[0140]

[0141] Among them, represents the time - delay amount of the target, represents the Doppler frequency shift amount of the target, represents the angular amount of the target, represents the three - dimensional time - delay Doppler search column vector, represents the imaginary unit, represents the element spacing of the antenna array, and [[ID=]] represents the wavelength of the 5G AeroMACS signal.

[0142] 4. Multidimensional Constant False Alarm Target Detection

[0143] The final target perception result is obtained through a three-dimensional constant false alarm detection algorithm.

[0144] (1) Set the false alarm probability .

[0145] (2) Perform necessary preprocessing on the RVA spectrogram, padding zeros around to reduce edge effects.

[0146] (3) As shown in [[ID=]], determine the current detection area, including the central detection unit and the protection area and reference area: For each detection unit (Cell Under Test, CUT) in the RVA spectrogram, set protection areas with widths of Figure 4 , , , respectively around it, symmetrically set in the range, velocity, and angle dimensions.

[0147] (4) Determine that the side lengths of the reference area in the three dimensions of range, velocity, and angle are , , respectively.

[0148] (5) Noise amplitude estimation:

[0149] Calculate the weighted value of the signal intensities of all units within the current detection area during the time window length Q, and use this as the average background clutter level Z . The formula is:

[0150]

[0151] Among them, represents the time-weighted forgetting factor, represents the signal intensity of the unit with range dimension index q , velocity dimension index i , and angle dimension index j within the current detection area at the k moment, represents the weight value indicating whether the unit with range dimension index i , velocity dimension index j , and angle dimension index k is located in the reference area. When the unit is a detection unit, , and when the unit is within the protection area, ; When the unit is within the reference area, , represents the distance-weighted standard deviation, represents the speed-weighted standard deviation, represents the angle-weighted standard deviation;

[0152]

[0153]

[0154]

[0155] Wherein, represents the speed of light, represents the 5G AeroMACS signal subcarrier spacing, represents the 5G AeroMACS signal OFDM symbol time.

[0156] (6) Determine the threshold factor according to the false alarm probability and the volume of the reference area:

[0157] In the case of average weighting, the expression of the threshold factor is:

[0158]

[0159] Otherwise,

[0160] Wherein, represents the expectation of calculating the weight value tensor.

[0161] (7) For each detection unit, use the threshold factor and the corresponding average background clutter level Z to calculate the detection threshold . The formula is:

[0162]

[0163] (8) Target detection: Compare the signal strength of each detection unit with the corresponding detection threshold. If the signal strength of the detection unit exceeds the detection threshold, it is considered that there is a target in the unit, and the position of the detection unit is recorded as the target position.

[0164] Embodiment 1

[0165] Assume that the 5G AeroMACS base station operates at 5.1 GHz. One frame of the transmitted OFDM signal contains 64 OFDM symbols. Each OFDM symbol contains 64 subcarriers. The subcarrier spacing is 60 kHz. The OFDM symbol time is 16.67 μs. The cyclic prefix length accounts for 25%, and the cyclic prefix time is 4.17 μs. The transmitted data uses 16QAM modulation. The array antenna is a linear array containing 16 array elements, and the element spacing is half a wavelength. When calculating the spectrogram, the number of spectral points in the distance dimension is set to 32, the number of spectral points in the velocity dimension is set to 32, and the number of spectral points in the angle dimension is set to 32.

[0166] Single-target scenario: There is 1 target in the signal coverage area, and the parameters are: distance 900 m, radial velocity -20 m / s, angle -32 degrees.

[0167] The number of targets inferred from the covariance matrix is 1, and the obtained three-dimensional super-resolution spectrogram is as Figure 5 , showing the cross-section of the three-dimensional spectrogram along the angle dimension, which are -37.74 degrees, -31.94 degrees, and -26.13 degrees respectively. It can be seen that a sharp spectral peak is formed at the cross-section (b) with the angle value closest to the target, while the peak values on the other cross-sections (a) and (c) are lower than that of (b).

[0168] The point cloud distance index obtained through target detection is 11, the velocity index is 15, and the angle index is 10.

[0169] Multi-target scenario: There are a total of 4 targets in the signal coverage area, and the parameters are respectively: distance 900 m, radial velocity -20 m / s, angle -32 degrees

[0170] distance 600 m, radial velocity -30 m / s, angle 9 degrees

[0171] distance 500 m, radial velocity 40 m / s, angle 20 degrees

[0172] distance 300 m, radial velocity 50 m / s, angle 38 degrees

[0173] The number of targets inferred from the covariance matrix is 4, and the obtained three-dimensional super-resolution spectrogram is as Figure 6 . Showing the cross-section of the three-dimensional spectrogram along the angle dimension, which are -60.97 degrees, -31.94 degrees, 8.71 degrees, 20.32 degrees, 37.74 degrees, and 60.97 degrees. In the angle value diagrams (a) and (f) far from the target, there are no obvious peaks, while in the cross-sections (b), (c), (d), and (e) with the angle values closest to the target, sharp spectral peaks are formed at the correct distance and correct velocity positions of the target.

[0174] The point cloud distance indices obtained through object detection are 11, 7, 6, 4, the speed indices are 15, 15, 16, 17, and the angle indices are 10, 17, 19, 22.

[0175] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A multi-dimensional super-resolution target perception method based on 5G AeroMACS, characterized in that, The following steps are involved: Step S1, using 5G AeroMACS to modulate the data to be transmitted into multiple OFDM symbols and transmit them; Step S2, multiple receiving antennas receive OFDM symbols in the air, perform data restoration and stack them into a three-dimensional data tensor, and store them in a three-dimensional complex matrix; Step S3, constructing a signal subspace, a noise subspace and a three-dimensional spectrum peak search function based on the three-dimensional complex matrix to obtain a multi-dimensional super-resolution spectrum; Step S4: using a three-dimensional constant false alarm detection algorithm to obtain a final target perception result; The step S3 specifically includes: Step S3-1: Divide the three-dimensional complex matrix into sub-tensors of size , where , , ; Among them, N represents the number of subcarriers included in each OFDM symbol, M represents the number of OFDM symbols, H represents the number of receiving antennas; Step S3-2: Expand each sub-tensor and merge them by columns to obtain column vectors; Step S3-3, calculating the covariance matrix; Step S3-4, based on the covariance matrix of the time window length Q, using the deep learning model to estimate the signal subspace dimension P; Step S3-5: Perform eigenvalue decomposition on the covariance matrix, and construct a signal subspace using the eigenvectors corresponding to the largest P eigenvalues. , and use the remaining eigenvectors to construct a noise subspace. ; Step S3-6, constructing a three-dimensional spectrum peak search function to obtain an RVA spectrum; The expression of the three-dimensional spectrum peak search function in step S3-6 is as follows: Among them, represents the time delay amount of the target, represents the Doppler frequency shift amount of the target, represents the angular amount of the target, represents a three-dimensional time delay Doppler search column vector.

2. The multi-dimensional super-resolution target perception method according to claim 1, wherein The step S1 specifically includes: Step S1-1, data preparation: using 5G AeroMACS to map the data to be transmitted into M OFDM symbols; Step S1-2, IFFT operation: perform IFFT operation on each OFDM symbol to convert the frequency domain signal into a time domain signal; Step S1-3, adding a cyclic prefix: adding a cyclic prefix at the end of each OFDM symbol; Step S1-4, signal transmission: the modulated OFDM symbols are sent into the air through a transmitting antenna.

3. The multi-dimensional super-resolution target perception method according to claim 2, wherein In step S1-1, each OFDM symbol includes N subcarriers, and the subcarriers are within the aviation-specific frequency range of 5091-5150 MHz.

4. The multi-dimensional super-resolution target perception method according to claim 3, wherein The step S2 specifically includes: Step S2-1, H receiving antennas each receive an OFDM symbol in the air; Step S2-2, sequentially intercepting, reshaping, removing cyclic prefixes, matrixing, performing FFT transformation and matched filtering on the OFDM symbols of each receiving antenna; Step S2-3: Store the result of matched filtering into a three-dimensional complex matrix with dimensions of H×N×M.

5. The multi-dimensional super-resolution target perception method according to claim 4, characterized in that The step S2-2 specifically includes: (1) Extract the first F samples of the OFDM symbol of the receiving antenna with the same length as the transmitted OFDM symbol and store them in the variable Rx0; (2) Reshape the variable Rx0 into a (N, -1) matrix and then transpose it to obtain the variable Rx1, where -1 represents the automatically calculated matrix dimension to ensure that the total number of elements remains unchanged after reshaping; (3) Remove the cyclic prefix from the variable Rx1 and reshape it into a matrix Rx with dimension (N-CPsize,-1), where CPsize is the size of the cyclic prefix; (4) Perform FFT transformation on the matrix Rx to obtain the complex matrix Rx_dem, whose dimension is (N-CPsize,M); (5) Perform element-by-element multiplication of the complex matrix Rx_dem with the complex conjugate of the transmitted OFDM symbol to eliminate the influence of 5G AeroMACS transmission.

6. The multi-dimensional super-resolution target perception method according to claim 5, wherein The said is expressed as: wherein, represents the imaginary unit, represents the element spacing of the antenna array, represents the wavelength of the 5G AeroMACS signal.

7. The multi-dimensional super-resolution target perception method according to claim 6, characterized in that The step S4 specifically includes: Step S4-1, set the false alarm probability ; Step S4-2, preprocessing the RVA spectrum; Step S4-3: Determine the current detection area, including the detection unit at the center, the protection area, and the reference area: For each detection unit in the RVA spectrogram, set protection areas with widths of , , around it, symmetrically set in the distance, speed, and angle dimensions; the side lengths of the reference area in the distance, speed, and angle dimensions are , , ; Step S4-4, noise amplitude estimation: Calculate the weighted value of the signal strengths of all cells within the current detection area during the time window length Q, and use it as the average background clutter level Z ; Step S4-5: Determine a threshold factor according to the false alarm probability , the volume of the reference area, and the time window length Q ; Step S4-6: For each detection unit, use the threshold factor and the average background clutter level Z to calculate the detection threshold ; Step S4-7, target detection: Compare the signal strength of each detection unit with the detection threshold If the signal strength of the detection unit exceeds the detection threshold , it is considered that there is a target in the detection unit, and the position of the detection unit is recorded as the target position.

8. The multi-dimensional super-resolution target perception method according to claim 7, wherein The expression of the average background clutter level Z is as follows: Among them, represents the time-weighted forgetting factor, denotes the q signal strength of the cell with distance dimension index i , speed dimension index j , and angle dimension index k at the moment in the current detection area; i denotes the weight value indicating whether the cell with distance dimension index j , speed dimension index k , and angle dimension index is located in the reference area. When the cell is a detection cell, , when the cell is located in the protection area, ; when the cell is located in the reference area, ; represents the distance-weighted standard deviation, represents the speed-weighted standard deviation, represents the angle-weighted standard deviation; wherein, represents the speed of light, represents the subcarrier spacing of the 5G AeroMACS signal, represents the OFDM symbol time of the 5G AeroMACS signal; The threshold factor has the following expression: Among them, represents the expectation of the tensor of the calculated weight value.

9. The multi-dimensional super-resolution target perception method according to claim 8, wherein Detection threshold The expression is as follows: 。

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