Unmanned aerial vehicle positioning method and device, electronic equipment and storage medium

Abstract

The invention provides an unmanned aerial vehicle positioning method and device, electronic equipment and a storage medium, and relates to the technical field of wireless communication. The method comprises the steps that a plurality of target reflection signals of each intelligent reflection surface in a plurality of intelligent reflection surfaces are determined, and the target reflection signals are obtained by reflecting signals sent by an unmanned aerial vehicle through the intelligent reflection surfaces; determining an estimated direction angle of the unmanned aerial vehicle relative to the intelligent reflection surface according to the plurality of target reflection signals of the intelligent reflection surface; and determining target position coordinates of the unmanned aerial vehicle according to the estimated direction angle of the unmanned aerial vehicle relative to each intelligent reflecting surface. Therefore, the target position coordinates of the unmanned aerial vehicle are accurately determined through the multiple target emission signals of each intelligent reflection surface in the multiple intelligent reflection surfaces, and accurate positioning of the unmanned aerial vehicle is realized.

Description

Technical Field

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

[0002] The widespread application of low-altitude drones in logistics, emergency rescue, and agricultural plant protection has created an urgent need for precise positioning technology. However, traditional Global Positioning System (GPS) positioning technology faces serious challenges in low-altitude environments. In complex environments such as cities and transitional zones between indoor and outdoor areas, GPS signals are frequently obstructed by buildings and subjected to multipath interference, resulting in a significant decrease in positioning accuracy and making it difficult to meet the requirements for safe flight and precise operation of low-altitude drones. Therefore, how to accurately position drones in urban low-altitude scenarios is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for locating unmanned aerial vehicles (UAVs).

[0004] In a first aspect, this disclosure provides a method for locating a drone, the method comprising: determining multiple target reflection signals of each of a plurality of intelligent reflective surfaces, wherein the target reflection signals are obtained by the intelligent reflective surfaces reflecting signals sent by the drone; determining an estimated orientation angle of the drone relative to the intelligent reflective surfaces based on the multiple target reflection signals of the intelligent reflective surfaces; and determining the target position coordinates of the drone based on the estimated orientation angles of the drone relative to each of the intelligent reflective surfaces.

[0005] Secondly, this disclosure provides a positioning device for a drone, the device comprising: a first determining module, configured to determine target reflection signals corresponding to each of a plurality of intelligent reflective surfaces, wherein the target reflection signals are obtained by the intelligent reflective surfaces reflecting signals sent by the drone; a second determining module, configured to determine an estimated orientation angle of the drone relative to the intelligent reflective surfaces based on any one of the plurality of target reflection signals; and a third determining module, configured to determine the target position coordinates of the drone based on the estimated orientation angles of the drone relative to each of the intelligent reflective surfaces.

[0006] Thirdly, this disclosure provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the drone positioning method disclosed in the embodiments of this disclosure.

[0007] Fourthly, this disclosure provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the drone positioning method disclosed in the embodiments of this disclosure.

[0008] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the drone positioning method disclosed in the embodiments of this disclosure.

[0009] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: Multiple target reflection signals from various intelligent reflective surfaces are identified, where the target reflection signals are obtained by reflecting signals transmitted by the UAV. Based on these multiple target reflection signals, the estimated azimuth angle of the UAV relative to each intelligent reflective surface is determined. Finally, based on the estimated azimuth angle of the UAV relative to each intelligent reflective surface, the target position coordinates of the UAV are determined. Therefore, by using multiple target transmission signals from various intelligent reflective surfaces, the target position coordinates of the UAV are accurately determined, achieving accurate positioning of the UAV. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0011] Figure 1 This is a flowchart illustrating a positioning method for a drone according to an exemplary embodiment; Figure 2 This is a detailed flowchart illustrating step 102 according to an exemplary embodiment; Figure 3 This is a detailed flowchart illustrating step 103 according to an exemplary embodiment; Figure 4 This is a flowchart illustrating another method for locating a drone according to an exemplary embodiment; Figure 5 This is a schematic diagram illustrating the structure of a positioning device for a drone according to an exemplary embodiment; Figure 6 This is a schematic diagram illustrating the structure of a positioning system for a drone according to an exemplary embodiment; Figure 7 This is a structural block diagram of an electronic device according to an exemplary embodiment.

[0012] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0013] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0014] The technical solutions of this disclosure and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this disclosure will now be described with reference to the accompanying drawings.

[0015] First, combine Figure 1 The following is an exemplary description of the drone positioning method provided in the embodiments of this disclosure.

[0016] Figure 1 This is a flowchart illustrating a positioning method for a drone according to an exemplary embodiment.

[0017] It should be noted that the drone positioning method provided in this embodiment can be executed by a drone positioning device, which can be implemented by software and / or hardware. The drone positioning device can be a central processing unit (CPU), or can be configured within a CPU. The CPU can be configured in an electronic device.

[0018] It should be noted that the electronic device can be any device with computing capabilities, such as a terminal device or a server. This embodiment does not specifically limit the electronic device.

[0019] It should be noted that this embodiment uses the example of the electronic device being configured in a ground base station for illustrative purposes.

[0020] like Figure 1 As shown, the drone's positioning method includes the following steps: Step 101: Determine multiple target reflection signals of each of the multiple smart reflective surfaces, wherein the target reflection signals are obtained by the smart reflective surfaces reflecting the signals sent by the UAV.

[0021] In some embodiments, to accurately obtain the target reflection signal of each intelligent reflective surface, correspondingly, for each of the multiple reconfigurable intelligent surfaces (RIS), a first reflection signal obtained by the intelligent reflective surface under a first phase configuration can be acquired, wherein the first reflection signal is obtained by the intelligent reflective surface reflecting a signal transmitted by the UAV under the first phase configuration; a second reflection signal obtained by the intelligent reflective surface under a second phase configuration can be acquired, wherein the second reflection signal is obtained by the intelligent reflective surface reflecting a signal transmitted by the UAV under the second phase configuration, wherein the phase difference between the first phase configuration and the second phase configuration is 180 degrees; the target reflection signal is obtained by subtracting the first reflection signal and the second reflection signal. Thus, by subtracting the first reflection signal and the second reflection signal of the intelligent reflective surface, an enhanced target transmission signal of the intelligent transmitting surface can be obtained, effectively suppressing interference from direct path signals, signals from other intelligent reflective surfaces, and environmental scattering signals.

[0022] It is understood that in this embodiment, the first and second reflected signals have the same amplitude, but their phases differ by π (i.e., 180 degrees), while the signal components of other propagation paths remain unchanged. Therefore, by subtracting the first and second reflected signals, the enhanced target emission signal of the intelligent emitting surface can be obtained, effectively suppressing interference from direct path signals, signals from other intelligent reflective surfaces, and environmental scattering signals.

[0023] It should be noted that the intelligent reflective surface in this embodiment is composed of M×N uniformly distributed reflective units, where N and M are both integers greater than 1.

[0024] In some embodiments, when there are n smart reflective surfaces (n is an integer greater than 1), the ground base station can number the n smart reflective surfaces. The numbers corresponding to these n smart reflective surfaces can be: RIS1, RIS2, ..., RIS n Therefore, a complete signal separation cycle consists of n consecutive processing time slots. Within the i-th time slot, the ground base station specifies the RIS via control signaling. iFor the target smart reflective surface, a two-state phase modulation operation is performed, while all other smart reflective surfaces maintain their current phase configuration. The control signaling can include key information such as the target RIS identifier, phase configuration instructions, time slot duration, and synchronization timestamp, and is broadcast simultaneously to all smart reflective surface devices. The core concept of the two-state phase modulation technology is to transform the programmable characteristics of the smart reflective surface into a selective signal enhancement tool, achieving precise separation of different RIS signals through two-state phase modulation. Specifically: In the first phase, the ground base station sends an initial configuration command to the target smart reflective surface, setting each reflective element in the target smart reflective surface to a preset phase state sequence. During this configuration activation period, the base station's receiving link captures the first set of mixed signals containing contributions from all propagation paths. In the second phase, the ground base station switches the smart reflective surface to a complementary phase state, and the ground base station acquires the second set of mixed signals. Based on the symmetry principle of phase reversal, the reflected signals generated by the target intelligent reflective surface in both configurations have the same amplitude but differ in phase by π, while the signal components of other propagation paths remain unchanged. By subtracting the first set of mixed signals from the second set of mixed signals, the target reflected signal of the target intelligent reflective surface can be extracted, effectively suppressing interference from direct path signals, signals from other intelligent reflective surfaces, and environmental scattering signals.

[0025] It is understandable that the phase state locking strategy ensures the configuration stability of non-target smart reflective surfaces. When a smart reflective surface is not the target smart reflective surface in the current time slot, its phase control circuit enters a locked mode, prohibiting configuration change operations. Through the aforementioned cyclic time slot dual-state control mechanism, the ground base station can orderly separate the target reflection signals of multiple smart reflective surfaces in complex urban electromagnetic environments, providing a reliable signal foundation for subsequent precise angle analysis and three-dimensional positioning fusion.

[0026] Step 102: Determine the estimated orientation angle of the UAV relative to the smart reflective surface based on multiple target reflection signals from the smart reflective surface.

[0027] It can be understood that the estimated direction angle of the UAV relative to the smart reflective surface refers to the estimated direction angle pointing from the smart reflective surface as the origin to the UAV.

[0028] In some embodiments, the estimated orientation angle of the UAV relative to the smart reflective surface can be obtained by analyzing multiple target reflection signals of the smart reflective surface.

[0029] In this embodiment, the estimated direction angle may include: the estimated azimuth angle and the estimated pitch angle.

[0030] Step 103: Determine the target position coordinates of the UAV based on the estimated orientation angles of the UAV relative to each smart reflective surface.

[0031] In some embodiments, after obtaining the estimated orientation angles of the UAV relative to each smart reflective surface, the multiple estimated orientation angles are subjected to collaborative positioning fusion processing to obtain the target position coordinates of the UAV.

[0032] The target position coordinates refer to the three-dimensional position coordinates of the UAV in the world coordinate system.

[0033] The drone positioning method provided in this disclosure determines multiple target reflection signals from each of a plurality of intelligent reflective surfaces, wherein the target reflection signals are obtained by reflecting signals transmitted by the drone onto the intelligent reflective surfaces; an estimated orientation angle of the drone relative to the intelligent reflective surfaces is determined based on the multiple target reflection signals of the intelligent reflective surfaces; and the target position coordinates of the drone are determined based on the estimated orientation angles of the drone relative to each intelligent reflective surface. Thus, by using multiple target emission signals from each of the multiple intelligent reflective surfaces, the target position coordinates of the drone are accurately determined, achieving accurate positioning of the drone.

[0034] In some embodiments, to clearly understand the process of determining the estimated orientation angle of the UAV relative to the smart reflective surface based on multiple target reflection signals from the smart reflective surface, the following is combined with... Figure 2 An exemplary implementation of determining the estimated orientation angle of a UAV relative to a smart reflective surface based on multiple target reflection signals is described.

[0035] Figure 2 This is a detailed flowchart illustrating step 102 according to an exemplary embodiment.

[0036] like Figure 2 As shown, the method may include: Step 201: Determine the theoretical steering vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface when it is a candidate direction angle.

[0037] In some embodiments, one possible implementation for determining the theoretical steering vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface when it is a candidate direction angle is as follows: determine the spatial response vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface when it is a candidate direction angle; form a phase configuration matrix based on the phase configuration vector used by the intelligent reflective surface when receiving signals transmitted from various targets; multiply the phase configuration matrix by the spatial response vector to obtain the theoretical steering vector. Thus, the theoretical steering vector is accurately determined.

[0038] Step 202: Form an observation vector based on the signals emitted by multiple targets on the smart reflective surface.

[0039] Step 203: Perform eigenvalue decomposition on the covariance matrix of the observation vector to obtain the eigenvalue decomposition results.

[0040] In some embodiments, the covariance matrix of the observation vector is determined, and eigenvalue analysis is performed on the covariance matrix of the observation vector to obtain the eigenvalue decomposition result.

[0041] Step 204: Based on the eigenvectors of each eigenvalue other than the largest eigenvalue in the eigenvalue analysis results, form a noise subspace.

[0042] Step 205: Obtain the candidate orientation angle that minimizes the projection energy of the theoretical steering vector onto the noise subspace as the estimated orientation angle.

[0043] It is understandable that the projection energy of the theoretical steering vector onto the noise subspace is the minimum, indicating that the theoretical steering vector is approximately orthogonal to the noise subspace.

[0044] It is understandable that in urban low-altitude positioning scenarios, the distance between the UAV and the smart reflective surface is usually much larger than the geometric dimensions of the smart reflective surface itself, satisfying the far-field propagation assumption. Based on the far-field approximation, the incident signal from the UAV can be modeled as a plane wave propagation mode. Let the smart reflective surface be deployed at the origin of a two-dimensional plane coordinate system (e.g., the center point of the smart reflective surface can be used as the origin of the two-dimensional plane coordinate system), the azimuth angle of the UAV relative to the smart reflective surface be θ, and the pitch angle be φ. The smart reflective surface consists of M×N uniformly distributed reflective units, and the position coordinates of the (m,n)th reflective unit are (…). , ), where m∈[1,M], n∈[1,N]. Under the far-field plane wave assumption, the phase delay of the UAV signal when it reaches the (m,n)th reflecting unit can be expressed as: , where f is the carrier frequency and c is the electromagnetic wave propagation speed.

[0045] The phase control architecture of the intelligent reflective surface achieves spatial mapping of UAV angle information through spatial coding. Ground base stations can design specific spatially coded phase distributions for each reflective configuration. Where k represents the configuration sequence number. After path correction phase processing, the equivalent reflection signal from the target smart reflective surface can be simplified to: , in , ( , This expression represents the geometric angle at which the smart reflective surface points towards the base station; it is directly related to the drone's azimuth information. , ).

[0046] To accurately obtain the estimated orientation angle of the UAV relative to the smart reflective surface, a multi-configuration signal collaborative processing technique can be employed. Assuming k different smart reflective surface configurations are collected, the corresponding received signals can be organized into an observation vector. Combined with the spatial coding phase matrix of each configuration ,in The phase configuration vector of the k-th configuration can be used to establish the observation equation. , ,in , Represents the spatial response vector corresponding to the direction of the UAV. Represents the disturbance vector. Spatial response vector. , The (m, n)th element is This vector is entirely determined by the angular position of the UAV. , The decision is the core parameter of the angle analysis algorithm.

[0047] The covariance matrix of the observed signal can be calculated from the observed vector. , obtained through eigenvalue decomposition ,in and Let be the eigenvalues ​​and their corresponding eigenvectors, respectively. Then the noise subspace is composed of the eigenvectors... Composition. Based on the orthogonal separation characteristics of the signal subspace and noise subspace, the azimuth position of the UAV can be determined by constructing a two-dimensional angle recognition function. For any candidate azimuth angle in the angle space... This involves calculating the theoretical configuration response vector (i.e., the theoretical steering vector) corresponding to the candidate orientation angle. The two-dimensional angle recognition function can be expressed as:

[0048] When the candidate azimuth angle is close to the actual azimuth angle of the UAV, the theoretical configuration response vector is approximately orthogonal to the noise subspace, causing the denominator to approach zero, resulting in a significant peak in the angle recognition function. Therefore, the recognition function value of each candidate azimuth angle can be calculated within a preset two-dimensional angle search space according to a set angle resolution. The azimuth search range is usually set to [-90°, 90°], and the pitch search range is determined according to the system geometry, generally [0°, 90°].

[0049] The angular resolution is preset based on actual needs. As an example, the choice of angular resolution needs to balance computational complexity and angular resolution accuracy, and is usually set to 1 / 4 to 1 / 2 of the desired angular accuracy.

[0050] In some embodiments, the candidate direction that maximizes the two-dimensional angle recognition function can be used as the estimated orientation angle of the UAV relative to the smart reflective surface. ).

[0051] In this embodiment, the angular resolution limitation of traditional array processing can be overcome, and high-precision UAV azimuth angle estimation can be achieved with a limited number of configurations.

[0052] In some embodiments, to clearly understand the specific process of determining the target position coordinates of the UAV based on the estimated orientation angles of the UAV relative to each intelligent reflective surface, the following is combined with... Figure 3 An exemplary description is provided of one possible implementation for determining the target position coordinates of a UAV based on the estimated orientation angles of the UAV relative to each intelligent reflective surface.

[0053] Figure 3 This is a detailed flowchart illustrating step 103 according to an exemplary embodiment.

[0054] like Figure 3 As shown, it may include: Step 301: Determine the theoretical orientation angle of the UAV relative to each smart reflective surface, wherein the theoretical orientation angle is calculated based on the candidate position coordinates of the UAV.

[0055] In some embodiments, the geometric constraint relationship between the candidate position coordinates (x, y, z) of the UAV and the theoretical orientation angle of the UAV relative to the i-th smart reflective surface is as follows: i=1,2,……,n in Represents geometric transformation functions, 3D coordinates Mapped to the drone relative to the i-th smart reflective surface Theoretical azimuth angle; 3D coordinates Mapped to the drone relative to the i-th smart reflective surface The theoretical pitch angle.

[0056] Step 302: Determine the sum of squared errors between the estimated and theoretical orientation angles of the UAV relative to each smart reflective surface.

[0057] It should be noted that the method for determining the sum of squared errors between the estimated and theoretical orientation angles of the UAV relative to each intelligent reflective surface differs in different application scenarios. An example is provided below: As an example, for any smart reflective surface, the squared error between the estimated and theoretical orientation angles of the UAV relative to the smart reflective surface is determined; the error bisectors corresponding to each smart reflective surface are summed to obtain the sum of squared errors.

[0058] As another example, for each intelligent reflective surface, a first weight is determined. This first weight is obtained by comparing the largest eigenvalue in the covariance matrix with the sum of all other eigenvalues ​​in the covariance matrix. The covariance matrix is ​​determined based on multiple target reflection signals from the intelligent reflective surface. The squared error between the estimated and theoretical heading angles of the UAV relative to the intelligent reflective surface is determined. The squared errors are then weighted according to the first weight to obtain the weighted squared errors corresponding to each intelligent reflective surface. Finally, the weighted squared errors corresponding to each intelligent reflective surface are summed to obtain the sum of squared errors. Therefore, by combining the first weights of each intelligent reflective surface with the weighted sum of the squared errors, the accurate sum of squared errors is obtained.

[0059] Step 303: The candidate position coordinates that minimize the sum of squared errors are taken as the target position coordinates of the UAV.

[0060] In some embodiments, based on the least squares criterion, the three-dimensional localization fusion problem of multiple smart reflective surfaces can be constructed as the following optimization problem:

[0061] i=1,2,……,n Where n represents the total number of intelligent reflective surfaces, and in the formula... This represents the estimated azimuth angle of the UAV relative to the i-th smart reflective surface; in the formula... This represents the estimated pitch angle of the UAV relative to the i-th smart reflective surface.

[0062] The goal of this optimization problem is to find the optimal three-dimensional coordinates (x, y, z) that minimize the sum of squared errors between the theoretical angle values ​​calculated from these coordinates and the estimated angle values ​​of each intelligent reflective surface. It can be understood that the optimal three-dimensional coordinates (x, y, z) found are the target position coordinates of the UAV.

[0063] It is understandable that the angle estimation accuracy of different intelligent reflective surfaces varies due to the influence of environmental noise and multipath effects. Therefore, to further improve the accuracy of the obtained UAV target position coordinates, in some embodiments, a weighting mechanism can be introduced to enhance the accuracy of the UAV target position coordinates. This is particularly relevant considering that in the eigenvalue analysis of the covariance matrix, the largest eigenvalue... The primary energy of the target reflected signal is represented by the eigenvalues, while the remaining eigenvalues ​​reflect the energy of environmental noise and multipath interference. Based on this, the first weight of the i-th intelligent reflective surface (which can also be called the noise interference weight) can be determined according to the primary energy of the target reflected signal and the energy of the noise subspace. The first weight of the i-th intelligent reflective surface can be expressed as:

[0064] in, It can represent the j-th feature value under the i-th smart reflective surface, where j is an integer greater than or equal to 2 and less than k.

[0065] It is understandable that when the noise of a certain smart reflective surface is high, the energy of the received signal will be more distributed in the noise subspace, leading to a decrease in the main eigenvalue. The weight at this location is relatively small. During the optimization process, the weight at this location will also decrease accordingly. Therefore, the final joint localization optimization function for the multiple intelligent reflectors can be expressed as:

[0066] i=1,2,……,n This weighted optimization problem, by adaptively adjusting the contribution weights of the measurement results from each intelligent reflective surface, can effectively suppress the impact of environmental noise and the imperfect characteristics of the intelligent reflective surfaces on the final UAV positioning result. The joint positioning optimization function of the multiple intelligent reflective surfaces can be solved using the Levenberg-Marquardt algorithm to determine the target position coordinates of the UAV.

[0067] It is understandable that this algorithm combines the advantages of gradient descent and Gauss-Newton methods, enabling fast numerical solutions while ensuring convergence stability, thereby helping to improve the efficiency of obtaining the target position coordinates of the UAV.

[0068] To facilitate a clear understanding of this disclosure, the following will be combined with... Figure 4 The method of this embodiment is described by way of example.

[0069] Figure 4 This is a flowchart illustrating another method for locating a drone according to an exemplary embodiment.

[0070] like Figure 4 As shown, it may include: Step 401: For each of the multiple smart reflective surfaces, perform dual-state phase modulation on the smart reflective surface to obtain two reflection signals of the smart reflective surface, and subtract the two reflection signals to obtain the target reflection signal of the smart reflective surface.

[0071] Step 402: Perform efficient and precise angle analysis on multiple target reflection signals of the intelligent reflective surface to obtain the estimated orientation angle of the UAV relative to the intelligent reflective surface.

[0072] Step 403: Perform collaborative positioning fusion on the estimated orientation angles of the UAV relative to each smart reflective surface to obtain the target position coordinates of the UAV.

[0073] It should be noted that for a detailed description of steps 401 to 403, please refer to the relevant descriptions in other embodiments, which will not be repeated here.

[0074] In this embodiment, high-precision positioning of the UAV is achieved through the collaborative operation of multiple intelligent reflective surfaces.

[0075] Figure 5 This is a schematic diagram illustrating the structure of a positioning device for a drone according to an exemplary embodiment.

[0076] like Figure 5 As shown, the positioning device 500 for the UAV includes: a first determining module 501, a second determining module 502, and a third determining module 503, wherein: The first determining module 501 is used to determine the target reflection signal corresponding to each of the multiple smart reflective surfaces, wherein the target reflection signal is obtained by the smart reflective surface reflecting the signal sent by the UAV.

[0077] The second determining module 502 is used to determine the estimated orientation angle of the UAV relative to the smart reflective surface based on any one of the multiple target reflection signals.

[0078] The third determining module 503 is used to determine the target position coordinates of the UAV based on the estimated orientation angle of the UAV relative to each intelligent reflective surface.

[0079] In one embodiment of this disclosure, the third determining module 503 is specifically used for: determining the theoretical orientation angle of the UAV relative to each intelligent reflective surface, wherein the theoretical orientation angle is calculated based on the candidate position coordinates of the UAV; determining the sum of squared errors between the estimated orientation angle and the theoretical orientation angle of the UAV relative to each intelligent reflective surface; and using the candidate position coordinates that minimize the sum of squared errors as the target position coordinates of the UAV.

[0080] In one embodiment of this disclosure, the third determining module 503 determines the sum of squared errors between the estimated and theoretical orientation angles of the UAV relative to each intelligent reflective surface in the following manner: For each intelligent reflective surface, a first weight is determined, wherein the first weight is obtained by comparing the largest eigenvalue in the covariance matrix with the sum of all other eigenvalues ​​in the covariance matrix, wherein the covariance matrix is ​​determined based on multiple target reflection signals of the intelligent reflective surface; the squared errors between the estimated and theoretical orientation angles of the UAV relative to the intelligent reflective surface are determined; the squared errors are weighted according to the first weight to obtain the weighted squared errors corresponding to the intelligent reflective surface; the weighted squared errors corresponding to each intelligent reflective surface are summed to obtain the sum of squared errors.

[0081] In one embodiment of this disclosure, the first determining module 501 is specifically configured to: for each of the plurality of smart reflective surfaces, acquire a first reflection signal obtained by the smart reflective surface under a first phase configuration, wherein the first reflection signal is obtained by the smart reflective surface reflecting a signal sent by the UAV under the first phase configuration; acquire a second reflection signal obtained by the smart reflective surface under a second phase configuration, wherein the second reflection signal is obtained by the smart reflective surface reflecting a signal sent by the UAV under the second phase configuration, wherein the phase difference between the first phase configuration and the second phase configuration is 180 degrees; and subtract the first reflection signal and the second reflection signal to obtain a target reflection signal.

[0082] In one embodiment of this disclosure, the second determining module 502 is specifically used for: determining the theoretical steering vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface as a candidate direction angle; forming an observation vector based on multiple target emission signals from the intelligent reflective surface; performing eigenvalue decomposition on the covariance matrix of the observation vector to obtain the eigenvalue decomposition result; forming a noise subspace based on the eigenvectors of each eigenvalue other than the largest eigenvalue in the eigenvalue analysis result; and obtaining the candidate direction angle that minimizes the projection energy of the theoretical steering vector on the noise subspace as the estimated direction angle.

[0083] In one embodiment of this disclosure, the second determining module 502 determines the theoretical steering vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface as a candidate direction angle in the following specific way: determining the spatial response vector corresponding to the direction angle of the UAV relative to the intelligent reflective surface as a candidate direction angle; forming a phase configuration matrix based on the phase configuration vector used by the intelligent reflective surface when obtaining the transmission signals of each target; and multiplying the phase configuration matrix with the spatial response vector to obtain the theoretical steering vector.

[0084] It should be noted that the foregoing description of the drone positioning method embodiment also applies to the drone positioning device of this embodiment, and will not be repeated here.

[0085] The drone positioning device provided in this disclosure determines multiple target reflection signals from each of a plurality of intelligent reflective surfaces, wherein the target reflection signals are obtained by reflecting signals transmitted by the drone from the intelligent reflective surfaces; an estimated orientation angle of the drone relative to the intelligent reflective surfaces is determined based on the multiple target reflection signals of the intelligent reflective surfaces; and the target position coordinates of the drone are determined based on the estimated orientation angles of the drone relative to each intelligent reflective surface. Thus, by using multiple target emission signals from each of the multiple intelligent reflective surfaces, the target position coordinates of the drone are accurately determined, achieving accurate positioning of the drone.

[0086] This disclosure provides a positioning system for an unmanned aerial vehicle (UAV).

[0087] Figure 6 This is a schematic diagram illustrating the structure of a positioning system for a drone according to an exemplary embodiment.

[0088] like Figure 6 As shown, the positioning system of the UAV may include: a UAV 601, multiple smart launch surfaces 602, a ground base station 603, and a central processing unit 604, wherein, In some embodiments, the central processing unit 604 described above may be configured in a ground base station, wherein the central processing unit 604 may be used to execute the drone positioning method of the embodiments of this disclosure.

[0089] In some embodiments, the drone 601 may be configured with a standard wireless communication transmission module, which can transmit positioning signals to the surrounding environment according to a preset power and frequency. Furthermore, the drone 601 may be designed with an omnidirectional antenna to ensure that the signal can cover the intelligent reflective surface 602 in all directions.

[0090] In some embodiments, the aforementioned plurality of smart reflective surfaces 602 can be installed on the exterior walls, rooftop edges, or other locations of urban buildings in urban low-altitude positioning scenarios.

[0091] In some embodiments, the smart reflective surface 602 may include a reflective element array, a phase control circuit, and a wireless communication module.

[0092] The reflective unit array consists of a large number of programmable reflective units, each of which can independently adjust the phase of the reflected signal.

[0093] The phase control circuit is used to adjust the working state of each reflection unit in real time according to the instructions of the central processing unit 604.

[0094] The wireless communication module is used for data transmission with the central processing unit 604.

[0095] In some embodiments, the ground base station 603 is used to receive reflected signals from the smart reflective surface.

[0096] The ground base station 603 may include: a receiving antenna, a radio frequency front-end processing circuit, an analog-to-digital converter, and a digital signal processing unit.

[0097] The central processing unit 604 is the command center of the UAV's positioning system, used to execute the UAV positioning method of this embodiment. That is, the central processing unit 604 is used to coordinate the working states of each intelligent reflective surface, execute core algorithms such as signal separation, angle analysis and position fusion, and finally output the target position coordinates of the UAV.

[0098] The drone positioning system of this disclosure identifies multiple target reflection signals from various intelligent reflective surfaces, wherein the target reflection signals are obtained by reflecting signals transmitted by the drone onto the intelligent reflective surfaces. Based on the multiple target reflection signals from the intelligent reflective surfaces, an estimated orientation angle of the drone relative to each intelligent reflective surface is determined. Based on the estimated orientation angle of the drone relative to each intelligent reflective surface, the target position coordinates of the drone are determined. Thus, by using multiple target emission signals from various intelligent reflective surfaces, the target position coordinates of the drone are accurately determined, achieving accurate drone positioning.

[0099] It is understood that the intelligent reflective surface in this embodiment adopts a passive design, resulting in relatively low manufacturing and maintenance costs. Simultaneously, it can be deeply integrated with existing cellular communication networks, effectively utilizing existing infrastructure resources, avoiding redundant construction investments, and reducing the complexity and cost of positioning system deployment. Furthermore, the intelligent reflective surface can be flexibly deployed on urban infrastructure such as building facades and communication towers, achieving an efficient integration of positioning functionality with urban construction, and providing new business models and service capabilities for telecommunications operators and urban management departments.

[0100] It should be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution all comply with the relevant provisions of national laws and regulations.

[0101] According to embodiments of this disclosure, an electronic device is also provided, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the drone positioning method disclosed in embodiments of this disclosure.

[0102] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the drone positioning method disclosed in this disclosure.

[0103] To implement the above embodiments, this disclosure also provides a computer program product.

[0104] The computer program product includes a computer program that, when executed by a processor, implements the drone positioning method disclosed in this embodiment.

[0105] Figure 7 This is a structural block diagram of an electronic device according to an exemplary embodiment. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0106] like Figure 7 As shown, the electronic device 1000 includes a processor 111, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 112 or a program loaded from memory 116 into random access memory (RAM) 113. The RAM 113 also stores various programs and data required for the operation of the electronic device 1000. The processor 111, ROM 112, and RAM 113 are interconnected via a bus 114. An input / output (I / O) interface 115 is also connected to the bus 114.

[0107] The following components are connected to I / O interface 115: memory 116 including hard disks, etc.; and communication section 117 including network interface cards such as local area network (LAN) cards, modems, etc., communication section 117 performs communication processing via a network such as the Internet; and driver 118 is also connected to I / O interface 115 as needed.

[0108] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 117. When the computer program is executed by processor 111, it performs the functions defined in the methods of this disclosure.

[0109] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, which can be executed by the processor 111 of the electronic device 1000 to perform the above-described method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0110] In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A positioning method for an unmanned aerial vehicle (UAV), characterized in that, The method includes: Multiple target reflection signals are determined for each of the multiple smart reflective surfaces, wherein the target reflection signals are obtained by the smart reflective surfaces reflecting signals sent by the UAV; The estimated orientation angle of the UAV relative to the smart reflective surface is determined based on multiple target reflection signals from the smart reflective surface; The target position coordinates of the UAV are determined based on the estimated orientation angle of the UAV relative to each of the smart reflective surfaces.

2. The method as described in claim 1, characterized in that, Determining the target position coordinates of the UAV based on the estimated orientation angles of the UAV relative to each of the smart reflective surfaces includes: Determine the theoretical orientation angle of the UAV relative to each of the smart reflective surfaces, wherein the theoretical orientation angle is calculated based on the candidate position coordinates of the UAV; Determine the sum of squared errors between the estimated and theoretical orientation angles of the UAV relative to each of the intelligent reflective surfaces; The candidate position coordinates that minimize the sum of squared errors will be used as the target position coordinates of the UAV.

3. The method as described in claim 2, characterized in that, Determining the sum of squared errors between the estimated and theoretical orientation angles of the UAV relative to each of the smart reflective surfaces includes: For each of the intelligent reflective surfaces, a first weight is determined for the intelligent reflective surface. The first weight is obtained by the ratio of the largest eigenvalue in the covariance matrix to the sum of the other eigenvalues ​​in the covariance matrix. The covariance matrix is ​​determined based on multiple target reflection signals of the intelligent reflective surface. Determine the squared error between the estimated and theoretical orientation angles of the UAV relative to the smart reflective surface; The squared error is weighted according to the first weight to obtain the weighted squared error corresponding to the smart reflective surface; The weighted squared errors corresponding to each of the intelligent reflective planes are summed to obtain the sum of squared errors.

4. The method as described in claim 1, characterized in that, The determination of multiple target reflection signals of each of the multiple smart reflective surfaces includes: For each of the plurality of smart reflective surfaces, a first reflection signal obtained by the smart reflective surface under a first phase configuration is acquired, wherein the first reflection signal is obtained by the smart reflective surface reflecting the signal sent by the UAV under the first phase configuration; The second reflection signal obtained by the smart reflective surface under the second phase configuration is obtained, wherein the second reflection signal is obtained by the smart reflective surface reflecting the signal sent by the UAV under the second phase configuration, and wherein the phase difference between the first phase configuration and the second phase configuration is 180 degrees; The target reflection signal is obtained by subtracting the first reflection signal and the second reflection signal.

5. The method according to any one of claims 1-4, characterized in that, Determining the estimated orientation angle of the UAV relative to the smart reflective surface based on multiple target reflection signals from the smart reflective surface includes: Determine the theoretical steering vector corresponding to the direction angle of the UAV relative to the smart reflective surface when it is a candidate direction angle; An observation vector is formed based on the signals emitted by multiple targets on the intelligent reflective surface; The covariance matrix of the observed vector is decomposed into eigenvalues ​​to obtain the eigenvalue decomposition results. Based on the eigenvectors of each eigenvalue other than the largest eigenvalue in the eigenvalue analysis results, a noise subspace is formed; The candidate orientation angle that minimizes the projection energy of the theoretical steering vector onto the noise subspace is obtained as the estimated orientation angle.

6. The method as described in claim 5, characterized in that, The determination of the theoretical steering vector corresponding to the orientation angle of the UAV relative to the smart reflective surface being a candidate orientation angle includes: Determine the spatial response vector corresponding to the orientation angle of the UAV relative to the smart reflective surface as a candidate orientation angle; A phase configuration matrix is ​​formed based on the phase configuration vector used by the intelligent reflective surface when receiving signals emitted by each target. The phase configuration matrix is ​​multiplied by the spatial response vector to obtain the theoretical steering vector.

7. A positioning device for a drone, characterized in that, The device includes: The first determining module is used to determine the target reflection signal corresponding to each of the multiple smart reflective surfaces, wherein the target reflection signal is obtained by the smart reflective surface reflecting the signal sent by the UAV; The second determining module is used to determine the estimated orientation angle of the UAV relative to the smart reflective surface based on any one of the multiple target reflection signals. The third determining module is used to determine the target position coordinates of the UAV based on the estimated orientation angle of the UAV relative to each of the smart reflective surfaces.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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