Base station antenna parameter calibration methods, electronic equipment, and storage media

Abstract

This application discloses a parameter calibration method for a base station antenna. The method includes: acquiring first location information reported by a positioning device and second location information reported by a base station; wherein the first location information includes multiple coordinates of the positioning device itself reported during movement, and the second location information includes multiple coordinates of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle; based on the first and second location information, the deviation of the base station antenna is determined, the deviation of the base station antenna including angular deviation and positional deviation; and based on the deviation, the parameter calibration value of the base station antenna is determined. The parameter calibration values ​​determined in this application embodiment have high accuracy, require no manual intervention, can improve the automation level of base station antenna parameter calibration, save labor costs, and improve calibration efficiency.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method for calibrating the parameters of a base station antenna, an electronic device, and a storage medium. Background Technology

[0002] During base station antenna installation, antenna technicians use measuring tools to install the antennas according to the location, height, and angle determined during network planning. Due to various factors such as manual labor, instrumentation, and the construction environment, there is a significant deviation between the technical parameters and the actual values. After antenna installation, the technical parameters of the base station antenna will change due to natural vibration, wind vibration, and the influence of the external environment, requiring timely calibration to ensure communication coverage and sensing accuracy. Currently, related technologies rely on manual determination of technical parameter calibration values. Factors such as the antenna technician's skill level and the measuring tools used can lead to significant errors in the calibration values, often resulting in calibration accuracy that does not meet service requirements. Furthermore, manual calibration is inefficient. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method for calibrating the parameters of a base station antenna, an electronic device, a storage medium, and a program product.

[0004] The technical solution of this application embodiment is implemented as follows:

[0005] On one hand, embodiments of this application provide a method for calibrating the parameters of a base station antenna, the method comprising:

[0006] The system acquires first location information reported by the positioning device and second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle;

[0007] Based on the first location information and the second location information, the deviation of the base station antenna is determined; the deviation of the base station antenna includes angular deviation and positional deviation.

[0008] Based on the deviation, the parameter calibration values ​​of the base station antenna are determined.

[0009] In the above scheme, the angular deviation of the base station antenna includes the rotation matrix of the base station antenna, and the positional deviation of the base station antenna includes the translation vector of the antenna base station.

[0010] In the above scheme, the first location information and the second location information include corresponding timestamps. Before determining the deviation of the base station antenna based on the first location information and the second location information, the method further includes:

[0011] Based on the timestamps corresponding to the first location information and the second location information, the first location information and the second location information are fused to obtain fused location information; the fused location information includes multiple data pairs, and the data pairs are composed of coordinate information reported by the positioning device and the base station at the same time.

[0012] In the above scheme, the method further includes:

[0013] Obtain the signal-to-noise ratio when the base station reports the second location information;

[0014] The weight of each data pair in the fused location information is set based on the signal-to-noise ratio.

[0015] In the above scheme, after setting the weight of each data pair in the fused location information based on the signal-to-noise ratio, the method further includes:

[0016] Based on the fused location information and the weight of each data pair in the fused location information, the first weighted centroid of the first location information and the second weighted centroid of the second location information in the fused location information are determined;

[0017] Based on the first weighted centroid, the second weighted centroid, and the fused location information, the rotation matrix and translation vector of the base station antenna are determined;

[0018] Correspondingly, determining the parameter calibration value of the base station antenna based on the deviation includes:

[0019] Based on the rotation matrix and the translation vector, the parameter calibration values ​​of the base station antenna are determined.

[0020] In the above scheme, determining the parameter calibration values ​​of the base station antenna based on the rotation matrix and the translation vector includes:

[0021] Based on the rotation matrix, the offset angle of the base station antenna is determined;

[0022] Based on the translation vector, the position deviation value of the base station antenna is determined; the parameter calibration value includes the offset angle and the position deviation value.

[0023] In the above scheme, before obtaining the first location information reported by the positioning device and the second location information reported by the base station, the method further includes:

[0024] The movement trajectory of the vehicle is planned to obtain a planned trajectory; the planned trajectory is used to instruct the vehicle to move according to the set trajectory, and the planned trajectory includes a first trajectory and a second trajectory, wherein the first trajectory is used to determine the parameter calibration value of the base station antenna, and the second trajectory is used to verify the parameters of the calibrated base station antenna.

[0025] In the above scheme, after determining the parameter calibration value of the base station antenna, the method further includes:

[0026] The third location information reported by the positioning device and the fourth location information reported by the base station are obtained; the third location information includes multiple coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth location information includes multiple coordinate information reported by the base station when the positioning device is detected moving along the second trajectory;

[0027] Based on the third and fourth location information, the antenna parameters of the calibrated base station are verified.

[0028] On the other hand, embodiments of this application also provide a parameter calibration device for a base station antenna, comprising:

[0029] An acquisition module is used to acquire first location information reported by the positioning device and second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle;

[0030] The determining module is used to determine the deviation of the base station antenna based on the first location information and the second location information; the deviation of the base station antenna includes angular deviation and positional deviation.

[0031] A calibration module is used to determine the parameter calibration values ​​of the base station antenna based on the deviation.

[0032] On the other hand, embodiments of this application also provide an electronic device, including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, performs the steps in the above-described method.

[0033] On the other hand, embodiments of this application also provide a computer storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described method.

[0034] On the other hand, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the steps in the above-described method.

[0035] This invention, in its embodiments, acquires first location information reported by a positioning device and second location information reported by a base station. The first location information includes multiple coordinates of the positioning device itself reported during movement, while the second location information includes multiple coordinates of the positioning device detected by the base station. The positioning device is mounted on a moving vehicle. Based on the first and second location information, the deviation of the base station antenna is determined. This deviation includes angular and positional deviations. Based on these deviations, the parameter calibration values ​​for the base station antenna are determined. This invention uses a moving positioning device, combined with the base station's sensing capabilities, to assist in determining the parameter calibration values ​​for the base station antenna. The high accuracy of the parameter calibration values ​​ensures that the calibration precision meets service requirements. Furthermore, it eliminates the need for manual intervention, improving the automation level of base station antenna parameter calibration, saving labor costs, and increasing calibration efficiency. Attached Figure Description

[0036] Figure 1 This is a schematic diagram illustrating the implementation process of a parameter calibration method for a base station antenna provided in an embodiment of the present invention;

[0037] Figure 2 This is an architecture diagram of a sensor base station calibration system provided in an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of flight path planning at different altitudes provided in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of an arc-shaped flight path provided in an embodiment of the present invention;

[0040] Figure 5 This is a flowchart of a base station antenna calibration process provided in an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0042] The present application will now be described in further detail with reference to the accompanying drawings and embodiments.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0044] The low-altitude economy is increasingly demanding low-altitude networks and low-altitude monitoring, and integrated sensing and communication systems can effectively meet this need. By reusing a single set of hardware, sensing and communication base stations can interact with the backend in real time, achieving efficient scheduling and providing seamless communication and high-precision sensing services, thus expanding the service scope of traditional wireless networks.

[0045] The accuracy of the engineering parameters of inductive base station antennas is fundamental to network optimization and service deployment. Therefore, during network construction or optimization, the engineering parameters of inductive base station antennas need to be calibrated promptly and accurately, including the antenna's longitude, latitude, height, and the horizontal and elevation angles of installation.

[0046] During base station antenna installation, antenna technicians use measuring tools to install the antennas according to the location, height, and angle determined during network planning. Due to various factors such as manual labor, instrumentation, and the construction environment, there can be significant deviations between the technical parameters and the actual values. After antenna installation, the technical parameters of the base station antenna will change due to natural vibration, wind vibration, and the influence of the external environment, requiring timely calibration to ensure communication coverage and sensing accuracy.

[0047] Currently, base station antennas are mainly calibrated manually using tools such as compasses and magnifying glasses during installation, and subsequent calibration is performed based on engineering parameters using MR and MDT data from road tests.

[0048] Existing radar calibration methods can be used to perform calibration with the help of calibration objects in the natural environment or artificial calibration objects. The parameters are solved by the one-to-one correspondence between the world coordinate system of the selected calibration object or feature and the feature points in the radar coordinate system.

[0049] In summary, the relevant technologies have the following drawbacks:

[0050] 1. During the construction or expansion of base stations, the calibration of base station antenna position, height, and angle information relies heavily on manual labor. Factors such as the antenna technician's skill level, proficiency, and measuring tools can introduce significant errors. Due to the massive amount of data generated by base stations, manual calibration is inefficient, costly, and its accuracy often fails to meet operational requirements.

[0051] 2. In network optimization, the verification and calibration of base station antenna parameters typically utilizes communication service data such as MR and MDT to obtain user distribution and signal quality information. Parameter calibration is then performed based on antenna parameters and maps. The calibration effect usually aims to meet service coverage requirements, but suffers from insufficient calibration accuracy. Due to the different characteristics of communication services and sensing services, the required accuracy of base station antenna parameter calibration differs. For communication services, the accuracy of antenna parameter calibration indirectly affects user communication services through signal coverage; for sensing services, the accuracy of antenna calibration directly affects the calculation of sensing service detection. Existing base station antenna parameter calibration methods cannot meet the requirements of sensing services.

[0052] 3. Existing radar calibration technologies primarily focus on calibrating extrinsic parameters and correcting measurement data for radar detection operations. Parameter calibration based on calibration objects typically requires manual operation, resulting in a large workload, low efficiency, and high cost. Radar deployment methods, locations, and sensing base stations vary, making ground-based calibration methods unsuitable for applications such as low-altitude detection and communication services using sensing base stations. Current radar calibration technologies do not consider the impact of radar signal quality on the confidence level of detection data at different locations, thus affecting calibration accuracy and efficiency.

[0053] 4. As an emerging technology, the integration of communication and sensing is developing rapidly. The parameter calibration and verification of communication and sensing base stations involve both communication and sensing services. Currently, there is no mature technology that can simultaneously achieve parameter calibration and sensing result verification for both services.

[0054] To address the shortcomings of the aforementioned related technologies, embodiments of the present invention provide a method for calibrating the parameters of a base station antenna, which can efficiently, quickly, accurately, and at low cost determine and calibrate antenna engineering parameters. To illustrate the technical solution described in this invention, specific embodiments are provided below.

[0055] refer to Figure 1 , Figure 1 This is a schematic diagram illustrating the implementation process of a parameter calibration method for a base station antenna provided in an embodiment of the present invention. The parameter calibration method for the base station antenna includes:

[0056] S101, acquire the first location information reported by the positioning device and the second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during the movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle.

[0057] In this embodiment, the positioning device can be a drone. With the continuous development of connected terminals, especially the maturing drone industry, drones have been widely used in various vertical industries. Drones equipped with Real-Time Kinematic (RTK) technology can achieve centimeter-level positioning accuracy. Combined with communication modules, networked drones can achieve real-time, high-speed data interaction between the drone and the cloud.

[0058] Other calibration tools and calibration algorithms can also be used to achieve the same calibration purpose. For example, vehicle-mounted or ship-mounted equipment equipped with high-precision positioning modules can report reference data such as position to the calibration calculation platform.

[0059] In one embodiment, a drone equipped with an RTK high-precision positioning module is used as a target reference source. The drone flies within the coverage area of ​​the sensing base station according to the planned route and reports the drone's flight status data in real time (including latitude and longitude, flight speed, heading angle, roll angle, pitch angle, etc. with timestamps). Then, the sensing results of the target drone detected by the sensing base station are combined to achieve the calibration of the antenna parameters of the sensing base station.

[0060] Figure 2 This is an architecture diagram of a sensor base station calibration system provided by an embodiment of the present invention. A calibration drone flies according to a planned route within the coverage area of ​​the sensor base station. The sensor base station performs real-time detection and sensing of the drone and reports the detection results, combined with a timestamp, to the calibration calculation platform. Simultaneously, the calibration drone transmits its own flight information, carrying a timestamp, to the calibration calculation platform in real time. The calibration calculation platform correlates the drone's actual position information and the sensor base station's detected position information at the same time based on the timestamps. It uses a rotation matrix and translation vector to model the difference between the two, and uses the least squares method to calculate the angle and position deviation of the sensor base station antenna for multiple measurement points, thus completing the sensor base station antenna parameter calibration.

[0061] The first location information includes multiple location data reported by the positioning device during its movement. Taking a drone as an example, the first location information includes latitude, longitude, altitude, flight speed, heading angle, roll angle, and pitch angle, all with timestamps. During the drone's flight, the calibration drone reports flight data to the calibration and calculation platform in real time via its communication module, including at least situational information such as location and flight speed with associated timestamps.

[0062] The second location information includes multiple location information of the positioning device detected by the base station during its movement, including timestamps and corresponding spatial coordinates.

[0063] The drones fly along a preset trajectory, and the location reported by the drones and the location information of the drones detected by the sensor base stations are both based on the world coordinate system.

[0064] For example, the location reported by the drone uses a vector p. u It means that p u =[x u ,y u ,z u ],in:

[0065] x u This indicates the X-coordinate of the UAV's reference position in the world coordinate system;

[0066] y u This represents the Y-coordinate of the UAV's reference position in the world coordinate system;

[0067] z uThis represents the Z-coordinate of the UAV's reference position in the world coordinate system;

[0068] The location of the drone detected by the base station uses vector p b It means that p b =[x b ,y b ,z b ],in:

[0069] x b This indicates the X-coordinate of the UAV's detection location in the world coordinate system;

[0070] y b This indicates the Y-coordinate of the UAV's detection location in the world coordinate system;

[0071] z b This represents the Z-coordinate of the UAV's detection location in the world coordinate system;

[0072] The collected drone trajectory data point set is P u_train ,

[0073]

[0074] M represents the number of trajectory points in the situational data reported by the UAV. Let i be the timestamp of the trajectory point. Let i be the coordinates of the UAV reference position trajectory point i in the world coordinate system.

[0075] The set of drone trajectory data points collected by the sensor base station is P. b_train ,

[0076]

[0077] N represents the number of trajectory points in the detection drone data reported by the base station, t bi Let i be the timestamp of trajectory point i, (x bi ,y bi ,z bi ) represents the coordinates of the base station detection trajectory point i in the world coordinate system.

[0078] according to Figure 2 The system architecture diagram shows that UAV flight data can be reported to the calibration and calculation platform via cellular links, UAV-built links, or other links. UAV data detected by the sensing base station can be reported to the calibration and calculation platform via sensing function (SF) network elements or other links. The calibration and calculation platform can also be deployed at the edge. This application embodiment does not restrict the data reporting method or the deployment scheme of the sensing and calculation platform.

[0079] S102, based on the first location information and the second location information, determine the deviation of the base station antenna, wherein the deviation of the base station antenna includes angular deviation and positional deviation.

[0080] Because the actual latitude and longitude, mounting height, elevation angle, horizontal angle, roll angle and design values ​​of the antennas at the sensing base station are objectively different during installation, even though the sensing base station has undergone electronic calibration and signal calibration before installation, these deviations in the position and angle of the antennas during installation will still cause errors in the estimation of the detection target when the sensing base station detects and senses.

[0081] In this embodiment, the deviation of the base station antenna includes angular deviation and positional deviation. In one embodiment, the angular deviation of the base station antenna includes the offset angle between the actual antenna value and the design value in terms of pitch angle, roll angle, and horizontal angle. The positional deviation of the base station antenna includes the deviation of the antenna position relative to the design value.

[0082] The first location information reported by the positioning device can be considered accurate location information. Due to the deviation of the base station antenna, the second location information measured by the base station is inaccurate. Therefore, the second location information deviates from the first location information. The deviation of the base station antenna can be calculated based on the first location information reported by the positioning device and the second location information reported by the base station.

[0083] In one embodiment, different deviations of the base station antenna will result in different deviations of the first location information and the second location information. The correspondence between the deviation of the base station antenna and the deviations of the first location information and the second location information can be summarized. Based on this correspondence, the deviation of the base station antenna can be deduced from the deviation of the first location information and the second location information.

[0084] S103, determine the parameter calibration value of the base station antenna based on the deviation.

[0085] For example, the parameter calibration values ​​of a base station antenna include the calibration values ​​of the antenna's elevation angle, roll angle, horizontal angle, and the calibration values ​​of the antenna's latitude and longitude.

[0086] The parameter calibration values ​​for the base station antenna are determined based on the deviation. First, the parameter calibration values ​​are calculated based on the deviation, and then the antenna parameters are calibrated based on these values. Antenna parameter calibration can be performed manually or automatically by a control device on the antenna based on the parameter calibration values.

[0087] For example, α, β, and γ are the offset angles (calibration values) between the actual and design values ​​of the antenna in terms of elevation, roll, and horizontal angles, obtained through calibration methods. Based on these offset angles, the horizontal angle, elevation angle, and roll angle of the base station antenna can be calibrated.

[0088] For example, calibrating the latitude and longitude of an antenna requires using a translation vector t. t represents the deviation of the calibrated antenna position from its design value. If the design position for the base station antenna deployment is o = (x0, y0, z0), then the calibrated antenna position is o' = (x0 + Δx, y0 + Δy, z0 + Δz).

[0089] This invention embodiment acquires first location information reported by a positioning device and second location information reported by a base station. The first location information includes multiple coordinates of the positioning device itself reported during movement, and the second location information includes multiple coordinates of the positioning device detected by the base station. The positioning device is mounted on a moving vehicle. Based on the first and second location information, the deviation of the base station antenna is determined. The deviation of the base station antenna includes angular deviation and positional deviation. Based on the deviation, the parameters of the base station antenna are calibrated. This invention embodiment uses a moving positioning device, combined with the sensing capabilities of the base station, to assist in determining the parameter calibration values ​​of the base station antenna. The parameter calibration values ​​have high accuracy, ensuring that the calibration accuracy meets service requirements. Furthermore, no manual intervention is required, which can improve the automation level of base station antenna parameter calibration, save labor costs, and improve calibration efficiency.

[0090] In one embodiment, the angular deviation of the base station antenna includes the rotation matrix (RM) of the base station antenna, and the positional deviation of the base station antenna includes the translation vector of the antenna base station.

[0091] The measurement error caused by the antenna position and angle deviation can be decomposed into errors in the antenna rotation direction and position translation direction.

[0092] In one embodiment, the first location information and the second location information include corresponding timestamps. Before determining the deviation of the base station antenna based on the first location information and the second location information, the method further includes:

[0093] Based on the timestamps corresponding to the first location information and the second location information, the first location information and the second location information are fused to obtain fused location information; the fused location information includes multiple data pairs, and the data pairs are composed of coordinate information reported by the positioning device and the base station at the same time.

[0094] Taking a drone as an example, after the calibration and calculation platform obtains the drone flight data and the sensor base station detection data trajectory set, it needs to perform fusion processing based on the timestamp to obtain the drone's reference position and detection position at the same time. Since the frequency of drone flight data reporting is greater than that of sensor base station reporting, the number of trajectory points reported by the drone on the same flight path will be greater than the number of trajectories reported by the sensor base station. Therefore, the drone flight data can be fused based on the detection data reported by the sensor base station.

[0095] In the fusion processing, the set of trajectory points P reported by the sensing base stations is traversed. b_train The fused trajectory point set is obtained. This refers to the fusion of location information, where That is, the merged timestamp is based on the timestamp of the trajectory points reported by the sensing base station, and the number of trajectory points N is the same as the number of trajectory points reported by the sensing base station. For the corresponding time after fusion The detection position and reference position (data pair) represent the error in calibrating the UAV's detection and reference positions.

[0096] exist At that moment, the corresponding From P b_train of Get it anytime. Unable to directly from P u_train The query results are available from P. u_train Find the distance Recent Moments With its corresponding position Based on this, add position offset estimation The reference position of the drone at any given time.

[0097] in for and Time difference, for The drone's flight speed v j Components on the X-axis for The drone's flight speed v j Components on the Y-axis for The drone's flight speed v j The component along the Z-axis, v j Reporting can be done from drones The data was obtained from the flight status data at any given time.

[0098] Because sensor data contains measurement errors, and may even include false alarms or other interference reported to the calibration and calculation platform, outliers need to be removed before calibration and calculation. This involves traversing the entire fused data point set (fused location information) P. c_train Calculate each Distance between reference position and detection position at all times like Exceeding the threshold δ d ,Right now This fusion calibration point is considered an outlier, from P c_train Delete it; otherwise, consider it a normal value and retain it in the dataset.

[0099] This embodiment associates UAV flight data and sensor base station detection data using timestamps and performs calibration calculations on the associated data based on a unified coordinate system. Real-time reported data improves the timeliness of calibration; however, for non-real-time reporting in different usage scenarios, offline data fusion processing can also complete the calibration work. The filtering of abnormal data and the estimation compensation for position misalignment during data association are also crucial for calibration calculations, affecting the performance and accuracy of the calibration.

[0100] In one embodiment, the method further includes:

[0101] Obtain the signal-to-noise ratio when the base station reports the second location information;

[0102] The weight of each data pair in the fused location information is set based on the signal-to-noise ratio.

[0103] Because the accuracy of sensing by a sensing base station fluctuates depending on the quality of the sensed signal—for example, a decrease in the signal-to-interference-plus-noise ratio (SINR) will reduce the reliability of the detection results—to further improve calibration accuracy, the (fused location information) P... c_train Different weights are applied to each calibration point, thereby increasing the weight of reliable observations on calibration or decreasing the weight of unreliable observations on calibration correction.

[0104] The fundamental reason for assigning weights to different calibration points is to reduce the impact of fluctuations in the quality of the sensed signal on the calibration results and improve calibration accuracy. Therefore, when generating the weights for each fusion calibration point, the signal quality at each point can be used as a benchmark, and the settings can be optimized through simulation and measured data. Tables 1 and 2 provide a reference method for weighting values. Weights can also be set in other ways, such as using the Friis transmission equation to calculate the signal received power at different distances and then setting the weighting coefficients.

[0105] Distance from calibration point to base station (meters) weight 0-200 1 200-500 0.95 500-800 0.9 800-1200 0.85

[0106] Table 1. Distance and Weight Reference Mapping Table

[0107] Signal-to-interference-plus-noise ratio (SINR) (dB) weight >＝25 1 20-25 0.95 15-20 0.9 10-15 0.85 <10 0.8

[0108] Table 2. Signal-to-Interference-Ratio and Weighting Reference Mapping Table

[0109] In this embodiment, the weight w of each verification point is generated by estimating the signal-to-noise ratio. i Finally, the trajectory point dataset P is expanded and fused. c_train Element is P c_train Updated to:

[0110]

[0111] This embodiment assigns weights based on signal quality to facilitate more accurate estimation of calibration parameters. Assigning different weights to each sampling point in the dataset based on signal quality also significantly impacts the calibration results; points with good signal quality should have higher weights for parameter calibration to reduce the influence of points with poor signal quality on parameter calibration estimation.

[0112] In one embodiment, after setting the weight of each data pair in the fused location information based on the signal-to-noise ratio, the method further includes:

[0113] Based on the fused location information and the weight of each data pair in the fused location information, the first weighted centroid of the first location information and the second weighted centroid of the second location information in the fused location information are determined;

[0114] Based on the first weighted centroid, the second weighted centroid, and the fused location information, the rotation matrix and translation vector of the base station antenna are determined;

[0115] Correspondingly, determining the parameter calibration value of the base station antenna based on the deviation includes:

[0116] Based on the rotation matrix and the translation vector, the parameter calibration values ​​of the base station antenna are determined.

[0117] The above embodiments yielded a set of fused trajectory points, each trajectory point containing time information. UAV location detection and reference position This embodiment uses these data for calibration calculation.

[0118] The measurement error caused by antenna position and angle deviations can be decomposed into errors in the antenna rotation direction and position translation direction. Let P c_train The detection position vector of the trajectory point M is p i , The corresponding UAV reference position vector is q i , For the same trajectory point, the detection vector p i and reference vector q i The lengths are the same, with only deviations in the rotation angle θ and translation vector t. Based on rigid body rotation theory, the calibration UAV measurement vector p can be obtained. i and the true vector q i Relationship:

[0119] q i =R*p i +t

[0120] Where R is the antenna rotation matrix, and t is the translation vector between the antenna reference position and the design position.

[0121]

[0122] q i =R*p i +t can be represented as:

[0123]

[0124] Fusion trajectory point set P c_train The set of reference position vectors for a UAV can be represented by matrix B:

[0125]

[0126] Fusion trajectory point set P c_train The set of detection vectors for a mid-range induction base station can be represented by matrix A:

[0127]

[0128] Then matrices B and A satisfy B = R*A + t, that is, matrix B can be obtained by performing vector rotation and translation on the point set A.

[0129] Given matrices B and A, based on the overdetermined system of equations B = R*A + t, the rotation matrix R and translation vector t can be estimated using the weighted least squares method, thus obtaining the equations satisfying the given conditions. R and t.

[0130] Through derivation, estimating R and t can be transformed into solving by singular value decomposition of the weighted covariance matrices of two datasets B and A. The steps are as follows:

[0131] (1) Calculate P c_train Weighted centroid of mid-location dataset

[0132] The elements of the sensor base station location detection dataset are The second weighted centroid of the second location information is Centroid. p ,

[0133]

[0134] The drone reference position dataset elements are The first weighted centroid of the first location information is Centroid. q ,

[0135]

[0136] (2) Solving for the translation vector

[0137] Let the translation vector function be F(t), its expression is:

[0138]

[0139] The optimal solution for t can be obtained by taking the derivative of F(t).

[0140]

[0141] Divide both sides by achievable That is, t = Centroid q -R*Centroid p .

[0142] (3) Solving for the rotation matrix

[0143] The expression for t is t = Centroid q -R*Centroid p Substituting into the F(t) function, we get:

[0144]

[0145] Let x i =p i -Centroid p y i =q i -Centroid q Then the solution for the optimal rotation matrix R becomes

[0146]

[0147] Through derivation ||Rx i -y i || 2 =x i T x i -2y i T Rx i +y i T y i ,but

[0148]

[0149] Solving for the rotation matrix R can then be transformed into solving for the trace tr(WY) that maximizes the matrix. T The rotation matrix R of RX).

[0150] According to tr(AB)=tr(BA), we can obtain

[0151] tr(WY T RX)=tr((WY T (RX))=tr((RX)(WY T ))=tr(RXWY T )

[0152] Let S = XWY T Perform singular value decomposition (SVD) on S, S = U∑V T ,but

[0153] tr(RXWY T )=tr(RS)=tr(RUΣV T ) = tr(V T RU)

[0154] Since U, V, and R are all orthogonal matrices, then M = V T If RU is also an orthogonal matrix, then the maximum trace can only be obtained when M is an identity matrix, which satisfies the following condition. If the rotation matrix R corresponds to an identity matrix M, then I = M = V T RU, then R = VU T Where U and V are matrices S = XWY T The singular value decomposition matrix.

[0155] S = XWY T , where W is a weighted diagonal matrix, X is a matrix whose column elements are the vectors obtained by subtracting the corresponding weighted centroid from the probe position, and Y is a matrix whose column elements are the vectors obtained by subtracting the corresponding weighted centroid from the reference position.

[0156] X = [x T 1,x T 2,...x T i ,...x T N ], Y = [y T 1,y T 2,...y T i ,...y T N ],

[0157]

[0158] The rotation matrix R can be obtained through Singular Value Decomposition (SVD).

[0159] [U,S,V]=SVD(H), R=VU T .

[0160] The aforementioned rotation matrix and translation vector represent the angular and positional deviations of the base station antenna. Based on the rotation matrix and translation vector, the base station parameter calibration values ​​are calculated, which include the offset angle and positional deviation values.

[0161] In one embodiment, determining the parameter calibration values ​​of the base station antenna based on the rotation matrix and the translation vector includes:

[0162] Based on the rotation matrix, the offset angle of the base station antenna is determined;

[0163] Based on the translation vector, the position deviation value of the base station antenna is determined; the parameter calibration value includes the offset angle and the position deviation value.

[0164] The offset angle of a base station antenna includes the offset angle of the base station antenna in the pitch angle, roll angle, and horizontal angle. The position deviation value of the base station antenna includes the deviation of the antenna position after calibration from the design value.

[0165] The rotation matrix R has been obtained above. Assuming that the actual angle and design angle of the inductive base station antenna are offset by rotation angles α, β, and γ around the X, Y, and Z axes respectively, the rotation matrix around the X-axis is R. x (α):

[0166]

[0167] The matrix for rotation about the Y-axis is R. y (β):

[0168]

[0169] The matrix for rotation about the Z-axis is R. z (γ):

[0170]

[0171] The rotation matrix R can be obtained by rotating in the ZXY rotation order (first around its own axis Z, then around its own axis Y, and finally around its own axis X).

[0172] R = R y (β)*R x (α)*R z (γ)

[0173] Substituting α, β, and γ, the expression becomes:

[0174]

[0175] Substituting into the R expression, we get:

[0176]

[0177] The values ​​of α, β, and γ are obtained:

[0178]

[0179] α, β, and γ are the offset angles of the actual and design values ​​of the antenna in terms of elevation, roll, and horizontal angle, obtained through calibration methods. Based on these offset angles, the horizontal angle, elevation angle, and roll angle of the inductive base station antenna can be calibrated.

[0180] For antenna latitude and longitude calibration, a translation vector t is required. The translation vector, already calculated above, is t = -R*Centroid. A +Centroid B = (Δx, Δy, Δz), where t is the deviation of the calibrated antenna position from the design value. If the design position of the antenna deployment of the inductive base station is o = (x0, y0, z0), then the calibrated antenna position is o' = (x0 + Δx, y0 + Δy, z0 + Δz).

[0181] In this embodiment, during calibration calculation, a rotation matrix and a translation vector are used to associate the UAV reference position and the detection position. By combining multiple measurement trajectories, the rotation matrix and translation vector are solved using the weighted least squares method. Finally, the horizontal angle, pitch angle, and roll angle of the sensing base station antenna relative to the design value, as well as the latitude, longitude, and altitude calibration values ​​of the antenna, are obtained.

[0182] In one embodiment, before obtaining the first location information reported by the positioning device and the second location information reported by the base station, the method further includes:

[0183] The movement trajectory of the vehicle is planned to obtain a planned trajectory; the planned trajectory is used to instruct the vehicle to move according to the set trajectory, and the planned trajectory includes a first trajectory and a second trajectory, wherein the first trajectory is used to determine the parameter calibration value of the base station antenna, and the second trajectory is used to verify the parameters of the base station antenna after calibration based on the parameter calibration value.

[0184] Since different coverage scenarios have different environments and there are multiple options for route planning, and this embodiment does not depend on a specific route, this embodiment does not fix a specific route, but only provides the criteria for route planning, which can meet the parameter calibration requirements.

[0185] One embodiment uses a drone as a detection target to calibrate the antenna parameters of a sensing base station. To improve the accuracy of the calibration, when collecting detection and sensing data, the typical signal coverage area of ​​the sensing base station is traversed as much as possible, such as the coverage center area, the coverage boundary area (up, down, left, and right), and the coverage remote area.

[0186] Since this embodiment uses the deviation between base station detection data and target reference positioning data to train and obtain the calibration values ​​of antenna parameters, the UAV flight path can be divided into a training path (first trajectory) and a verification path (second trajectory) to ensure the consistency and ubiquity of the calculated parameters. During verification, the training path is used to check the consistency of parameter estimation, and other paths are used to check the ubiquity of parameter estimation.

[0187] As an example, training flight paths can be planned at different altitudes within the coverage area of ​​the sensing base station. For example, at altitudes of 80 meters, 100 meters, 120 meters, and 150 meters, longitudinal flight paths perpendicular to the antenna normal can be planned. Three flight paths are planned for the extension direction, flying both near and away from the sensing base station within its coverage area. One path follows the horizontal angle of the normal, while the other two paths are spaced at angles on either side of this path, forming the planned horizontal coverage angle. Three longitudinal flight paths are planned, which can be based on the distance from the sensing base station. For example, paths can be planned at distances of 200 meters, 400 meters, and 600 meters from the sensing base station, flying along the direction perpendicular to the antenna normal. Flight path planning at each altitude can be referenced. Figure 3 .

[0188] In another embodiment, the training route can also be planned at different altitudes according to irregular shapes, such as circles or other arbitrary shapes, like... Figure 4 As shown.

[0189] The validation routes are also planned at different altitudes. The validation routes mainly verify the universality of the calibration parameters and need to reflect the differences from the training routes. Different lateral and longitudinal routes, or other arbitrary shapes of routes, can be planned at each altitude compared to the training routes. No specific rules are specified here.

[0190] In one embodiment, after the trajectory is planned, the time of the positioning device and the base station is synchronized.

[0191] After the flight path is planned, the drone flies manually or by marking points along the path. Before takeoff, the drone and the sensor base station need to be synchronized in time, which can be done using network synchronization or GPS / BeiDou time synchronization.

[0192] After the UAV completes time synchronization, it is necessary to confirm that the high-precision positioning module is working properly and can report flight status data with timestamps to the calibration and calculation platform in real time. After the sensing base station completes time synchronization, it is necessary to confirm that the detection and sensing functions are working properly and can report detection and sensing data with timestamps to the calibration and calculation platform.

[0193] Before takeoff, the data reporting frequencies of the aircraft and the base station need to be set according to their respective capabilities. Since the frequency of flight status data reported by the UAV can reach over 10Hz, while the frequency of detection results reported by the sensor base station is generally no more than 3Hz, the two data reporting frequencies differ and need to be aligned during calibration and calculation. It is advisable to set the UAV data reporting frequency f as high as possible. uav_data The frequency f of base station detection results reporting is greater than bs_data .

[0194] After completing these preparations, the flight will proceed according to the planned route.

[0195] In one embodiment, after determining the parameter calibration value of the base station antenna, the method further includes:

[0196] The third location information reported by the positioning device and the fourth location information reported by the base station are obtained; the third location information includes multiple coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth location information includes multiple coordinate information reported by the base station when the positioning device is detected moving along the second trajectory;

[0197] Based on the third location information and the fourth location information, the antenna parameters of the base station after calibration based on the parameter calibration values ​​are verified.

[0198] Taking a drone as an example, the set of drone trajectory data points (third location information) collected on the verification route is P. u_test ,

[0199]

[0200] K represents the number of trajectory points in the situational data reported by the UAV, and t ui For the timestamp of the drone's reference position trajectory point i, Let i be the coordinates of the trajectory point i in the world coordinate system.

[0201] The set of drone trajectory data points (fourth location information) detected by the sensor base station on the verification route is P. b_test ,

[0202]

[0203] L represents the number of trajectory points in the detection drone data reported by the sensing base station, t' bi Let i be the timestamp of the trajectory point i detected by the sensor base station, (x' bi ,y' bi ,z' bi ) represents the coordinates of trajectory point i in the world coordinate system.

[0204] To improve calibration performance and accuracy, the calibration method utilizes both training and validation datasets. The training dataset is used to calculate calibration parameters, while the validation dataset is used to test the accuracy of these parameters. This key aspect enhances calibration performance and accuracy, and improves the engineering applicability of the calibration method.

[0205] The system collects and verifies the UAV position measurements from the sensor base station and the UAV reference position values ​​at each sampling time along the test route. For the data collected along the test route, timestamps can be used for fusion to obtain the fused trajectory point set P. c_test :

[0206]

[0207] For P c_test A point in a point set Its reference position is The location detected by the sensor base station is To verify the accuracy of the calibration calculation, the detection position of the sensing base station is calibrated and corrected, and then compared with the reference position to obtain the calibration error of the positioning result.

[0208] When calibrating and correcting the position of a drone detected by a sensor base station, a homogeneous matrix can be used. The homogeneous matrix P is based on the rotation matrix R and translation vector t calculated using S104 verification, and its composition is as follows:

[0209]

[0210] After calibration, the position vector of the calibrated UAV detected by the sensor base station Become The relationship between the two is

[0211] Let matrix A be P c_test Point-based centralized sensing base station location detection The set matrix, B is P c_test Reference position of Chinese UAV The set matrix, where B' is the corrected position of A. Set matrix.

[0212]

[0213] Matrix A is calibrated to obtain matrix B'.

[0214]

[0215] Alternatively, the calibrated matrix can be obtained by B' = R*A + t.

[0216] By calculating the difference between B' and B, the deviation Δerr between the calibrated position and the reference position can be obtained.

[0217]

[0218] If Δerr exceeds the threshold Γ, it indicates that the calibration error is large and recalibration is required.

[0219] After calibration, the antenna parameters of the inductive base station are calibrated, and the calibrated values ​​of the antenna parameters are output, including horizontal angle, elevation angle, roll angle, latitude and longitude, and antenna height.

[0220] Figure 5 This is a flowchart of a base station antenna parameter calibration method provided in an embodiment of the present invention, including:

[0221] S501, Route planning.

[0222] The UAV flight path can be divided into training paths and validation paths to ensure the consistency and ubiquity of the calculated parameters. During validation, the training path is used to verify the consistency of the parameter estimation, while other paths are used to verify the ubiquity of the parameter estimation.

[0223] S502, flight preparation.

[0224] After the flight path is planned, the drone flies manually or by marking points along the path. Before takeoff, the drone and the sensor base station need to be synchronized in time, which can be done using network synchronization or GPS / BeiDou time synchronization.

[0225] S503, Data Acquisition and Reporting.

[0226] The calibration and calculation platform collects UAV flight data and sensor base station detection data, with the two data sets correlated using timestamps. Both the location reported by the UAV and the detected UAV location information reported by the sensor base station are based on the world coordinate system.

[0227] S504, calibration solution.

[0228] Based on the collected UAV flight data and sensor base station detection data, the rotation matrix and translation vector are calculated, and the sensor base station calibration parameters are obtained through the rotation matrix and translation vector.

[0229] S505, Verification Calibration.

[0230] In S504, the position and angle calibration of the sensing base station antenna were completed based on the training flight path data. This step uses the verification flight path data collected in S503 to verify the calibration parameters calculated by S504 and evaluate the accuracy of the verification parameters.

[0231] S506, calibration complete.

[0232] Complete the calibration of the antenna parameters of the inductive base station and output the calibrated values ​​of the antenna parameters, including horizontal angle, elevation angle, roll angle, latitude and longitude, and antenna height.

[0233] This application utilizes drone flight, combined with the detection and sensing capabilities of a sensing base station, to efficiently, quickly, accurately, and cost-effectively complete antenna parameter calibration. In addition to low-altitude scenarios, this proposal can also be extended to sensing scenarios such as waterways and sea surfaces.

[0234] This embodiment of the application reports flight information to the calibration and calculation platform in real time during UAV flight. Simultaneously, the sensing base station detects and locates the UAV's position by transmitting wireless signals, obtaining the UAV's measured position and reporting it to the calibration and calculation platform. Then, the calibration and calculation platform calibrates the sensing base station antenna parameters based on the deviation between the reference position information reported by the UAV and the UAV position information detected by the sensing base station. During calibration and calculation, a rotation matrix and translation vector are used to associate the UAV's reference position and the detected position. Combining multiple measurement trajectories, a weighted least squares method is used to solve for the rotation matrix and translation vector, ultimately obtaining the angles of the sensing base station antenna's horizontal angle, elevation angle, and roll angle relative to the design values, as well as the antenna's latitude, longitude, and altitude calibration values. Different weights are assigned to calibration data under different channel quality conditions during calibration and calculation, which can further improve calibration accuracy and efficiency. Compared to traditional base station antenna parameter calibration methods, angle, latitude, and longitude calibration can be completed in one step, ensuring high calibration accuracy.

[0235] This application's embodiments associate UAV flight data and sensor base station detection data using timestamps, and perform calibration calculations on the associated data based on a unified coordinate system. Real-time reported data improves the timeliness of calibration; however, for non-real-time reporting in different usage scenarios, offline data fusion processing can also complete the calibration work. The filtering of abnormal data and the compensation for position estimation when timestamps are misaligned are also crucial for calibration calculations, affecting calibration performance and accuracy. Assigning different weights to each sampling point in the dataset based on signal quality also significantly impacts the calibration results; points with good signal quality should have higher weights for parameter calibration to reduce the impact of points with poor signal quality on parameter calibration estimation.

[0236] To improve calibration performance and accuracy, this application's embodiments utilize training and validation datasets in the calibration method. The training dataset is used to calculate calibration parameters, while the validation dataset is used to test the accuracy of these parameters. This key aspect enhances calibration performance and accuracy, and improves the engineering applicability of the calibration method.

[0237] This embodiment of the application corrects the detection results reported by the sensing base station after completing the parameter calibration of the sensing base station, in order to obtain better detection and sensing performance and accuracy. This embodiment of the application utilizes the sensing capability of the sensing base station to assist in antenna parameter calibration, thereby improving the performance of sensing services while leveraging sensing to enhance communication capabilities.

[0238] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0239] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0240] It should be noted that the technical solutions described in the embodiments of the present invention can be combined arbitrarily without conflict.

[0241] In addition, in the embodiments of the present invention, "first," "second," etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0242] This application also provides a parameter calibration device for a base station antenna, which corresponds to the parameter calibration method for a base station antenna described above. The steps in the parameter calibration method embodiment are also fully applicable to this device embodiment. The device includes:

[0243] An acquisition module is used to acquire first location information reported by the positioning device and second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle;

[0244] The determining module is used to determine the deviation of the base station antenna based on the first location information and the second location information; the deviation of the base station antenna includes angular deviation and positional deviation.

[0245] A calibration module is used to determine the parameter calibration values ​​of the base station antenna based on the deviation.

[0246] In one embodiment, the angular deviation of the base station antenna includes the rotation matrix of the base station antenna, and the positional deviation of the base station antenna includes the translation vector of the antenna base station.

[0247] In one embodiment, the first location information and the second location information include corresponding timestamps, and the device further includes:

[0248] The fusion module is used to fuse the first location information and the second location information based on the timestamps corresponding to the first location information and the second location information to obtain fused location information; the fused location information includes multiple data pairs, and the data pairs are composed of coordinate information reported by the positioning device and the base station at the same time.

[0249] In one embodiment, the device further includes:

[0250] Signal-to-noise ratio (SNR) acquisition module, used to acquire the SNR when the base station reports the second location information;

[0251] The weight setting module is used to set the weight of each data pair in the fused location information based on the signal-to-noise ratio.

[0252] In one embodiment, the device further includes:

[0253] The weighted centroid determination module is used to determine the first weighted centroid of the first location information and the second weighted centroid of the second location information in the fused location information based on the fused location information and the weight of each data pair in the fused location information;

[0254] The rotation matrix and translation vector determination module is used to determine the rotation matrix and translation vector of the base station antenna based on the first weighted centroid, the second weighted centroid, and the fused position information.

[0255] Correspondingly, the calibration module is specifically used for:

[0256] Based on the rotation matrix and the translation vector, the parameter calibration values ​​of the base station antenna are determined.

[0257] In one embodiment, the calibration module is specifically used for:

[0258] Based on the rotation matrix, the offset angle of the base station antenna is determined;

[0259] Based on the translation vector, the position deviation value of the base station antenna is determined; the parameter calibration value includes the offset angle and the position deviation value.

[0260] In one embodiment, the device further includes:

[0261] A planning module is used to plan the movement trajectory of the vehicle to obtain a planned trajectory; the planned trajectory is used to instruct the vehicle to move according to the set trajectory, and the planned trajectory includes a first trajectory and a second trajectory, wherein the first trajectory is used to determine the parameter calibration value of the base station antenna, and the second trajectory is used to verify the parameters of the base station antenna after calibration based on the parameter calibration value.

[0262] In one embodiment, the device further includes:

[0263] The location acquisition module is used to acquire the third location information reported by the positioning device and the fourth location information reported by the base station; the third location information includes multiple coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth location information includes multiple coordinate information reported by the base station when the positioning device is detected moving along the second trajectory;

[0264] The verification module is used to verify the antenna parameters of the base station after calibration based on the parameter calibration values, based on the third location information and the fourth location information.

[0265] In practical applications, the determining module and the acquiring module can be implemented by processors in electronic devices, such as central processing units (CPUs), digital signal processors (DSPs), microcontroller units (MCUs), or field-programmable gate arrays (FPGAs).

[0266] It should be noted that the parameter calibration device for base station antennas provided in the above embodiments is only illustrated by the division of the modules described above when calibrating the parameters of the base station antenna. In practical applications, the above processing can be assigned to different modules as needed, that is, the internal structure of the device can be divided into different modules to complete all or part of the processing described above. In addition, the parameter calibration device for base station antennas provided in the above embodiments and the parameter calibration method embodiments for base station antennas belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0267] The aforementioned base station antenna parameter calibration device can be in the form of an image file. After execution, this image file can be run as a container or virtual machine to implement the base station antenna parameter calibration method described in this application. However, it is not limited to the image file format; any software implementation capable of achieving the base station antenna parameter calibration method described in this application is within the scope of protection of this application.

[0268] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide an electronic device. Figure 6 This is a schematic diagram of the hardware composition structure of an electronic device provided in an embodiment of this application, such as... Figure 6 As shown, the electronic device includes:

[0269] The communication interface 601 enables information exchange with other devices, such as network devices.

[0270] The processor 602 is connected to the communication interface 601 to enable information interaction with other devices and to execute the methods provided by one or more of the above-described technical solutions when running a computer program. The computer program is stored in the memory 603.

[0271] Of course, in practical applications, the various components in an electronic device are coupled together through a bus system 604. It can be understood that the bus system 604 is used to implement communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 6 The general designated all buses as Bus System 604.

[0272] The memory 603 in this embodiment is used to store various types of data to support the operation of the computer device. Examples of such data include any computer program used to operate on the electronic device.

[0273] It is understood that memory 603 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memory.

[0274] The methods disclosed in the embodiments of this application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory. The processor reads the program from the memory and, in conjunction with its hardware, completes the steps of the aforementioned method.

[0275] Optionally, when the processor 602 executes the program, it implements the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, these will not be described in detail here.

[0276] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a first memory storing a computer program, which can be executed by a processor of a computer device to complete the steps described in the aforementioned method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0277] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, computer devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0278] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0279] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0280] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0281] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0282] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor 602 of an electronic device to complete the steps described in the parameter calibration method for a base station antenna in this application embodiment.

[0283] It should be noted that terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0284] Furthermore, the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0285] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for calibrating the parameters of a base station antenna, characterized in that, The method includes: The system acquires first location information reported by the positioning device and second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle; Based on the first location information and the second location information, the deviation of the base station antenna is determined; the deviation of the base station antenna includes angular deviation and positional deviation. The parameter calibration value of the base station antenna is determined based on the deviation.

2. The method according to claim 1, characterized in that, The angular deviation of the base station antenna includes the rotation matrix of the base station antenna, and the positional deviation of the base station antenna includes the translation vector of the antenna base station.

3. The method according to claim 2, characterized in that, The first location information and the second location information include corresponding timestamps. Before determining the deviation of the base station antenna based on the first location information and the second location information, the method further includes: Based on the timestamps corresponding to the first location information and the second location information, the first location information and the second location information are fused to obtain fused location information; the fused location information includes multiple data pairs, and the data pairs are composed of coordinate information reported by the positioning device and the base station at the same time.

4. The method according to claim 3, characterized in that, The method further includes: Obtain the signal-to-noise ratio when the base station reports the second location information; The weight of each data pair in the fused location information is set based on the signal-to-noise ratio.

5. The method according to claim 4, characterized in that, After setting the weight of each data pair in the fused location information based on the signal-to-noise ratio, the method further includes: Based on the fused location information and the weight of each data pair in the fused location information, the first weighted centroid of the first location information and the second weighted centroid of the second location information in the fused location information are determined; Based on the first weighted centroid, the second weighted centroid, and the fused location information, the rotation matrix and translation vector of the base station antenna are determined; Correspondingly, determining the parameter calibration value of the base station antenna based on the deviation includes: Based on the rotation matrix and the translation vector, the parameter calibration values ​​of the base station antenna are determined.

6. The method according to claim 5, characterized in that, Determining the parameter calibration values ​​of the base station antenna based on the rotation matrix and the translation vector includes: Based on the rotation matrix, the offset angle of the base station antenna is determined; Based on the translation vector, the position deviation value of the base station antenna is determined; the parameter calibration value includes the offset angle and the position deviation value.

7. The method according to claim 1, characterized in that, Before obtaining the first location information reported by the positioning device and the second location information reported by the base station, the method further includes: The movement trajectory of the vehicle is planned to obtain a planned trajectory; the planned trajectory is used to instruct the vehicle to move according to the set trajectory, and the planned trajectory includes a first trajectory and a second trajectory, wherein the first trajectory is used to determine the parameter calibration value of the base station antenna, and the second trajectory is used to verify the parameters of the base station antenna after calibration based on the parameter calibration value.

8. The method according to claim 7, characterized in that, After determining the parameter calibration values ​​of the base station antenna, the method further includes: The third location information reported by the positioning device and the fourth location information reported by the base station are obtained; the third location information includes multiple coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth location information includes multiple coordinate information reported by the base station when the positioning device is detected moving along the second trajectory; Based on the third location information and the fourth location information, the antenna parameters of the base station after calibration based on the parameter calibration values ​​are verified.

9. A parameter calibration device for a base station antenna, characterized in that, The device includes: An acquisition module is used to acquire first location information reported by the positioning device and second location information reported by the base station; wherein, the first location information includes multiple coordinate information of the positioning device itself reported during movement, and the second location information includes multiple coordinate information of the positioning device detected by the base station; the positioning device is mounted on a moving vehicle; The determining module is used to determine the deviation of the base station antenna based on the first location information and the second location information; the deviation of the base station antenna includes angular deviation and positional deviation. A calibration module is used to determine the parameter calibration values ​​of the base station antenna based on the deviation.

10. An electronic device, characterized in that, include: A processor and memory for storing computer programs that can run on the processor, wherein, The processor, when running a computer program, performs the steps of the method according to any one of claims 1 to 8.

11. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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