Trajectory generation method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a trajectory generation method and device, electronic equipment and storage medium, and relates to the technical field of communication. The above method comprises: being applied to a near-field perception framework, the near-field perception framework comprising a UAV and a base station, the method comprising: the UAV and the base station performing two-dimensional angle estimation on a target, obtaining two-dimensional angles respectively, and generating a first ray and a second ray; the UAV generating a candidate three-dimensional position point of the target according to the intersection of the first ray and the second ray on the XOY plane, and the base station generating a candidate three-dimensional position point of the target according to the intersection of the first ray and the second ray on the XOY plane, so as to obtain two candidate three-dimensional position points; the base station estimates the speed of the target, processes the two candidate three-dimensional position points and the speed, and generates the position and speed of the target. The present disclosure proposes a near-field 3D UAV auxiliary perception framework and a corresponding perception algorithm, which can quickly and accurately estimate the position and speed of a user through 2D parameter scanning.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method, apparatus, electronic device, and storage medium for generating a trajectory. Background Technology

[0002] Sensing technology is considered a key component of 6G communication due to its significant enhancement of network adaptability. Introducing sensing capabilities into communication systems can improve network performance in multiple dimensions. For example, sensing technology enables smarter resource allocation, improves communication reliability, and supports various context-aware applications. Based on these advantages, wireless systems with sensing capabilities are particularly suitable for highly dynamic scenarios, such as unmanned aerial vehicle (UAV) communication networks. Advances in Integrated Sensing and Communication (ISAC) technologies have significantly improved hardware utilization efficiency, enabling UAVs to function as sensing nodes in wireless networks. The integration of sensing UAVs with local networks facilitates data offloading and in-flight computing, thereby enhancing sensing data processing capabilities. Therefore, sensing UAVs have emerged in various application scenarios. For example, sensing UAVs can assist in disaster recovery, road traffic management, and various data collection tasks. However, millimeter-wave (mmWave) communication and other high-frequency technologies place more stringent demands on future low-altitude communication networks, which in turn drives the development of near-field sensing UAV research.

[0003] The increasing array size and carrier frequency in 6G communication systems are pushing Rayleigh distances to hundreds of meters; therefore, drone communication scenarios are primarily in the near field. In these scenarios, applying near-field models is crucial for accurate and effective perception. However, research on near-field drone perception is very limited, and accuracy needs improvement. Summary of the Invention

[0004] In view of this, the purpose of this disclosure is to provide a method, apparatus, electronic device and storage medium for generating a trajectory, which can specifically solve the existing problems.

[0005] Based on the above objectives, in a first aspect, this disclosure proposes a trajectory generation method applied to a near-field perception framework, the near-field perception framework including a drone and a base station. The method includes: the drone and the base station performing two-dimensional angle estimation on a target to obtain two-dimensional angles respectively, and generating a first ray and a second ray; the drone generating candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray on the XOY plane; the base station generating candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray on the XOY plane, to obtain two candidate three-dimensional position points; the base station estimating the velocity of the target, processing the two candidate three-dimensional position points and the velocity, and generating the position and velocity of the target.

[0006] Secondly, a trajectory generation device is also provided, applied to a near-field perception framework, the near-field perception framework including a drone and a base station. The device includes: an estimation unit configured for the drone and the base station to perform two-dimensional angle estimation on a target, respectively obtaining two-dimensional angles, and generating a first ray and a second ray; a first generation unit configured for the drone to generate candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray in the XOY plane, and the base station to generate candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray in the XOY plane, to obtain two candidate three-dimensional position points; and a second generation unit configured for the base station to estimate the velocity of the target, process the two candidate three-dimensional position points and the velocity, and generate the position and velocity of the target.

[0007] Thirdly, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor running the computer program to implement the method of the first aspect.

[0008] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program being executed by a processor to implement the method described in any one of the first aspects.

[0009] Fifthly, a computer program product is also provided, comprising a computer program that is executed by a processor to implement the method described in any one of the first aspects.

[0010] In summary, this disclosure has at least the following beneficial effects: This disclosure proposes a near-field 3D UAV-assisted perception framework and a corresponding perception algorithm, which can quickly and accurately estimate the user's position and velocity through 2D parameter scanning. Attached Figure Description

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this disclosure and should not be construed as limiting the scope of this disclosure.

[0012] Figure 1a A flowchart of a trajectory generation method according to an embodiment of the present disclosure is shown; Figure 1b A schematic diagram of a near-field UAV perception framework in a trajectory generation method according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of the projection of the near-field velocity components at each antenna in a planar array of a trajectory generation method according to an embodiment of the present disclosure is shown. Figure 3 A schematic diagram illustrating an application scenario of a UAV-assisted dual-MUSIC algorithm according to an embodiment of the present disclosure is shown. Figure 4 A schematic diagram of a trajectory generation apparatus according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of a storage medium provided according to an embodiment of the present disclosure is shown. Detailed Implementation

[0013] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0014] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0015] Figure 1a The present disclosure illustrates a trajectory generation method applied to a near-field sensing framework, which includes a drone and a base station. In embodiments of the present disclosure, the method includes: In step S101, the UAV and the base station perform two-dimensional angle estimation on the target, obtain two-dimensional angles respectively, and generate a first ray and a second ray.

[0016] In step S102, the UAV generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray on the XOY plane, and the base station generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray on the XOY plane, so as to obtain two candidate three-dimensional location points.

[0017] In step S103, the base station estimates the velocity of the target, processes the two candidate three-dimensional location points and the velocity, and generates the position and velocity of the target.

[0018] like Figure 1b As shown, we consider a near-field drone perception framework consisting of a base station (BS) and a drone, which assists the BS by providing the location of the user (i.e., the target). The BS is deployed with... A uniform planar array (UPA) consisting of several antennas, while UAV sensing relies on... UPA array. (The last part is incomplete and likely refers to a specific array or array.) The index of each antenna is determined by Given, among which and We assume that the BS and UAV operate in full-duplex mode at different carrier frequencies, enabling them to transmit and receive signals simultaneously. The bandwidths of the UAV and BS are expressed as follows: and The corresponding symbol duration is and .set up and The number of coherent processing intervals (CPI) for BS and UAV, during which the rate remains constant and the duration is [duration missing]. and Similarly, let's assume... , This indicates the number of signs within CPI.

[0019] The absolute coordinate system is defined with its origin at the projection of the BS array center onto the XOY plane. Its X and Y axes are aligned with the columns and rows of the UPA, while the Z axis points upwards to complete the right-handed coordinate system. The centroid of the BS's UPA is located at... The position of the m-th antenna element in the array is represented as... Furthermore, the UAV's UPA geometric center is located in 3D coordinates. The first in the array The positions of the antenna elements are represented as follows: .definition For the goal in the The speed of each CPI, relative to the speed of each antenna in the BS and UAV arrays, is expressed as follows: and Based on the estimated velocity value, the trajectory prediction is updated by multiplying the velocity gradient and the CPI length. The predicted trajectory point for the next time slot is represented as follows: .

[0020] Considering the hovering energy assumption of UAVs, we assume that the speed sensing function is deployed on the BS side. In the 3D near-field scene, the parameters analyzed include radial distance. Azimuth and pitch angle Consider a mobile user with an unknown trajectory within the near-field range of both the BS and UAV, whose trajectory is in the first... The position of each CPI can be represented as To facilitate the generation of projection vectors, the received 3D near-field position parameters can be converted to Cartesian coordinates, represented as follows: The vectors from the user to the BS antenna and the UAV antenna can be represented as follows: and .

[0021] Accordingly, such as Figure 2 As shown in the figure, the projection of the near-field velocity components at each antenna is illustrated. The projection of the object velocity onto the 3D near-field parameters at the center of the BS array can be expressed as:

[0022]

[0023]

[0024]

[0025] Similarly, the parameters of a UAV can be exported as and . No. The distance from the user to the center of the BS and UAV antennas is represented in each CPI.

[0026] It can be given as and Using the distance difference between the user and each antenna, the array response can be expressed as:

[0027] This disclosure proposes a near-field 3D UAV-assisted perception framework and a corresponding perception algorithm, which can quickly and accurately estimate the user's position and velocity through 2D parameter scanning.

[0028] In some optional implementations of any embodiment of this disclosure, the method is applied to a near-field UAV perception framework, the framework consisting of the base station and the UAV, the base station being deployed with a uniform planar array of multiple antennas; the method further includes: determining the array response of the planar array based on the distance difference between the target and each antenna; projecting the velocity parameters of the target into three-dimensional near-field geometry to obtain Doppler frequency shift information; and determining the residual feedback signal based on the sensing signal transmitted corresponding to each time index and the transmitted signal corresponding to each antenna.

[0029] Doppler shift is encompassed within the overall channel response and involves spectral decomposition operations, including noise spectral decomposition and signal spectral decomposition. Residual echo signals are used in the MUSIC algorithm.

[0030] Meanwhile, the Doppler frequency shift caused by the moving object must be considered in the signal model. This requires projecting the user's velocity onto the 3D near-field geometry, and the relationship is expressed as follows:

[0031]

[0032] set up This indicates that BS and UAV are in the first... CPI time index The sensing signal sent from the location, among which Indicates the first The transmitted signals from each antenna. Therefore, the residual echo signal can be expressed as:

[0033]

[0034]

[0035]

[0036] in It represents the Hadamah accumulation. This represents additive white Gaussian noise.

[0037] In some optional implementations of any embodiment of this disclosure, the two-dimensional angle includes an azimuth angle and a pitch angle; the UAV and the base station perform two-dimensional angle estimation on the target to obtain two-dimensional angles respectively, and generate a first ray and a second ray, including: the UAV performs two-dimensional angle estimation on the direction of arrival of the target based on the Multi-Signal Classification (MUSIC) algorithm to obtain a two-dimensional angle, and generates a first ray based on the two-dimensional angle; the base station performs two-dimensional angle estimation on the direction of arrival of the target based on the MUSIC algorithm to obtain a two-dimensional angle, and generates a second ray based on the two-dimensional angle.

[0038] This embodiment proposes a UAV-BS cooperative perception framework, in which we extend the MUSIC algorithm to 3D near-field scenes and use a novel dual MUSIC method to achieve efficient localization through 2D parameter scanning.

[0039] In some optional implementations of any embodiment of this disclosure, the method further includes: the UAV and the base station are each configured with a uniform planar array, and both receive and transmit sensing signals in full-duplex mode at different carrier frequencies; a covariance matrix is ​​constructed using the received sensing signals, and eigenvalue decomposition is performed on the covariance matrix to obtain a noise subspace; the two-dimensional angle estimation of the arrival direction of the target object based on the multi-signal classification algorithm MUSIC includes: constructing a spectral function according to the noise subspace using the MUSIC algorithm; and estimating the two-dimensional angle of the arrival direction of the target object by determining the peak value of the spectral function.

[0040] In some optional implementations of any embodiment of this disclosure, generating candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray in the XOY plane includes: determining the intersection of the first ray and the second ray in the XOY plane; determining the height estimate of the target based on the intersection of the first ray and the second ray respectively; and forming two candidate three-dimensional position points of the target from the intersection and the obtained height estimate.

[0041] The MUSIC algorithm is a high-resolution method for parameter estimation that utilizes the orthogonality of the signal subspace. Specifically, the signal subspace can be derived from the covariance matrix of the received echo signal and can be represented as follows: Based on eigenvalue decomposition, the th The signal subspace of the covariance matrix in CPI and noise subspace The following can be exported:

[0042] in and Include The largest eigenvalues ​​and their corresponding eigenvectors, and and It consists of the remaining eigenvalues ​​and eigenvectors. The minimum value will occur when a correlation operation is performed between the steering vector and the eigenvectors spanning the noisy subspace.

[0043] To address the high complexity issue in 3D near-field scanning, the proposed dual-MUSIC algorithm can obtain 3D coordinates based on a two-dimensional scan vector. The simplified 3D scan vector can be formed as follows: and ,in and Indicates the first Distance obtained in each time slot. Function Defined as: .

[0044] like Figure 3 The diagram illustrates an application scenario of the UAV-assisted dual-MUSIC algorithm. The two-dimensional direction vector of the user relative to the UAV and BS can be obtained, and can be written as... and These vectors generate two rays representing pitch and azimuth information. Due to noise, the geometric relationship of these rays in 3D space is usually represented as two non-coplanar (skew) lines. To address this issue, we first extract the coordinates of the intersection points of these lines projected onto the XOY plane. , can be represented as:

[0045]

[0046] in and Represents rays and The intersection with the XOY plane. Next, calculate the intersection of the two rays. The corresponding Z-coordinate is represented as and It can be formed as:

[0047] Therefore, with the assistance of UAVs, it is possible to... The computational complexity of obtaining two three-dimensional coordinate points and .

[0048] In some optional implementations of any embodiment of this disclosure, the step of processing the two candidate three-dimensional position points and the velocity to generate the position and velocity of the target includes: inputting the two candidate three-dimensional position points and the velocity into a Kalman filter; and, based on the Kalman filter, using a target motion model, processing the two candidate three-dimensional position points and the velocity, as well as the three-dimensional position points predicted in the previous time slot, to predict the position and velocity of the target in the current time slot.

[0049] Optionally, the step of using the Kalman filter and a target motion model to process the two candidate three-dimensional position points and the velocity, as well as the three-dimensional position points predicted in the previous time slot, to predict the position and velocity of the target in the current time slot includes: concatenating the two candidate three-dimensional position points to obtain a six-dimensional vector; processing the six-dimensional vector using an observation model to obtain first trajectory calculation parameters; processing the trajectory prediction vector predicted in the previous time slot using a state transition model to obtain second trajectory calculation parameters; and using the Kalman filter to update the trajectory using the first trajectory calculation parameters and the second trajectory calculation parameters to obtain the position and velocity of the target in the current time slot.

[0050] In CPI During this period, a KF-based fusion method was implemented to integrate dual spatial coordinates. and With trajectory prediction vector This represents the position and velocity predicted from the previous time slot. The two obtained 3D vectors are concatenated to form a 6D vector to be fused, which can be mathematically represented as... Therefore, the state transition model and observation model can be derived as follows:

[0051]

[0052]

[0053]

[0054]

[0055] in This represents the prior estimate of the subsequent CPI. Indicates CPI The true value, and the corresponding covariance matrix. .also, and Representing the prediction error and observation error respectively, they can be expressed as: and Assuming that the observation and prediction errors follow a standard normal distribution, their covariance matrix is ​​expressed as follows: and The Kalman gain can be derived as:

[0056] Therefore, coordinate fusion and trajectory update are described as follows:

[0057]

[0058]

[0059] A complexity analysis is performed on the proposed algorithm. The computational complexity of two MUSIC symbols can be expressed as formula (24). The expression ) represents the complexity of calculating the covariance matrix. This represents the complexity of SVD decomposition. This represents the complexity of peak search / traversal for the spectral function. This represents the complexity of the fusion of geometric computation and Kalman calculus. and These represent the number of scans for elevation and azimuth angles, respectively. Considering the significant difference between array size and the number of scans, we assume the BS and UAV array sizes are... .

[0060] This embodiment proposes a KF-based fusion algorithm that significantly improves user trajectory tracking accuracy by integrating the predicted state vector with dual-observation coordinates. Conventional 3D near-field MUSIC is applicable but limited by finite distance accuracy; the framework proposed in this disclosure can quickly and accurately estimate the user's position and velocity. This embodiment can also employ a dual-MUSIC algorithm to achieve 3D coordinate estimation through two-dimensional parameter scanning. Finally, a KF data fusion method is found in the Cartesian coordinate system to update the predicted data, reducing computational and time complexity.

[0061] This disclosure provides a trajectory generation apparatus for executing the trajectory generation method described in the above embodiments, such as... Figure 4 As shown, an application is made to a near-field perception framework, which includes a drone and a base station. The device 400 includes: an estimation unit 401, configured for the drone and the base station to perform two-dimensional angle estimation on a target, obtain two-dimensional angles respectively, and generate a first ray and a second ray; a first generation unit 402, configured for the drone to generate candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray on the XOY plane, and the base station to generate candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray on the XOY plane, so as to obtain two candidate three-dimensional position points; and a second generation unit 403, configured for the base station to estimate the velocity of the target, process the two candidate three-dimensional position points and the velocity, and generate the position and velocity of the target.

[0062] The trajectory generation apparatus provided in the above embodiments of this disclosure and the trajectory generation method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0063] This disclosure also provides an electronic device corresponding to the trajectory generation method provided in the foregoing embodiments, for executing the trajectory generation method described above. This disclosure is not limiting.

[0064] Please refer to Figure 5 This illustrates a schematic diagram of an electronic device provided by some embodiments of the present disclosure. For example... Figure 5 As shown, the electronic device 50 includes: a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected via the bus 502. The memory 501 stores a computer program that can run on the processor 500. When the processor 500 runs the computer program, it executes the method provided in any of the foregoing embodiments of this disclosure.

[0065] The memory 501 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0066] Bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs. After receiving an execution instruction, the processor 500 executes the program. The trajectory generation method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 500, or implemented by the processor 500.

[0067] The processor 500 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 500 or by instructions in software form. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501. The processor 500 reads the information in memory 501 and, in conjunction with its hardware, completes the steps of the above method.

[0068] The electronic device provided in this disclosure and the trajectory generation method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0069] This disclosure also provides a computer-readable storage medium corresponding to the trajectory generation method provided in the foregoing embodiments. Please refer to... Figure 6 The computer-readable storage medium shown is an optical disc 60, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the trajectory generation method provided in any of the foregoing embodiments.

[0070] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0071] The computer-readable storage medium provided in the above embodiments of this disclosure and the trajectory generation method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0072] It should be noted that: In the foregoing text, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in this disclosure is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0074] The embodiments of this disclosure have been described above with reference to the accompanying drawings. These are merely specific implementations of this disclosure, but this disclosure is not limited to the specific implementations described above. The specific implementations described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this disclosure without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this disclosure.

Claims

1. A method for generating a trajectory, characterized in that, Applied to a near-field sensing framework, which includes a drone and a base station, the method includes: The drone and the base station perform two-dimensional angle estimation on the target, obtain two-dimensional angles respectively, and generate a first ray and a second ray; The UAV generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray in the XOY plane, and the base station generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray in the XOY plane, so as to obtain two candidate three-dimensional location points. The base station estimates the velocity of the target, processes the two candidate three-dimensional location points and the velocity, and generates the position and velocity of the target. The two-dimensional angle includes azimuth and pitch angle; The drone and the base station perform two-dimensional angle estimation on the target, obtain two-dimensional angles respectively, and generate a first ray and a second ray, including: The UAV estimates the direction of arrival of the target object in two dimensions based on the MUSIC multi-signal classification algorithm, and generates a first ray based on the two-dimensional angle. The base station estimates the direction of arrival of the target object in two dimensions based on the MUSIC algorithm, and generates a second ray based on the two-dimensional angle. The method further includes: The drone and the base station are each configured with a uniform planar array, and both receive and transmit sensing signals in full-duplex mode at different carrier frequencies; A covariance matrix is ​​constructed using the received sensing signals, and eigenvalue decomposition is performed on the covariance matrix to obtain the noise subspace; The two-dimensional angle estimation of the arrival direction of the target object based on the MUSIC multi-signal classification algorithm includes: Using the MUSIC algorithm, a spectral function is constructed based on the noise subspace; By determining the peak value of the spectral function, a two-dimensional angle estimation of the target's direction of arrival is performed; The method is applied to a near-field unmanned aerial vehicle (UAV) perception framework, which consists of a base station and the UAV. The base station is equipped with a uniform planar array of multiple antennas. The method further includes: The array response of the planar array is determined based on the distance difference between the target and each antenna; The velocity parameters of the target are projected onto three-dimensional near-field geometry to obtain Doppler frequency shift information; The residual return signal is determined based on the sensed signal transmitted for each time index and the transmitted signal for each antenna.

2. The method according to claim 1, characterized in that, The step of generating candidate three-dimensional position points of the target based on the intersection of the first ray and the second ray in the XOY plane includes: Determine the intersection point of the first ray and the second ray in the XOY plane; The height estimate of the target is determined by the intersection of the first ray and the second ray, respectively; The intersection point and the obtained height estimate form two candidate three-dimensional location points for the target.

3. The method according to claim 1, characterized in that, The process of processing the two candidate 3D position points and the velocity to generate the position and velocity of the target includes: The two candidate 3D position points and the velocity are input into a Kalman filter; Based on the Kalman filter, a target motion model is used to process the two candidate three-dimensional position points and the velocity, as well as the three-dimensional position points predicted in the previous time slot, in order to predict the position and velocity of the target in the current time slot.

4. The method according to claim 3, characterized in that, The process of using the Kalman filter and a target motion model to process the two candidate 3D position points and the velocity, as well as the 3D position points predicted in the previous time slot, to predict the position and velocity of the target in the current time slot includes: The two candidate three-dimensional position points are concatenated to obtain a six-dimensional vector; The six-dimensional vector is processed using an observation model to obtain the first trajectory calculation parameters; The trajectory prediction vector predicted in the previous time slot is processed using a state transition model to obtain the second trajectory calculation parameters; The Kalman filter is used to update the trajectory of the first trajectory calculation parameters and the second trajectory calculation parameters to obtain the position and velocity of the target in the current time slot.

5. A trajectory generation device, characterized in that, Applied to a near-field sensing framework, the near-field sensing framework including a drone and a base station, the device includes: The estimation unit is configured to perform two-dimensional angle estimation on the target by the UAV and the base station, respectively obtain two-dimensional angles, and generate a first ray and a second ray; The first generation unit is configured such that the UAV generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray in the XOY plane, and the base station generates candidate three-dimensional location points of the target based on the intersection of the first ray and the second ray in the XOY plane, so as to obtain two candidate three-dimensional location points. The second generation unit is configured to have the base station estimate the velocity of the target, process the two candidate three-dimensional location points and the velocity, and generate the position and velocity of the target. The two-dimensional angle includes azimuth and pitch angle; The drone and the base station perform two-dimensional angle estimation on the target, obtain two-dimensional angles respectively, and generate a first ray and a second ray, including: The UAV estimates the direction of arrival of the target object in two dimensions based on the MUSIC multi-signal classification algorithm, and generates a first ray based on the two-dimensional angle. The base station estimates the direction of arrival of the target object in two dimensions based on the MUSIC algorithm, and generates a second ray based on the two-dimensional angle. The device is also configured to: The drone and the base station are each configured with a uniform planar array, and both receive and transmit sensing signals in full-duplex mode at different carrier frequencies; A covariance matrix is ​​constructed using the received sensing signals, and eigenvalue decomposition is performed on the covariance matrix to obtain the noise subspace; The two-dimensional angle estimation of the arrival direction of the target object based on the MUSIC multi-signal classification algorithm includes: Using the MUSIC algorithm, a spectral function is constructed based on the noise subspace; By determining the peak value of the spectral function, a two-dimensional angle estimation of the target's direction of arrival is performed; The device is applied to a near-field unmanned aerial vehicle (UAV) sensing framework, which consists of the base station and the UAV. The base station is equipped with a uniform planar array of multiple antennas. The device is also configured to: The array response of the planar array is determined based on the distance difference between the target and each antenna; The velocity parameters of the target are projected onto three-dimensional near-field geometry to obtain Doppler frequency shift information; The residual return signal is determined based on the sensed signal transmitted for each time index and the transmitted signal for each antenna.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-4.

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