Base station antenna parameter calibration method, electronic equipment and storage medium

Abstract

The invention discloses a parameter calibration method for a base station antenna. The method comprises the following steps: acquiring first position information reported by positioning equipment and second position information reported by a base station; wherein the first position information comprises a plurality of pieces of coordinate information, reported by the positioning equipment in the moving process, of the positioning equipment, and the second position information comprises a plurality of pieces of coordinate information, detected by the base station, of the positioning equipment; the positioning equipment is arranged on the movable carrier; based on the first position information and the second position information, deviation of the base station antenna is determined, and the deviation of the base station antenna comprises angle deviation and position deviation; and determining a parameter calibration value of the base station antenna based on the deviation. The parameter calibration value determined by the embodiment of the invention is high in precision, manual participation is not needed, the automation degree of base station antenna work parameter calibration can be improved, the labor cost is saved, and the calibration efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a parameter calibration method, electronic device and storage medium of a base station antenna. Background Art

[0002] When installing the base station antenna, the antenna workers use measuring tools to perform installation and construction according to the position, height and angle determined during network planning. Due to many reasons such as labor, instruments, and construction environment, there is a large deviation between the engineering parameters and the actual values. After the antenna is installed, the engineering parameters of the base station antenna will change due to natural vibration, wind vibration and the influence of the external environment, and need to be calibrated in time to ensure communication coverage and perception accuracy. When calibrating the engineering parameters of the base station antenna, the related technology relies on manual determination of the engineering parameter calibration value. Factors such as the antenna worker's skill proficiency and measuring tools will cause large errors in the engineering parameter calibration value. The calibration accuracy usually cannot meet business needs, and the manual calibration method is inefficient. Summary of the invention

[0003] In view of this, embodiments of the present application provide a base station antenna parameter calibration method, electronic device, storage medium, and program product.

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

[0005] On the one hand, an embodiment of the present application provides a method for calibrating parameters of a base station antenna, the method comprising:

[0006] Acquire first location information reported by a positioning device and second location information reported by a 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 set on a mobile vehicle;

[0007] 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 an angle deviation and a position deviation;

[0008] Based on the deviation, a parameter calibration value of the base station antenna is determined.

[0009] In the above solution, the angle deviation of the base station antenna includes the rotation matrix of the base station antenna, and the position deviation of the base station antenna includes the translation vector of the antenna base station.

[0010] In the above solution, 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 solution, the method further includes:

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

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

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

[0016] Determine, based on the fused position information and the weight of each data pair in the fused position information, a first weighted centroid of the first position information and a second weighted centroid of the second position information in the fused position information;

[0017] Determine a rotation matrix and a translation vector of the base station antenna based on the first weighted centroid, the second weighted centroid and the fused position information;

[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, a parameter calibration value of the base station antenna is determined.

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

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

[0022] Based on the translation vector, a 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 solution, before acquiring 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 moving 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 solution, after determining the parameter calibration value of the base station antenna, the method further includes:

[0026] Acquire third position information reported by the positioning device and fourth position information reported by the base station; the third position information includes a plurality of coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth position information includes a plurality of coordinate information reported by the base station when the positioning device is detected to move along the second trajectory;

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

[0028] On the other hand, an embodiment of the present application further provides a parameter calibration device for a base station antenna, comprising:

[0029] an acquisition module, configured to acquire first location information reported by a positioning device and second location information reported by a base station; wherein the first location information includes a plurality of coordinate information of the positioning device itself reported by the positioning device during movement, and the second location information includes a plurality of coordinate information of the positioning device detected by the base station; the positioning device is disposed on a mobile vehicle;

[0030] A determination module, configured to determine a 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 an angle deviation and a position deviation;

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

[0032] On the other hand, an embodiment of the present application further provides an electronic device, comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor is used to execute the steps in the above method when running the computer program.

[0033] On the other hand, an embodiment of the present application further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method are implemented.

[0034] On the other hand, an embodiment of the present application further provides a computer program product, including a computer program, which implements the steps in the above method when executed by a processor.

[0035] The embodiment of the present invention obtains the first position information reported by the positioning device and the second position information reported by the base station, wherein the first position information includes multiple coordinate information of the positioning device itself reported during movement, and the second position information includes multiple coordinate information of the positioning device detected by the base station, and the positioning device is set on a mobile carrier. Based on the first position information and the second position information, the deviation of the base station antenna is determined, and the deviation of the base station antenna includes an angle deviation and a position deviation. The parameter calibration value of the base station antenna is determined based on the deviation. The embodiment of the present invention uses a mobile positioning device, combined with the perception capability of the base station to assist in determining the parameter calibration value of the base station antenna. The parameter calibration value has high accuracy, so that the calibration accuracy can meet business needs. And without manual participation, the degree of automation of the base station antenna engineering parameter calibration can be improved, saving labor costs and improving calibration efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of an implementation flow of a method for calibrating parameters of a base station antenna provided by an embodiment of the present invention;

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

[0038] Figure 3 It is a schematic diagram of route planning at different altitudes provided by an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of an arc-shaped route provided by an embodiment of the present invention;

[0040] Figure 5 is a base station antenna calibration flow chart provided by an embodiment of the present invention;

[0041] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0044] The low-altitude economy has an increasingly strong demand for low-altitude networks and low-altitude supervision, and the integration of telepathy can well meet this demand. By reusing a set of hardware, the telepathy base station can interact with the background in a timely manner for sensing signals and communication signals, achieve efficient scheduling, provide seamless communication and high-precision sensing services, and expand the business scope of traditional wireless networks.

[0045] The accuracy of the engineering parameters of the base station antenna is the basis for network optimization and business development. Therefore, when a new network is built or optimized, the engineering parameters of the base station antenna need to be calibrated in a timely and accurate manner, including the longitude, latitude, height, and installation level and pitch angle of the antenna.

[0046] When installing the base station antenna, the antenna workers use measuring tools to install and construct according to the position, height and angle determined during network planning. Due to many reasons such as labor, instruments, and construction environment, there is a large deviation between the engineering parameters and the actual values. After the antenna is installed, the engineering parameters of the base station antenna will change due to natural vibration, wind vibration and the external environment, and need to be calibrated in time to ensure communication coverage and perception accuracy.

[0047] During installation, existing base station antennas are mainly calibrated manually using tools such as compasses and compasses, and are subsequently calibrated based on engineering parameters using MR and MDT data from road tests.

[0048] The existing radar calibration method can be calibrated with the help of calibration objects in the natural environment or artificial calibration objects, and the parameters are solved by one-to-one correspondence between the world coordinate system of the selected calibration object or feature and the feature point pairs in the radar coordinate system.

[0049] In summary, the related technology has the following disadvantages:

[0050] 1. Related technologies When a base station is newly built or expanded, the calibration of the base station antenna position, height and angle information is too dependent on manual work. Factors such as antenna workers' skills, proficiency and measurement tools can cause large errors. Due to the huge amount of base station data, manual calibration is inefficient and costly, and the calibration accuracy usually cannot meet business needs.

[0051] 2. Related technologies usually use MR, MDT and other communication service data to check and calibrate base station antenna parameters during network optimization, obtain user distribution and signal quality information, and perform parameter calibration based on antenna parameters and maps. The calibration effect is usually aimed at meeting service coverage requirements, and there is a problem of insufficient calibration accuracy. Due to the different characteristics of communication services and perception services, the calibration accuracy of base station antenna parameters needs to be different. For communication services, the calibration accuracy of antenna parameters indirectly affects user communication services through signal coverage. For perception services, the antenna calibration accuracy directly affects the solution of detection and perception services. Existing base station antenna parameter calibration methods cannot meet the needs of perception services.

[0052] 3. The relevant radar calibration technology mainly focuses on the calibration of external parameters of radar detection services and the correction of measurement data. Parameter calibration based on calibration objects usually requires manual operation, which is labor-intensive, inefficient and costly. The radar deployment method, deployment location and telepathic base station are different. The ground calibration method cannot meet the needs of telepathic base station low-altitude detection and communication services. The existing radar calibration technology does not consider the impact of radar signal quality on the confidence of detection data at different locations, thus affecting the calibration accuracy and efficiency.

[0053] 4. As an emerging technology, synaesthesia integration is in rapid development, and the parameter calibration and calibration of synaesthesia base stations involve two services: communication and perception. Currently, there is no mature technology that can simultaneously realize the parameter calibration and perception result calibration of the two services.

[0054] In view of the shortcomings of the above-mentioned related technologies, an embodiment of the present invention provides a parameter calibration method for a base station antenna, which can efficiently, quickly, accurately and cost-effectively determine the antenna engineering parameter calibration. In order to illustrate the technical solution of the present invention, a specific embodiment is used for description below.

[0055] refer to Figure 1 , Figure 1 1 is a schematic diagram of an implementation flow of a method for calibrating parameters of a base station antenna provided in an embodiment of the present invention. The method for calibrating parameters of a base station antenna includes:

[0056] S101, obtaining first location information reported by a positioning device and second location information reported by a 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 set on a mobile vehicle.

[0057] In this embodiment, the positioning device can be a drone. With the continuous development of networked terminals, especially the continuous maturity of the drone industry, it has been widely used in various vertical industries. Drones equipped with carrier phase differential (Real-Time Kinematic, RTK) can achieve centimeter-level positioning accuracy, and the construction of networked drones with communication modules can realize real-time and high-speed interaction of data between drones 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 can be equipped with high-precision positioning modules to report position and other reference data to the calibration solution 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 of the synesthesia base station according to the planned route, and reports the drone flight status data (including longitude and latitude with timestamps, flight speed, heading angle, roll angle, pitch angle, etc.) in real time. Then, the target drone perception results detected by the synesthesia base station are combined to realize the calibration of the synesthesia base station antenna engineering parameters.

[0060] Figure 2 This is an architecture diagram of a synaesthesia base station calibration system provided by an embodiment of the present invention. The calibration drone flies according to the planned route within the coverage of the synaesthesia base station. The synaesthesia base station detects and senses the drone in real time and reports the detection results to the calibration solution platform in combination with a timestamp. At the same time, the calibration drone transmits its own flight information with a timestamp to the calibration solution platform in real time. The calibration solution platform associates the real position information of the drone and the position information detected by the synaesthesia base station at the same time according to the timestamp, uses the rotation matrix and translation vector to model the difference between the two, and uses the least squares method to solve multiple measurement points to obtain the angle and position deviation of the synaesthesia base station antenna, thereby completing the synaesthesia base station antenna engineering parameter calibration.

[0061] The first position information includes multiple position information reported by the positioning device during movement. Taking a drone as an example, the first position information includes longitude and latitude with timestamps, flight speed, heading angle, roll angle, pitch angle, etc. During the flight of the drone, the calibration drone reports flight data to the calibration solution platform in real time through the communication module, including at least the position with associated timestamps, flight speed and other situation information.

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

[0063] Among them, the drone flies according to the preset trajectory, and the position reported by the drone and the detected drone position information reported by the telepathic base station are both based on the world coordinate system.

[0064] For example, the position reported by the drone is represented by the vector p u Indicates that p u =[x u ,y u ,z u ],in:

[0065] x u Indicates the X coordinate of the drone's reference position in the world coordinate system;

[0066] y u Indicates the Y coordinate of the drone's reference position in the world coordinate system;

[0067] z uIndicates the Z coordinate of the drone's reference position in the world coordinate system;

[0068] The position of the drone detected by the base station is represented by the vector p b Indicates that p b =[x b ,y b ,z b ],in:

[0069] x b Indicates the X coordinate of the drone detection position in the world coordinate system;

[0070] y b Indicates the Y coordinate of the drone detection position in the world coordinate system;

[0071] z b Indicates the Z coordinate of the drone detection position in the world coordinate system;

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

[0073]

[0074] M is the number of trajectory points of the UAV’s reported situation data. is the timestamp of trajectory point i, is the coordinate value of the UAV reference position trajectory point i in the world coordinate system.

[0075] The collected UAV trajectory data point set detected by the synaesthesia base station is P b_train ,

[0076]

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

[0078] according to Figure 2 As shown in the system architecture diagram, the UAV flight data can be reported to the calibration and solution platform through the cellular link, or through the UAV self-built link or other links. The UAV data detected by the synaesthesia base station can be reported to the calibration and solution platform through the sensing function network element (SensingFunction, SF) or other links. The calibration and solution platform can also be deployed at the edge. The embodiment of the present application does not restrict the method of reporting collected data and the deployment scheme of the synaesthesia solution platform.

[0079] S102: Determine a deviation of the base station antenna based on the first position information and the second position information, where the deviation of the base station antenna includes an angle deviation and a position deviation.

[0080] When the synaesthesia base station antenna is installed, there are objective deviations between the actual latitude and longitude, hanging height, pitch angle, horizontal angle, roll angle and the design values ​​of the antenna. Although the synaesthesia base station has been electronically calibrated and signal calibrated before installation, the deviations in position and angle during the installation of these antennas will still cause errors in the estimation of the detection target during the synaesthesia base station detection and perception.

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

[0082] The first position information reported by the positioning device can be considered as accurate positioning information. Due to the deviation of the base station antenna, the second position information measured by the base station is inaccurate. Therefore, the second position information deviates from the first position information. The deviation of the base station antenna can be solved based on the first position information reported by the positioning device and the second position 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 position information and the second position information. The correspondence between the deviation of the base station antenna and the deviation of the first position information and the second position information can be summarized. Based on the correspondence, the deviation of the base station antenna can be deduced according to the deviation of the first position information and the second position information.

[0084] S103: Determine a parameter calibration value of the base station antenna based on the deviation.

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

[0086] The parameter calibration value of the base station antenna is determined according to the deviation. The parameter calibration value is first calculated according to the deviation, and then the antenna parameter calibration is implemented according to the parameter calibration value. The antenna parameter calibration can be performed manually or automatically according to the parameter calibration value by a control device on the antenna.

[0087] For example, α, β, and γ are the offset angles (calibration values) of the actual value and design value of the antenna in the pitch angle, roll angle, and horizontal angle obtained by the calibration method. Based on these offset angles, the calibration of the horizontal angle, pitch angle, and roll angle of the base station antenna can be completed.

[0088] For example, for the calibration of the antenna latitude and longitude, a translation vector t is required. t is the deviation of the calibrated antenna position relative to the design value. If the design position of the base station antenna deployment is o = (x0, y0, z0), then the calibrated antenna position is o' = (x0 + Δx, y0 + Δy, z0 + Δz).

[0089] The embodiment of the present invention obtains the first position information reported by the positioning device and the second position information reported by the base station, wherein the first position information includes multiple coordinate information of the positioning device itself reported during movement, and the second position information includes multiple coordinate information of the positioning device detected by the base station, and the positioning device is set on a mobile carrier. Based on the first position information and the second position information, the deviation of the base station antenna is determined, and the deviation of the base station antenna includes an angle deviation and a position deviation. Based on the deviation, the parameters of the base station antenna are calibrated. The embodiment of the present invention uses a mobile positioning device, combined with the perception capability of the base station to assist in determining the parameter calibration value of the base station antenna. The parameter calibration value has high accuracy, so that the calibration accuracy can meet business needs. And without manual participation, the degree of automation of the base station antenna engineering parameter calibration can be improved, saving labor costs and improving calibration efficiency.

[0090] In one embodiment, the angle deviation of the base station antenna includes a rotation matrix (RotationMatrix, RM) of the base station antenna, and the position deviation of the base station antenna includes a translation vector of the antenna base station.

[0091] The measurement error caused by 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, and 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 the positioning device as an example, after the calibration and solution platform obtains the trajectory set of the drone flight data and the synaesthesia base station detection data, it needs to perform fusion processing based on the timestamp to obtain the drone reference position and detection position at the same time. Since the frequency of drone flight data reporting is greater than the frequency of synaesthesia base station reporting, the number of trajectory points reported by the drone on the same route will be greater than the number of trajectories reported by the synaesthesia base station, so the drone flight data can be fused based on the detection data reported by the synaesthesia base station.

[0095] In the fusion process, the trajectory point set P reported by the synaesthesia base station is traversed b_train , and obtain the fused trajectory point set is the fused position information, where That is, the fused timestamp is based on the timestamp of the trajectory points reported by the synaesthesia base station, and the number of trajectory points N is the same as the number of trajectory points reported by the synaesthesia base station. The corresponding time after fusion The detection position and reference position (data pair) of the calibrated drone are represented by their deviation from the detection position and reference position.

[0096] exist time, corresponding From P b_train of Get it at all times. It is not possible to directly u_train Query to obtain, need to get from P u_train Find the The latest moment Its corresponding position Based on, add position offset estimation The reference position of the drone at that moment.

[0097] in for and The time difference, for UAV flight speed v j The component on the X-axis, for UAV flight speed v j The component on the Y axis, for UAV flight speed v j The component on the Z axis, v j Reporting from drones It can be obtained by querying the flight status data at the moment.

[0098] Since there are measurement errors in the synaesthesia detection data, and even false alarm data or other interference data reported to the calibration and solution platform, it is necessary to remove abnormal points before calibration and solution. Traverse the entire fused data point set (fused position information) P c_train , calculate each The distance between the reference position and the detection position at any time like Exceeding the threshold δ d ,Right now This fusion calibration point is considered to be an outlier. c_train Delete, otherwise it is considered to be a normal value and retained in the data set.

[0099] This embodiment associates the UAV flight data and the telepathic base station detection data through timestamps, and calibrates and solves the associated data based on a unified coordinate system. Real-time reported data can improve the timeliness of calibration. According to the usage scenario, non-real-time reporting and offline data fusion processing can also complete the calibration work. The screening of abnormal data during data association and the estimated compensation of position when the timestamps are not aligned are also crucial for calibration and solving, affecting the performance and accuracy of calibration.

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

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

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

[0103] Since the accuracy of the detection and perception of the telemetry base station will fluctuate with the quality of the perceived signal, for example, when the signal-to-noise ratio decreases, the credibility of the detection result will decrease. To further improve the calibration accuracy, the (fused location information) P c_train Different weights are applied to each calibration point in the process, thereby increasing the weight of the credible observations on the calibration or reducing the weight of the uncredible observations on the calibration correction.

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

[0105] Distance from calibration point to base station (m) 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 and 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 Noise Ratio and Weight Reference Mapping Table

[0109] In the embodiment of the present application, the weight w of each check point is generated by estimating the signal-to-noise ratio. i , and finally expand the fusion trajectory point dataset P c_train Elements are P c_train Updated to:

[0110]

[0111] This embodiment assigns weights according to signal quality to facilitate more accurate estimation of calibration parameters. Assigning different weights to each sampling point in the data set according to signal quality also has a significant impact on 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.

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

[0113] Determine, based on the fused position information and the weight of each data pair in the fused position information, a first weighted centroid of the first position information and a second weighted centroid of the second position information in the fused position information;

[0114] Determine a rotation matrix and a translation vector of the base station antenna based on the first weighted centroid, the second weighted centroid and the fused position information;

[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, a parameter calibration value of the base station antenna is determined.

[0117] The above embodiment obtains a set of fused trajectory points, each of which contains the time Drone detection location and reference position This embodiment uses these data to perform calibration solutions.

[0118] The measurement error caused by the antenna position and angle deviation can be decomposed into the error in the antenna rotation direction and position translation direction. c_train The detection position vector of the trajectory point M is p i , The corresponding drone reference position vector is q i , For the same trajectory point, the detection vector p i and the reference vector q i The length is the same, there is only a deviation in the rotation angle θ and the translation vector t. According to the rigid body rotation theory, the calibration drone measurement vector p can be obtained i and the true vector q i The relationship is:

[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 expressed as:

[0123]

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

[0125]

[0126] Fusion trajectory point set P c_train The set of detection vectors of the synaesthesia base station can be represented by matrix A:

[0127]

[0128] Then the matrices B and A satisfy B=R*A+t, that is, the 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 equation system B = R*A+t, the weighted least squares method can be used to estimate the rotation matrix R and the translation vector t, that is, to obtain R and t.

[0130] After derivation, in order to estimate R and t, it can be transformed into a solution through the weighted covariance matrix singular value decomposition of two data sets B and A. The steps are:

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

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

[0133]

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

[0135]

[0136] (2) Solving the translation vector

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

[0138]

[0139] By taking the derivative of F(t), we can get the optimal solution for t.

[0140]

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

[0142] (3) Solving the rotation matrix

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

[0144]

[0145] Let x i =p i -Centroid p ,y i =q i -Centroid q , then the optimal rotation matrix R becomes

[0146]

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

[0148]

[0149] Then solving the rotation matrix R can be transformed into solving the trace tr(WY T RX) is the rotation matrix R.

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

[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 RU is also an orthogonal matrix. Only when M is the unit matrix can the maximum trace be obtained, which satisfies The rotation matrix R corresponds to M as the unit matrix, then I = M = V T RU, then R=VU T , where U and V are matrices S = XWY T The singular value decomposition matrix of .

[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 detection 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 above rotation matrix and translation vector are the angle deviation and position deviation of the base station antenna. The base station parameter calibration value is calculated based on the rotation matrix and translation vector. The base station parameter calibration value includes the offset angle and position deviation value.

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

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

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

[0164] The offset angle of the base station antenna includes the offset angles 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 relative to the design value.

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

[0166]

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

[0168]

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

[0170]

[0171] The rotation matrix R can be obtained according to 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, γ, the expression becomes:

[0174]

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

[0176]

[0177] Get the values ​​of α, β, and γ:

[0178]

[0179] α, β, and γ are the offset angles of the actual value and design value of the antenna in the pitch angle, roll angle, and horizontal angle obtained by the calibration method. According to these offset angles, the calibration of the horizontal angle, pitch angle, and roll angle of the telesensory base station antenna can be completed.

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

[0181] This embodiment uses the rotation matrix and translation vector to associate the drone reference position and the detection position during calibration and solution, combines multiple measurement trajectories, and uses the weighted least squares method to solve the rotation matrix and translation vector, and finally obtains the horizontal angle, pitch angle, and roll angle of the telesensory base station antenna relative to the design value and the latitude, longitude, and altitude calibration values ​​of the antenna.

[0182] In one embodiment, before acquiring 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 moving 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 calibrated based on the parameter calibration value.

[0184] Since different coverage scenarios have different environments and there are multiple options for route planning itself, and this embodiment does not rely on a specific route, this embodiment does not fix a specific route, but only provides route planning guidelines to meet parameter calibration requirements.

[0185] In one embodiment, a drone is used as a detection target to calibrate the antenna parameters of the synaesthesia base station. To improve the accuracy of the calibration, the typical signal coverage area of ​​the synaesthesia base station is traversed as much as possible when collecting detection perception data, such as covering the central area, covering the boundary area (up, down, left, and right), and covering the distant area.

[0186] Since this embodiment uses the deviation of base station detection data and target reference positioning data to train and obtain the calibration value of antenna engineering parameters, the flight route of the drone can be divided into a training route (first trajectory) and a verification route (second trajectory) to ensure the consistency and ubiquity of the calculated engineering parameters. During verification, the training route is used to verify the consistency of parameter estimation, and other routes are used to verify the ubiquity of parameter estimation.

[0187] As an embodiment, the training routes can be planned at different altitudes within the planned coverage of the telepathic base station. For example, the distant direction routes and the longitudinal routes perpendicular to the normal of the antenna are planned at altitudes of 80 meters, 100 meters, 120 meters, and 150 meters. Three routes are planned in the distant direction, flying away from and close to the telepathic base station in the planned coverage area of ​​the telepathic base station. One of the routes is in the normal horizontal angle direction, and the other two routes are at angles on both sides of this route, which are the planned horizontal coverage angles. Three longitudinal routes are planned, and can be planned according to the distance from the telepathic base station. For example, it can be planned to fly 200 meters, 400 meters, and 600 meters away from the telepathic base station in the direction of the normal of the antenna. The route planning for each altitude can refer to Figure 3 .

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

[0189] Verification routes are also planned at different altitudes. The verification route mainly verifies the omnipresence of calibration parameters and needs to reflect the difference from the training route. Different lateral and longitudinal routes from the training route can be planned at each altitude, or routes of other arbitrary shapes. No specific regulations are given here.

[0190] In one embodiment, after the trajectory is planned, time synchronization between the positioning device and the base station is performed.

[0191] After planning the route, the drone will fly manually or by marking the route. Before taking off, the drone and the Tonggan base station need to be synchronized. The clock synchronization can be completed by network synchronization or GPS or Beidou timing.

[0192] After the drone completes time synchronization, it is necessary to confirm that the high-precision positioning module works normally and can report the flight situation data with timestamps to the calibration and solution platform in real time. After the telepathic base station completes time synchronization, it is necessary to confirm that the detection and perception function is normal and can report the detection and perception data with timestamps to the calibration and solution platform.

[0193] Before takeoff, you need to set the data reporting frequency according to the aircraft capabilities and the base station detection reporting capabilities. Since the frequency of the flight situation data reported by the drone can reach more than 10Hz, and the frequency of the detection results reported by the telepathic base station is generally no more than 3Hz, the data reporting frequencies of the two are different and need to be aligned in the calibration solution platform. Try to set the drone data reporting frequency f uav_data Greater than the base station detection result reporting frequency f bs_data .

[0194] After completing these preparations, execute the flight 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] Acquire third position information reported by the positioning device and fourth position information reported by the base station; the third position information includes a plurality of coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth position information includes a plurality of coordinate information reported by the base station when the positioning device is detected to move along the second trajectory;

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

[0198] Taking the UAV as an example, the UAV trajectory data point set (third position information) collected on the verification route is P u_test ,

[0199]

[0200] K is the number of trajectory points of the UAV reporting situation data, t ui is the timestamp of the reference position trajectory point i of the UAV, is the coordinate value of the trajectory point i in the world coordinate system.

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

[0202]

[0203] L is the number of trajectory points of the detection drone data reported by the synaesthesia base station, t' bi is the timestamp of trajectory point i detected by the synaesthesia base station, (x' bi ,y' bi ,z' bi ) is the coordinate value of the trajectory point i in the world coordinate system.

[0204] In order to improve the performance and accuracy of calibration, a training data set and a validation data set are used in the calibration method. The training data set is used to calculate the calibration parameters, and the validation data set is used to test the accuracy of the calibration parameters. This key point can improve the performance and accuracy of calibration and improve the practicality of the calibration method engineering implementation.

[0205] Collect the position measurement values ​​of the UAV at the synaesthesia base station and the reference position values ​​of the UAV at each sampling time on the verification route. For the data collected on the test route, the timestamp 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 detection location of the synaesthesia base station is In order to verify the accuracy of the calibration solution, the detection position of the telemetry base station is calibrated and corrected, and then compared with the reference position to obtain the calibration error of the positioning result.

[0208] The homogeneous matrix can be used to calibrate the position of the drone detected by the telepathic base station. The homogeneous matrix P is based on the rotation matrix R and the translation vector t calculated by S104 verification, and its composition is:

[0209]

[0210] After calibration, the calibration drone position vector detected by the synaesthesia base station becomes The relationship between the two is

[0211] Let the matrix A be P c_test Point-based synaesthesia base station detection location The collection matrix of P c_test UAV reference position The set matrix of A, B' is the corrected position of A Collection matrix.

[0212]

[0213] Matrix A is calibrated by homogeneous matrix to get matrix B'.

[0214]

[0215] Optionally, the calibrated matrix may also 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 means that the calibration error is large and recalibration is required.

[0219] After the calibration is completed, the antenna engineering parameter calibration of the synastry base station is completed, and the calibration quantity of the antenna engineering parameters is output, including the horizontal angle, pitch angle, roll angle, longitude and latitude, and antenna hanging height.

[0220] Figure 5 A base station antenna parameter calibration flow chart provided by an embodiment of the present invention includes:

[0221] S501, route planning.

[0222] The flight routes of UAVs can be divided into training routes and verification routes to ensure the consistency and ubiquity of the calculated parameters. During verification, the training routes are used to verify the consistency of parameter estimation, and other routes are used to verify the ubiquity of parameter estimation.

[0223] S502, flight preparation.

[0224] After planning the route, the drone will fly manually or by marking the route. Before taking off, the drone and the Tonggan base station need to be synchronized. The clock synchronization can be completed by network synchronization or GPS or Beidou timing.

[0225] S503, data collection and reporting.

[0226] The calibration and solution platform collects UAV flight data and telepathic base station detection data, and the two data are associated through timestamps. The position reported by the UAV and the detected UAV position information reported by the telepathic base station are both based on the world coordinate system.

[0227] S504, calibration solution.

[0228] According to the collected UAV flight data and synaesthesia base station detection data, the rotation matrix and translation vector are calculated, and the calibration parameters of the synaesthesia base station are obtained through the rotation matrix and translation vector.

[0229] S505, verify calibration.

[0230] In S504, the position and angle calibration of the synaesthesia base station antenna is completed based on the data collected from the training route. This step uses the verification route data collected in S503 to perform verification based on the calibration parameters solved in S504 to evaluate the accuracy of the verification parameters.

[0231] S506, calibration ends.

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

[0233] The embodiment of the present application uses drone flight, combined with the detection and perception capabilities of the synaesthesia base station, to efficiently, quickly, accurately, and low-costly complete the antenna engineering parameter calibration. In addition to low-altitude scenes, this proposal can also be expanded to synaesthesia scenes such as waterways and sea surfaces.

[0234] The embodiment of the present application reports the flight information to the calibration and solution platform in real time during the flight of the drone. At the same time, the telepathic base station detects and locates the position of the drone by transmitting wireless signals, obtains the measured position of the drone, and reports it to the calibration and solution platform; then the calibration and solution platform calibrates the engineering parameters of the telepathic base station antenna based on the deviation of the reference position information reported by the drone and the position information of the drone detected by the telepathic base station. During the calibration and solution, the rotation matrix and translation vector are used to associate the reference position and detection position of the drone, and the weighted least squares method is used to solve the rotation matrix and translation vector in combination with multiple measurement trajectories, and finally the horizontal angle, pitch angle, and roll angle of the telepathic base station antenna relative to the design value and the latitude and longitude and height calibration values ​​of the antenna are obtained. Different weights are given to the calibration data of different channel quality conditions during the calibration and solution, which can further improve the calibration accuracy and calibration efficiency. Compared with the traditional base station antenna engineering parameter calibration method, the calibration of angles and longitude and latitude can be completed at one time, and a high calibration accuracy can be guaranteed.

[0235] The embodiment of the present application associates the UAV flight data and the telepathic base station detection data through timestamps, and calibrates and solves the associated data based on a unified coordinate system. The data reported in real time can improve the timeliness of the calibration. According to the usage scenario, the non-real-time reporting and the offline data fusion processing can also complete the calibration work. The screening of abnormal data during data association and the estimated compensation of the position when the timestamps are not aligned are also crucial for the calibration solution, which affects the performance and accuracy of the calibration. Assigning different weights to each sampling point in the data set according to the signal quality also has a great influence on 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.

[0236] In order to improve the performance and accuracy of calibration, the embodiment of the present application uses a training data set and a validation data set in the calibration method. The training data set is used to calculate the calibration parameters, and the validation data set is used to test the accuracy of the calibration parameters. This key point can improve the performance and accuracy of calibration and improve the practicality of the calibration method engineering implementation.

[0237] After completing the calibration of the telepathic base station parameters, the embodiment of the present application corrects the detection results reported by the telepathic base station to obtain better detection perception performance and accuracy. The embodiment of the present application uses the perception capability of the telepathic base station to assist in the calibration of antenna parameters, and while using the perception to enable the improvement of communication capabilities, it also improves the performance of the perception service.

[0238] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean 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 embodiment of the present invention.

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

[0240] It should be noted that the technical solutions described in the embodiments of the present invention can be arbitrarily combined 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] The present application also provides a base station antenna parameter calibration device, which corresponds to the above-mentioned base station antenna parameter calibration method, and each step in the above-mentioned base station antenna parameter calibration method embodiment is also fully applicable to the present device embodiment. The device includes:

[0243] an acquisition module, configured to acquire first location information reported by a positioning device and second location information reported by a base station; wherein the first location information includes a plurality of coordinate information of the positioning device itself reported by the positioning device during movement, and the second location information includes a plurality of coordinate information of the positioning device detected by the base station; the positioning device is disposed on a mobile vehicle;

[0244] A determination module, configured to determine a 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 an angle deviation and a position deviation;

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

[0246] In one embodiment, the angle deviation of the base station antenna includes a rotation matrix of the base station antenna, and the position deviation of the base station antenna includes a 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 apparatus further includes:

[0248] A 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 comprises:

[0250] A signal-to-noise ratio acquisition module, used to acquire a signal-to-noise ratio when the base station reports the second location information;

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

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

[0253] A weighted centroid determination module, configured to determine a first weighted centroid of the first position information and a second weighted centroid of the second position information in the fused position information based on the fused position information and the weight of each data pair in the fused position information;

[0254] A rotation matrix and translation vector determination module, configured to determine a rotation matrix and a 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, a parameter calibration value of the base station antenna is determined.

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

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

[0259] Based on the translation vector, a 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 comprises:

[0261] A planning module is used to plan the moving 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 calibrated based on the parameter calibration value.

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

[0263] a position acquisition module, configured to acquire third position information reported by the positioning device and fourth position information reported by the base station; the third position information includes a plurality of coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth position information includes a plurality of coordinate information reported by the base station when the positioning device is detected to move along the second trajectory;

[0264] A verification module is used to verify the antenna parameters of the base station calibrated based on the parameter calibration value based on the third location information and the fourth location information.

[0265] In actual application, the determination module and the acquisition module can be implemented by a processor in an electronic device, such as a central processing unit (CPU), a parameter calibrator (DSP) of a digital base station antenna, a microcontroller unit (MCU) or a programmable gate array (FPGA).

[0266] It should be noted that: the parameter calibration device for the base station antenna provided in the above embodiment only uses the division of the above modules as an example when performing parameter calibration of the base station antenna. In actual applications, the above processing can be assigned to different modules as needed, that is, the internal structure of the device is divided into different modules to complete all or part of the processing described above. In addition, the parameter calibration device for the base station antenna provided in the above embodiment and the parameter calibration method embodiment of the base station antenna belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0267] The parameter calibration device of the base station antenna can be in the form of an image file. After the image file is executed, it can be run in the form of a container or a virtual machine to implement the parameter calibration method of the base station antenna described in this application. Of course, it is not limited to the image file form. As long as some software forms that can implement the parameter calibration method of the base station antenna described in this application are within the protection scope of this application.

[0268] Based on the hardware implementation of the above program modules and in order to implement the method of the embodiment of the present application, the embodiment of the present application also provides an electronic device. Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the electronic device includes:

[0269] Communication interface 601, capable of exchanging information with other devices such as network devices;

[0270] The processor 602 is connected to the communication interface 601 to implement information exchange with other devices and is used to execute the method provided by one or more technical solutions when running a computer program. The computer program is stored in the memory 603.

[0271] Of course, in actual application, the various components in the electronic device are coupled together through the bus system 604. It can be understood that the bus system 604 is used to realize the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 6 Various buses are labeled as bus system 604 .

[0272] The memory 603 in the embodiment of the present application 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 can be understood that the memory 603 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a 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), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM). The memory described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memory.

[0274] The method disclosed in the above embodiment of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the program in the memory and completes the steps of the above method in combination with its hardware.

[0275] Optionally, when the processor 602 executes the program, it implements the corresponding processes implemented by the electronic device in each method of the embodiments of the present application, which will not be described in detail here for the sake of brevity.

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

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

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

[0279] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0280] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0281] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

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

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

[0284] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0285] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for calibrating parameters of a base station antenna, characterized in that: The method comprises: Acquire first location information reported by a positioning device and second location information reported by a 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 set on a mobile vehicle; 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 an angle deviation and a position deviation; A 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 angle deviation of the base station antenna includes the rotation matrix of the base station antenna, and the position 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 comprises: Obtaining a signal-to-noise ratio when the base station reports the second location information; The weight of each data pair in the fused position 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 position information based on the signal-to-noise ratio, the method further includes: Determine, based on the fused position information and the weight of each data pair in the fused position information, a first weighted centroid of the first position information and a second weighted centroid of the second position information in the fused position information; Determine a rotation matrix and a translation vector of the base station antenna based on the first weighted centroid, the second weighted centroid and the fused position information; 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, a parameter calibration value of the base station antenna is determined.

6. The method according to claim 5, characterized in that: The determining, based on the rotation matrix and the translation vector, a parameter calibration value of the base station antenna comprises: Based on the rotation matrix, determining the offset angle of the base station antenna; Based on the translation vector, a 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 acquiring the first location information reported by the positioning device and the second location information reported by the base station, the method further includes: The moving 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 calibrated based on the parameter calibration value.

8. The method according to claim 7, characterized in that: After determining the parameter calibration value of the base station antenna, the method further includes: Acquire third position information reported by the positioning device and fourth position information reported by the base station; the third position information includes a plurality of coordinate information reported by the positioning device when the vehicle moves along the second trajectory, and the fourth position information includes a plurality of coordinate information reported by the base station when the positioning device is detected to move along the second trajectory; Based on the third location information and the fourth location information, the antenna parameters of the base station calibrated based on the parameter calibration value are verified.

9. A parameter calibration device for a base station antenna, characterized in that: The device comprises: an acquisition module, configured to acquire first location information reported by a positioning device and second location information reported by a base station; wherein the first location information includes a plurality of coordinate information of the positioning device itself reported by the positioning device during movement, and the second location information includes a plurality of coordinate information of the positioning device detected by the base station; the positioning device is disposed on a mobile vehicle; A determination module, configured to determine a 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 an angle deviation and a position deviation; A calibration module is used to determine a parameter calibration value of the base station antenna based on the deviation.

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

11. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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

Images