A method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO

Abstract

This invention discloses a low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO. First, the user aircraft sends pilot signals to the base station, and a beamforming algorithm under a fully connected architecture is designed. Then, a beamforming algorithm capable of obtaining channel images with clear key point features under low pilot overhead is designed. Based on the above hybrid beamforming architecture, the channel image is obtained. An image key point detection algorithm is designed to acquire and fine-tune the aircraft's position information within the observation area at the first moment. In subsequent moments, an efficient channel tracking module is designed. Then, based on the estimated aircraft position information, channel reconstruction and trajectory matching are completed. Finally, a trajectory-based low-altitude aircraft surveillance algorithm is designed to achieve trajectory error detection, aircraft signal interruption detection, and unauthenticated intruding aircraft detection within the base station's authentication range. This invention enables rapid uplink channel estimation, ensuring the real-time performance and reliability of the communication link between the aircraft and the base station.

Description

Technical Field

[0001] This invention relates to a method for low-altitude aircraft trajectory detection and channel tracking based on ultra-large-scale MIMO, belonging to the field of wireless communication technology. Background Technology

[0002] The sixth-generation mobile communication technology (6G) has been extensively researched globally. As the next-generation communication technology, it will provide higher network speeds, connection density, and intelligent capabilities, greatly promoting the development potential of the digital economy. With continuous technological advancements and the expansion of application scenarios, the development of the low-altitude economy has gradually become one of the important directions driving the development of the next-generation digital economy. The growth in market demand, breakthroughs in technological innovation, and the improvement of the industrial chain have propelled the rapid development of the low-altitude economy. The application of low-altitude aircraft (such as drones and electric vertical take-off and landing aircraft) in multiple fields such as logistics distribution, agricultural monitoring, and urban air travel is changing the traditional industrial landscape. At the same time, the improvement of relevant policies and regulations has laid a solid foundation for the healthy and sustainable development of the low-altitude economy. By developing low-altitude airspace, the low-altitude economy is becoming an important engine for driving economic growth, improving industrial efficiency, and promoting technological innovation.

[0003] The development of the low-altitude economy has not only promoted the opening and management of low-altitude airspace but also spurred the growth in demand for low-altitude flight communication. Especially with the support of 6G networks, the communication needs of low-altitude aircraft are experiencing explosive growth. Low-altitude aircraft have an extremely urgent need for high-speed, low-latency communication, and as one of the key technologies of 6G, very large-scale MIMO technology offers a wealth of application scenarios. Combining very large-scale MIMO technology with the applications of the low-altitude economy can provide more efficient communication support and positioning applications for low-altitude aircraft. Through spatial division multiplexing technology, very large-scale antenna arrays can effectively improve the communication and positioning capabilities of low-altitude aircraft. However, with the rapid increase in array size, traditional all-digital hardware architectures face enormous hardware cost pressures, especially in very large-scale MIMO systems, where the hardware overhead of transceivers rises sharply, becoming a major obstacle to its widespread adoption and practical application. At the same time, with the increase in array size and the generation of near-field effects, the complexity of user positioning and channel estimation increases accordingly, further increasing the challenges of very large-scale MIMO in practical applications. To reduce hardware costs and computational complexity while maintaining accuracy in user positioning and channel estimation, hybrid beamforming architecture has emerged as a potential solution. Hybrid beamforming technology reduces the number of RF links and lowers system hardware costs through a layered design of signal processing between RF and baseband. Meanwhile, the dynamic characteristics of low-altitude aircraft present challenges for ultra-large-scale MIMO systems with time-varying channels, requiring the integration of advanced technologies such as deep learning and image processing to reduce computational complexity and improve spectral efficiency.

[0004] The combination of 6G ultra-large-scale MIMO technology and the low-altitude economy will bring enormous innovative potential to future communication systems. Through technological innovation and rational architectural design, it is expected to overcome existing bottlenecks in hardware cost and computational complexity, promoting the widespread deployment of ultra-large-scale MIMO technology in practical applications and providing robust communication support for the low-altitude economy in the 6G era. Therefore, researching low-cost hardware architectures, hybrid beamforming algorithms, and low-complexity aircraft trajectory and channel tracking schemes is particularly important to meet the needs of ultra-large-scale MIMO applications in the low-altitude economy. These technologies will help improve the communication and positioning quality of low-altitude aircraft, promote the rapid development of the low-altitude economy, and further advance the application of 6G technology in the low-altitude economy, supporting future communication needs. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO, design a beamforming matrix that can recover a channel image with obvious features, recover the channel image from the signal received from the base station, design a key point detection network based on the image, obtain the position of the aircraft, design a channel extrapolation scheme, and design the trajectory tracking of the aircraft based on the position information at all times, so as to achieve low-cost and low-complexity aircraft identification and trajectory tracking.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO is disclosed. The ultra-large-scale MIMO system within a preset observation area includes a base station, an authenticated user vehicle, and S scatterer vehicles, including both authenticated and unauthenticated vehicles. The base station is equipped with M uniform linear array antennas and adopts a hybrid beamforming architecture. The base station antennas are divided into Q subarrays, each subarray having M / Q antennas and M / 2Q pilots.

[0008] The trajectory detection and channel tracking method includes the following steps:

[0009] Step 1: The user aircraft sends pilot signals to the base station. The base station designs a beamforming matrix under a hybrid beamforming architecture and recovers the channel image from the signals received from the base station.

[0010] Step 2: Use the key point detection network to perform network inference on the channel image recovered from the signal received from the base station to obtain the coarse estimated position of the user aircraft and all scatterer aircraft.

[0011] Step 3: For the channel estimation at the first moment, the coarse estimated position obtained in Step 2 is optimized using orthogonal matching pursuit optimization to obtain the estimated position; the path gain is obtained by using the least squares algorithm on the base station received signal to realize channel estimation.

[0012] Step 4: For channel tracking at subsequent time points, determine whether there is a path miss at the current time based on the received signal. If so, perform channel estimation at the current time point according to the channel estimation method of the first time point in Step 3. Otherwise, perform channel extrapolation using orthogonal matching pursuit optimization based on the aircraft position and mobile range estimated in the previous time point to achieve channel tracking.

[0013] Step 5: Based on the estimated positions of each aircraft at all times, the Hungarian algorithm is used to match and associate the positions at different times to obtain the movement trajectory of each aircraft.

[0014] Step 6: The base station compares the movement trajectory of each aircraft with the paths of the certified user aircraft and the certified aircraft to correct the trajectory of each aircraft.

[0015] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0016] 1. This invention, by designing a hybrid beamforming matrix, can adapt to low-complexity aircraft position estimation and channel estimation based on image key point detection with lower hardware cost and pilot overhead.

[0017] 2. By using high-precision image key point target detection, this invention can estimate the position of all aircraft at once, effectively reducing the complexity of the method while achieving channel estimation accuracy that surpasses that of orthogonal matching schemes with the same codebook size.

[0018] 3. This invention effectively tracks the trajectory of an aircraft by matching the estimated positions at different times using the Hungarian algorithm.

[0019] 4. This invention designs an aircraft monitoring algorithm based on estimated trajectory, which can effectively and accurately detect trajectory errors, signal interruptions, and intrusions of low-altitude aircraft. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of a low-altitude network under a large-scale MIMO system provided by the present invention;

[0021] Figure 2 This is a schematic diagram of the aircraft position detection scheme based on a key point detection network provided by the present invention;

[0022] Figure 3This is a flowchart of the low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO provided by the present invention. Detailed Implementation

[0023] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0024] In this invention, the ultra-large-scale MIMO system within the preset observation area consists of a base station, a certified user spacecraft, and S scatterer spacecraft, such as... Figure 1 As shown. The scatterer aircraft includes both certified and uncertified aircraft. The base station is equipped with M uniform linear array antennas and employs a hybrid beamforming architecture. The base station antennas are divided into Q subarrays, each subarray containing M / Q antennas and M / 2Q pilots.

[0025] The channel between the base station and the user can be represented as:

[0026]

[0027] Where L(t) represents the path number at time t, i.e., the number of aircraft within the observation area, and g l (t) represents the path complex gain of the l-th path at time t, (z) l (t),x l (t) represents the position of the l-th spacecraft at time t in the Cartesian coordinate system, a(z) l (t),x l (t)) and d(z) l (t),x l (t) represents the steering vector and the Doppler frequency shift vector, respectively. The m-th element in the steering vector can be modeled as:

[0028]

[0029] Where, k c Represents wave number, D m (z,x) represents the distance between the aircraft and the m-th antenna, which can be expressed as:

[0030]

[0031] Where d is the antenna spacing, i.e., half a wavelength. The m-th element in the Doppler frequency shift vector can be modeled as:

[0032]

[0033] Where v(t) represents the moving speed, Nsf T represents the number of subframes. sf φ represents the duration of a subframe. m This represents the angle between the direction of movement and the x-axis. The motion model of an aircraft in a time-varying scenario is defined as follows: Observations are conducted in a near-field region within a certain range. There is one user aircraft and S scatterer aircraft, some of which are mobile during the observation time, while others remain stationary. The paths at adjacent time points are represented by probabilities P. a P d 、(1-P a -P d The occurrence of path addition, annihilation, and unchanged path count. The positional relationship of the same spacecraft between adjacent moments is represented as follows:

[0034] (z t+1 ,x t+1 )=(z t +v t T s ·sinα0(t+1),x t +v t T s ·cosα0(t+1))

[0035] Here, α0 represents the angle between the direction of motion and the x-axis. During initialization, the direction of motion α0 is randomly initialized within the range [0, 2π]. In subsequent time steps, the change in the direction of motion remains within a certain range, i.e., it satisfies...

[0036] α l (t)=α l (t-1)(1+δ α (t)γ α,max )

[0037] Where, γ α,max δ represents the maximum range of differences in the direction of motion between adjacent moments. α (t) represents the rate of change.

[0038] Based on the above background explanation, such as Figure 3As shown, this invention proposes a low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO. It designs a beamforming matrix capable of recovering a channel image with distinct characteristics, recovers the channel image from the base station received signal, designs an image-based key point detection network to obtain the aircraft's position, designs a channel extrapolation scheme, and designs the aircraft's trajectory tracking based on position information at all times. This enables low-cost, low-complexity aircraft identification and trajectory tracking, thereby accurately achieving trajectory error detection, signal interruption detection, and intrusion detection for low-altitude aircraft. Furthermore, it enables rapid uplink channel estimation, ensuring the real-time performance and reliability of the communication link between the user's aircraft and the base station. Specifically, when the number of subarrays Q is 2, analog beamforming is designed and used; when the number of subarrays is larger, hybrid beamforming includes analog beamforming and digital beamforming. The received signal can be written as y(t), and the converted received image can be written as y(t). img (t) The keypoint detection network detects the positions of the user's aircraft and the scatterer aircraft from the received image. After optimization, the path complex gain is obtained using the least squares algorithm, and then substituted into the channel model to complete channel reconstruction. The channel tracking module extrapolates the channel using the received signal and the estimated position of the aircraft at the previous moment. Then, based on all the estimated positions, the Hungarian algorithm is used to match the positions at different times and form the motion trajectory of all aircraft. Finally, the base station compares the known trajectory with the estimated trajectory to achieve trajectory error detection, signal interruption detection, and intrusion detection for low-altitude aircraft.

[0039] Specifically, the steps include the following:

[0040] Step 1, Beamforming Stage: The model of a very large-scale MIMO system is as follows... Figure 1 As shown, the user aircraft sends pilot signals to the base station. The base station, under a partially connected molecular array hybrid beamforming architecture, designs a simulated beamforming matrix W when the number of subarrays Q is 2. A It is a diagonal matrix, which enables the recovery of a channel image with distinct propagation path characteristics from the received signal; that is...

[0041]

[0042] Q = 2, where These represent the first M / 4 rows and the last M / 4 rows of the M / 2-point Discrete Fourier Transform (DFT) matrix, respectively. The received signal can be represented as... in

[0043]

[0044] h1(t) and h2(t) are the first M / 2 elements and the last M / 2 elements of channel h, respectively.

[0045] When the number of subarrays Q is greater than 2, hybrid beamforming includes both digital and analog beamforming, enabling the recovery of a channel image of the same quality as when the number of subarrays Q is 2 from the received signal. The received signal can be represented as... Where F B It can be represented as:

[0046]

[0047] It can be represented as:

[0048]

[0049] M p,q It can be represented as:

[0050]

[0051] W B It can be represented as:

[0052]

[0053] When q is less than or equal to Q / 2, the simulated beamforming matrix of the q-th subarray can be expressed as:

[0054]

[0055] When q is greater than Q / 2, the simulated beamforming matrix of the q-th subarray can be expressed as:

[0056]

[0057] in, and These are the simulated beamforming matrices for the first and second subarrays of the simulated beamforming matrix when Q=2, respectively. Under this simulated beamforming algorithm, Q (Q>2) subarrays can obtain the same received pilot signal as Q=2 subarrays.

[0058] Under the hybrid beamforming matrix architecture, the near-field conversion domain codebook used can be represented as follows: in, It is a near-field transformation domain codebook under an all-digital architecture, where each element is a guide vector at the sampling point location.

[0059] The channel image can be generated by the following formula:

[0060]

[0061] Step 2, Aircraft Position Estimation Stage: Using methods such as... Figure 2The key point detection network shown is trained on a pre-labeled training set. During testing, the channel image recovered from the signal received from the base station is input to obtain a coarse estimate of the aircraft's position.

[0062] The network first extracts general image features using a ResNet-50 backbone network. Then, it passes through three identical detection heads: a 3x3, 64-channel convolutional kernel, a normalization layer, a ReLU activation layer, and a 1x1, 2-channel convolutional kernel. Each head outputs three feature maps of equal length and width: a heatmap, a coordinate prediction offset feature map, and a target size feature map. The value of each point on the heatmap represents the probability that the target's center point falls at that point. The coordinate prediction offset feature map represents the predicted coordinate offset for each point on the corresponding heatmap, and the target size feature map represents the predicted target size for each point on the corresponding heatmap. During inference, the network output passes through a score threshold filter, removing keypoints with low predicted probabilities from the heatmap, thus providing the final keypoint prediction values. This results in a coarse estimate of the positions of all user aircraft and scatterer aircraft obtained through a single network inference step.

[0063] Step 3, First-moment channel estimation stage: Based on the aircraft position information obtained in Step 2 We design an orthogonal matching tracing optimization using a small-range, fine-grained codebook near each path to obtain accurate location information. The path gain is obtained using the least squares algorithm. Implement channel estimation.

[0064] Step 4, Channel Tracking Phase at Subsequent Moments: Based on the position information estimated in the previous moment. Based on the received signal, determine if there are any missed path detections. If the residual signal satisfies the following formula, repeat step three to perform channel estimation at this moment:

[0065] ∥(a(x,z)) H y res (t)∥ 2 >=σ 2 (ln(M)-ln(-ln(1-P fa )))

[0066]

[0067] Among them, P fa This represents the false alarm probability. Otherwise, channel tracking can be completed simply by using orthogonal matched pursuit to extrapolate the channel from the previously detected aircraft position and its movable range.

[0068] Step 5, Aircraft Detection Position Association and Trajectory Matching Stage: Based on the estimated aircraft position information at all times, the Hungarian algorithm is used to match and associate the detected positions at different times. All positions estimated at two consecutive times are considered as two bipartite graphs. The Hungarian algorithm is used to achieve maximum matching of the bipartite graphs with a cost of 1-IOU, where IOU is the intersection-union ratio of the detection boxes. This yields the optimal matching of the aircraft position estimates at different times and forms the movement trajectory.

[0069] Step Six, Aircraft Monitoring Phase: The base station compares the estimated positions of user aircraft and scattering aircraft with the path set of certified aircraft on the base station. For certified aircraft, if their position is not detected at a certain moment, it is determined that the aircraft's signal is interrupted at that moment, and the true position of the aircraft is determined by estimating the trajectory information and compared with the preset trajectory to achieve trajectory error detection. For uncertified aircraft, if the detected position information deviates significantly from all the preset trajectories of certified aircraft on the base station at certain consecutive moments, the aircraft is determined to be an intrusion aircraft.

[0070] Based on the same inventive concept, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for low-altitude aircraft trajectory detection and channel tracking based on ultra-large-scale MIMO.

[0071] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO.

[0072] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO, wherein the ultra-large-scale MIMO system within a preset observation area includes a base station, a certified user vehicle, and S scatterer vehicles, wherein the S scatterer vehicles include certified vehicles and uncertified vehicles; the base station is configured with M uniform linear array antennas and adopts a hybrid beamforming architecture; the base station antennas are divided into Q subarrays, each subarray having M / Q antennas and M / 2Q pilots; Its features are, The trajectory detection and channel tracking method includes the following steps: Step 1: The user aircraft sends pilot signals to the base station. The base station designs a beamforming matrix under a hybrid beamforming architecture and recovers the channel image from the signals received from the base station. Step 2: Use the key point detection network to perform network inference on the channel image recovered from the signal received from the base station to obtain the coarse estimated position of the user aircraft and all scatterer aircraft. Step 3: For the channel estimation at the first moment, the coarse estimated position obtained in Step 2 is optimized using orthogonal matching pursuit optimization to obtain the estimated position; The path gain is obtained by using the least squares algorithm on the base station received signal, thereby achieving channel estimation; Step 4: For channel tracking at subsequent time points, determine whether there is a path miss at the current time based on the received signal. If so, perform channel estimation at the current time point according to the channel estimation method of the first time point in Step 3. Otherwise, perform channel extrapolation using orthogonal matching pursuit optimization based on the aircraft position and mobile range estimated in the previous time point to achieve channel tracking. Step 5: Based on the estimated positions of each aircraft at all times, the Hungarian algorithm is used to match and associate the positions at different times to obtain the movement trajectory of each aircraft. Step 6: The base station compares the movement trajectory of each aircraft with the paths of the certified user aircraft and the certified aircraft to correct the trajectory of each aircraft.

2. The method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO according to claim 1, characterized in that, The specific process of step 1 is as follows: The user aircraft sends pilot signals to the base station. Under a hybrid beamforming architecture, when the number of subarrays Q equals 2, the base station designs a simulated beamforming matrix W. A ,Right now Among them, W A It is a diagonal matrix. These are the first M / 4 rows and the last M / 4 rows of the M / 2-point discrete Fourier transform matrix, respectively. The base station received signal y(t) is represented as: Among them, P r Let n(t) be the received power at the base station, and n(t) be the Gaussian white noise. h1(t) and h2(t) are the first M / 2 elements and the last M / 2 elements of the uplink channel h, respectively; h(t) represents the uplink channel between the base station and the user aircraft, expressed as: Where L(t) represents the number of paths at time t, i.e., the number of aircraft within the preset observation area, and g l (t) represents the path complex gain of the l-th path at time t, (z) l (t),x l (t) represents the position of the l-th spacecraft at time t in the Cartesian coordinate system, a(z) l (t),x l (t)) and d(z) l (t),x l (t) represents the steering vector and the Doppler frequency shift vector, respectively; The m-th element in the guide vector [a(z l (t),x l (t))] m The model is as follows: The m-th element in the Doppler frequency shift vector [d(z l (t),x l (t))] m The model is as follows: Among them, D m (z l (t),x l (t) represents the distance between the user's aircraft and the m-th antenna. d is the antenna spacing, i.e., half wavelength; k c Let v(t) be the wave number, v(t) be the speed, and t be the velocity. sf N represents the subframe duration. sf φ is the number of subframes. m The angle between the direction of movement and the x-axis; When the number of subarrays Q is greater than 2, design the digital beamforming matrix F. B and simulated beamforming matrix W B This allows the channel image of the same quality as when the number of subarrays Q equals 2 to be recovered from the signal received from the base station; F B Represented as: in, for A matrix of all zeros. W B Represented as: The simulated beamforming matrix of the q-th subarray is represented as: The base station received signal y(t) is represented as: in, h1(t), h2(t) and h Q (t) represents the channels of the first, second, and Q-th subarrays, respectively; The channel image is generated by the following formula: Where, y′ img For channel image, For the near-field conversion domain codebook under the hybrid beamforming architecture, For a near-field transformation field codebook in an all-digital architecture, For the hybrid beamforming matrix W Hybrid The pseudo-reverse, W Hybrid =F B ⊙W B .

3. The method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO according to claim 1, characterized in that, In step 2, the key point detection network includes a skeleton network ResNet-50, a decoder, and three detection heads with the same structure. Each detection head includes a first convolutional layer, a normalization layer, a ReLU activation layer, and a second convolutional layer connected in sequence. The first convolutional layer has a kernel size of 3*3 and 64 channels, while the second convolutional layer has a kernel size of 1*1 and 2 channels. The ResNet-50 backbone network is used to extract features from the channel image recovered from the signal received from the base station. The decoder decodes the extracted features and sends them to three detector heads. The three detector heads output feature maps of the same length and width, namely a heat map, a coordinate prediction offset feature map, and a target size feature map. The value of each pixel on the heat map represents the predicted probability that the target center point falls on that point. The coordinate prediction offset feature map represents the predicted coordinate offset value of each point on the corresponding heat map. The target size feature map represents the predicted target size value of each point on the corresponding heat map. A fractional threshold filter is used to delete key points in the heat map whose predicted probability is less than a preset threshold, and the final key point prediction value is obtained, which is the coarse estimated position of the user aircraft and all scattering aircraft obtained by network inference.

4. The method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO according to claim 2, characterized in that, In step 4, determining whether there is a path miss based on the received signal specifically involves: If the residual signal satisfies the following formula, then there is a path missed detection: ∥(a(z l (t),x l (t))) H y res (t)∥ 2 >=σ 2 (ln(M)-ln(-ln(1-P fa ))) Among them, P fa This represents the probability of a false alarm. For the estimated path gain, This represents the estimated steering vector.

5. The method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO according to claim 1, characterized in that, The specific process of step 5 is as follows: Based on the estimated aircraft positions at all times, the Hungarian algorithm is used to match and associate the positions at different times. That is, all positions estimated at two consecutive times are regarded as two bipartite graphs. The Hungarian algorithm is used to achieve maximum matching of the bipartite graphs with a cost of 1-IOU, where IOU is the intersection-union ratio of the detection boxes. This yields the optimal matching of the aircraft position estimates at different times and forms the movement trajectory.

6. The method for low-altitude vehicle trajectory detection and channel tracking based on ultra-large-scale MIMO according to claim 1, characterized in that, The specific process of step 6 is as follows: The base station compares the estimated movement trajectories of user aircraft and scatterer aircraft with the preset paths of certified user aircraft and certified aircraft. For certified user aircraft or certified aircraft, if their position is not detected at a certain moment, it is determined that the signal of the aircraft was interrupted at the moment the position was not detected. The estimated position at the moment of signal interruption is taken as the true position of the aircraft and compared with the preset path to achieve trajectory error detection. For uncertified aircraft, if the movement trajectory formed by the positions detected at consecutive preset times is inconsistent with the preset paths of certified user aircraft and certified aircraft of the base station, it is determined to be an intrusion aircraft.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-altitude aircraft trajectory detection and channel tracking method based on ultra-large-scale MIMO as described in any one of claims 1 to 6.

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