Positioning method, apparatus, device, medium, and program product

Abstract

The application provides a positioning method, device, equipment, medium and program product, relates to the technical field of communication and sensing integration, and is used for improving sensing accuracy and sensing reliability. The specific technical scheme is as follows: based on the sensing accuracy of each coordinate axis in the coordinate system corresponding to a sensing base station at an i-th time point, the absolute weight of an i-th data source on each coordinate axis is determined; wherein the i-th data source includes: positioning information of a sensing target obtained by a plurality of devices at the i-th time point and sensing data of the sensing target obtained by the sensing base station; i is a positive integer; based on the absolute weight of the i-th data source on each coordinate axis, the relative weight of the i-th data source on each coordinate axis is determined; based on the relative weight of the i-th data source on each coordinate axis, the i-th data source is fused to obtain the position information of the sensing target at the i-th time point. The application is applied to a wireless positioning scene of a sensing target.

Description

Technical Field

[0001] This application relates to the field of integrated communication and sensing technology, and in particular to a positioning method, device, equipment, medium, and program product. Background Technology

[0002] Integrated Sensing and Communication (ISAC) technology enables communication networks to also have sensing capabilities by reusing the same spectrum and hardware resources. It can extract information such as the distance, speed and angle of a target from wireless signals, thereby supporting emerging services such as high-precision positioning, environmental reconstruction and sensing-assisted communication, and has become an important evolution direction in the field of wireless communication.

[0003] In ISAC-based positioning practices, sensing base stations typically rely on their own transmitted and received wireless signals to perform parameter estimation and location calculation. They then use a pre-defined signal processing link to convert the original waveform into target coordinates and output continuous frames of sensing results to construct motion trajectories.

[0004] However, this method is limited by local observation perspective and environmental obstruction, making it difficult to cope with complex propagation conditions such as weather disturbances, obstacle occlusion, and low signal-to-noise ratio at the cell edge. This leads to the amplification of measurement errors during the solution process, and the lack of complementary correction of cross-modal information results in poor sensing accuracy and reliability. Summary of the Invention

[0005] This application provides a positioning method, apparatus, device, medium, and program product for making the data source fusion results closer to the true value, thereby improving the sensing accuracy and sensing reliability.

[0006] In a first aspect, embodiments of this application provide a positioning method, which includes: determining the absolute weight of an i-th data source on each coordinate axis based on the sensing accuracy of a sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point; wherein the i-th data source includes: positioning information of a sensing target obtained by multiple devices and sensing data of the sensing target obtained by the sensing base station on the i-th time point; i is a positive integer; determining the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis; and performing fusion processing on the i-th data source based on the relative weight of the i-th data source on each coordinate axis to obtain the position information of the sensing target on the i-th time point.

[0007] The technical solution provided in this application offers at least the following advantages: At different points in time, based on the sensing accuracy of the sensing base station on each coordinate axis, the weights of the positioning information of the sensing target obtained by multiple devices and the sensing data of the sensing target obtained by the sensing base station on each coordinate axis can be determined. Then, based on these weights, the data are fused to obtain the location information of the sensing target. This allows the weights to be determined based on the network status of different data sources at different points in time, making the fusion result closer to the true value. This eliminates the limitation of the local observation perspective and environmental obstruction of a single base station, improving sensing accuracy and reliability.

[0008] One possible implementation is that the aforementioned location information includes at least one of the following: Time Difference of Arrival (TDOA) location data, Remote Identification (RID) data of unmanned aerial vehicles, location information obtained by a camera, and location information obtained by radar.

[0009] Another possible implementation, which is to determine the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis, includes: determining the first absolute weight based on the absolute weight of the i-th data source on each coordinate axis and the absolute weight of the ij-th data source on each coordinate axis; where j is a positive integer less than i; and determining the relative weight of the i-th data source on each coordinate axis based on the first absolute weight.

[0010] Another possible implementation method, before determining the absolute weight of the i-th data source on each coordinate axis based on the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point, further includes: determining the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point based on the azimuth angle, elevation angle, distance accuracy, azimuth angle accuracy, elevation angle accuracy, and distance between the sensing target and the sensing base station.

[0011] Another possible implementation method includes: acquiring multiple data sources; performing format conversion and time alignment on the multiple data sources to obtain multiple processed data sources; wherein the multiple processed data sources include the i-th data source.

[0012] Secondly, embodiments of this application provide a positioning device, including a determining module and a processing module. The determining module is used to determine the absolute weight of an i-th data source on each coordinate axis based on the sensing accuracy of each coordinate axis of the sensing base station at the i-th time point; wherein the i-th data source includes: positioning information of the sensing target obtained by multiple devices and sensing data of the sensing target obtained by the sensing base station at the i-th time point; i is a positive integer; the determining module is also used to determine the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis; the processing module is used to perform fusion processing on the i-th data source based on the relative weight of the i-th data source on each coordinate axis to obtain the position information of the sensing target at the i-th time point.

[0013] One possible implementation is that the aforementioned location information includes at least one of the following: base station TDOA location data, UAV RID data, location information obtained by camera, and location information obtained by radar.

[0014] Another possible implementation is that the aforementioned determining module is specifically used to determine the first absolute weight based on the absolute weight of the i-th data source on each coordinate axis and the absolute weight of the ij-th data source on each coordinate axis; where j is a positive integer less than i; and based on the first absolute weight, to determine the relative weight of the i-th data source on each coordinate axis.

[0015] In another possible implementation, the aforementioned determining module is further used to determine the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point, based on the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point, before determining the absolute weight of the i-th data source on each coordinate axis, based on the azimuth angle, elevation angle, distance accuracy, azimuth angle accuracy, elevation angle accuracy, and distance between the sensing target and the sensing base station.

[0016] In another possible implementation, the positioning device further includes an acquisition module; the acquisition module is used to acquire multiple data sources; the processing module is also used to perform format conversion and time alignment processing on the multiple data sources to obtain processed multiple data sources.

[0017] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory stores a program or instructions executable on the processor, wherein the program or instructions, when executed by the processor, implement the method of the first aspect described above.

[0018] Fourthly, this application provides a readable storage medium on which a program or instructions are stored, which, when executed by a computer, implement the method of the first aspect described above.

[0019] Fifthly, this application provides a computer program product stored in a storage medium, which, when executed by a computer, implements the method described in the first aspect.

[0020] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0021] The beneficial effects of the second to sixth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0022] Figure 1 A schematic diagram of a network architecture for a positioning method application provided in an embodiment of this application;

[0023] Figure 2 A flowchart illustrating a positioning method provided in an embodiment of this application;

[0024] Figure 3 A flowchart illustrating a positioning method provided in an embodiment of this application;

[0025] Figure 4 A flowchart illustrating a positioning method provided in an embodiment of this application;

[0026] Figure 5 A flowchart illustrating another positioning method provided in an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of the structure of a positioning device provided in an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0030] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0031] The terms "at least one," "at least one of," etc., used in the specification and claims of this application refer to any one, any two, or a combination of two or more of the included items. For example, at least one of a, b, and c can mean: "a," "b," "c," "a and b," "a and c," "b and c," and "a, b, and c," where a, b, and c can be single or multiple. Similarly, "at least two" refers to two or more items, and its meaning is similar to that of "at least one."

[0032] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0033] The positioning method, apparatus, device, medium, and program product provided in this application embodiment can be applied in wireless positioning scenarios.

[0034] Integrated Sensing and Communication (ISAC) technology reuses the same spectrum and hardware resources, enabling communication networks to also function as sensors. The entire communication network can act as a giant sensor, extracting information such as the distance, speed, and angle of targets from wireless signals. This supports emerging services such as high-precision positioning, gesture capture, motion recognition, detection and tracking of passive objects, imaging and environmental reconstruction, and perception-assisted communication. On the other hand, the high-precision positioning, imaging, and environmental reconstruction capabilities provided by sensing can help improve communication performance, such as more accurate beamforming, faster beam failure recovery, and lower overhead for tracking terminal channel state information, thus realizing "sensing-assisted communication" and becoming an important evolutionary direction in the field of wireless communication.

[0035] In ISAC-based positioning practices, sensing base stations typically rely on their own transmitted and received wireless signals to perform parameter estimation and location calculation. They then use a pre-defined signal processing link to convert the original waveform into target coordinates and output continuous frames of sensing results to construct motion trajectories.

[0036] However, this method is limited by local observation perspective and environmental obstruction, making it difficult to cope with complex propagation conditions such as weather disturbances, obstacle occlusion, and low signal-to-noise ratio at cell edges. It may also have coverage blind spots, which amplify measurement errors during the calculation process. Furthermore, it lacks complementary correction of cross-modal information, resulting in poor sensing accuracy and reliability.

[0037] To address the aforementioned technical problems, embodiments of this application provide a positioning method, apparatus, device, medium, and program product. At different points in time, based on the sensing accuracy of the sensing base station on each coordinate axis, the positioning information of the sensing target obtained by multiple devices and the weights of the sensing data of the sensing target obtained by the sensing base station on each coordinate axis can be determined. Then, based on these weights, these data are fused to obtain the location information of the sensing target. This allows the weights to be determined based on the network status of different data sources at different times, making the fusion result closer to the true value. Thus, it is not limited by the local observation perspective of a single base station or environmental obstructions, improving sensing accuracy and reliability.

[0038] The positioning methods, apparatus, devices, media, and program products provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0039] Figure 1 The network architecture for a positioning method provided in an embodiment of this application is illustrated. For example... Figure 1 As shown, the network architecture includes a positioning device 101 and a terminal device 102. The positioning device 101 and the terminal device 102 are interconnected.

[0040] In some embodiments, the positioning device 101 may be a server, a computer, or a processor or processing unit within a server or computer. The server may be a single server or a server cluster comprising multiple servers. It should be noted that the specific device form of the positioning device 101 is not limited in the embodiments of this application. Figure 1 The example shown is a single server using the positioning device 101.

[0041] In some embodiments, the terminal device may be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, personal computer (PC), ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc., and the embodiments of this application do not specifically limit it. Figure 1 The example shown is a mobile phone, with terminal device 102 as an example.

[0042] In some embodiments, the positioning device 101 determines the absolute weight of the i-th data source on each coordinate axis based on the sensing accuracy of each coordinate axis in the coordinate system corresponding to the i-th time point; wherein, the i-th data source includes: the positioning information of the sensing target obtained by multiple devices (e.g., including terminal device 102) and the sensing data of the sensing target obtained by the sensing base station at the i-th time point; i is a positive integer; then, the positioning device 101 determines the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis; finally, the positioning device 101 performs fusion processing on the i-th data source based on the relative weight of the i-th data source on each coordinate axis to obtain the position information of the sensing target at the i-th time point.

[0043] It should be noted that the network architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As network architectures evolve, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0044] See Figure 2 This is a flowchart illustrating a positioning method provided in an embodiment of this application. Figure 2 As shown, the positioning method provided in this application embodiment can be implemented by the above-mentioned positioning device, specifically including the following steps 201 to 203.

[0045] Step 201: Based on the sensing accuracy of each coordinate axis of the sensing base station in the coordinate system corresponding to the i-th time point, the positioning device determines the absolute weight of the i-th data source on each coordinate axis.

[0046] In some embodiments, the i-th data source includes: at the i-th time point, the positioning information of the sensing target obtained by multiple devices and the sensing data of the sensing target obtained by the sensing base station; i is a positive integer.

[0047] In some embodiments, the i-th time point is any time point in the time interval from time (tT) to time t; where t and T are positive integers. The coordinate system can be such that t > T.

[0048] In some embodiments, the coordinate system described above may be a radar local rectangular coordinate system, a local tangent plane coordinate system, or a base station body coordinate system, etc.

[0049] For example, taking the above coordinate system as a local rectangular coordinate system of the radar, each of the above coordinate axes may include the X-axis, Y-axis and Z-axis under the local rectangular coordinate system of the radar.

[0050] In some embodiments, the aforementioned multiple devices may include devices such as drones, cameras, and radar.

[0051] In some embodiments, the sensing target can be a static object such as a wall or street lamp, a dynamic object such as a bicycle or car, or a small object such as a mobile phone or backpack.

[0052] In some embodiments, the absolute weights described above are used to characterize the credibility or importance of the i-th data source itself.

[0053] In some embodiments, the above-mentioned location information includes at least one of the following: base station TDOA location data, UAV RID data, location information obtained by camera, and location information obtained by radar.

[0054] In some embodiments, the aforementioned positioning information also includes sensing data from a sensing base station, i.e., all data that can obtain positioning information.

[0055] In some embodiments, the aforementioned base station TDOA positioning data includes data such as time difference of arrival, calculated coordinates (x, y, z), timestamp, positioning accuracy, and error ellipse.

[0056] In some embodiments, the aforementioned drone RID data includes drone ID, latitude and longitude, altitude, speed, heading, timestamp, operator location, and other data.

[0057] In some embodiments, the position information obtained by the camera includes pixel coordinates (u,v), world coordinates (x,y,z) after calibration transformation, target box center, depth, timestamp, and other information.

[0058] In some embodiments, the position information acquired by the radar includes polar coordinates (range R, azimuth Az, elevation E1), rectangular coordinates (x, y, z), Doppler velocity, timestamp, and other information.

[0059] In this way, by acquiring positioning information such as base station time difference of arrival (TDOA) positioning data, UAV remote identification (RID) data, camera location information, and radar location information, positioning without relying on a single data source makes the location information of the perceived target closer to the true value through the fusion of positioning information from multiple sources.

[0060] Step 202: The positioning device determines the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis.

[0061] In some embodiments, the relative weights described above are used to characterize the priority or contribution ratio of the i-th data source relative to other data sources.

[0062] In some embodiments, the absolute weights Wxi, Wyi, and Wzi of different data sources are updated at time T, and the relative weights Rxi, Ryi, and Rzi are calculated.

[0063] In some embodiments, combined with Figure 2 ,like Figure 3 As shown, step 202 above can be implemented through steps 202a and 202b.

[0064] Step 202a: The positioning device determines the first absolute weight based on the absolute weight of the i-th data source on each coordinate axis and the absolute weight of the ij-th data source on each coordinate axis.

[0065] Where j is a positive integer less than i.

[0066] In some embodiments, assuming j is 1, the ij-th data source refers to the previous data source of the ith data source.

[0067] In some embodiments, the above-mentioned formula for calculating the first absolute weight is as shown in formulas (1), (2) and (3) below:

[0068] Wxi=(1-k)×Wxit'+k×Wxi'; (1)

[0069] Wyi=(1-k)×Wyit'+k×Wyi'; (2)

[0070] Wzi=(1-k)×Wzit'+k×Wzi'; (3)

[0071] Where Wxi, Wyi, and Wzi are the first absolute weights of data source i on the x, y, and z axes, respectively; Wxit', Wyit', and Wzit' are the absolute weights of data source i on the x, y, and z axes calculated at the current time t; and Wxi', Wyi', and Wzi' are the absolute weights of the ij-th data source on the x, y, and z axes calculated at time (tT). k is the smoothing coefficient, which is a positive number less than or equal to 1.

[0072] Step 202b: The positioning device determines the relative weight of the i-th data source on each coordinate axis based on the first absolute weight.

[0073] In some embodiments, the relative weight Ri is calculated as shown in formulas (4), (5) and (6) below:

[0074] Rxi = Wxi / ∑Wxi; (4)

[0075] Ryi = Wyi / ∑Wyi; (5)

[0076] Rzi=Wzi / ∑Wzi ; (6)

[0077] In some embodiments, Wxi is the absolute weight of data source i along the x-axis, Wyi is the absolute weight of data source i along the y-axis, and Wzi is the absolute weight of data source i along the z-axis.

[0078] Specifically, (x1, y1, z1) represents the location information obtained by the sensing base station, Wx1 is the absolute weight of the sensing base station on the x-axis, Rx1 is the relative weight of the sensing base station on the x-axis, Wy1 is the absolute weight of the sensing base station on the y-axis, Ry1 is the relative weight of the sensing base station on the y-axis, Wz1 is the absolute weight of the sensing base station on the z-axis, and Rz1 is the relative weight of the sensing base station on the z-axis; (x2, y2, z2) represents the TDOA location information obtained by the base station, Wx2 is the absolute weight of the TDOA data on the x-axis, Rx2 is the relative weight of the TDOA data on the x-axis, Wy2 is the absolute weight of the TDOA data on the z-axis, and Rz1 is the relative weight of the TDOA data on the z-axis. The absolute weight of the y-axis for OA data, Ry2 is the relative weight of the y-axis for TDOA data, Wz2 is the absolute weight of the z-axis for TDOA data, and Rz2 is the relative weight of the z-axis for TDOA data; (x3, y3, z3) represents the positional information of the RID data, Wx3 is the absolute weight of the x-axis for RID data, Rx3 is the relative weight of the x-axis for RID data, Wy3 is the absolute weight of the y-axis for RID data, Ry3 is the relative weight of the y-axis for RID data, Wz3 is the absolute weight of the z-axis for RID data, and Rz3 is the relative weight of the z-axis for RID data.

[0079] In this way, by determining the absolute weight based on the network status of different data sources at different time points, and calculating the relative weight based on the absolute weight, without relying on fixed weight settings, the characteristics of different data sources can be maximized to achieve higher positioning accuracy.

[0080] Step 203: The positioning device performs fusion processing on the i-th data source based on the relative weights of the i-th data source on each coordinate axis to obtain the position information of the perceived target at the i-th time point.

[0081] For example, the fusion processing of the above data sources can be as follows: Assuming the current time is t, the location information (X, Y, Z) from time (tT) to time t is fused using the following formulas (7), (8), and (9) (T can be 640ms or 1s for example):

[0082] X = ∑xi × Rxi; (7)

[0083] Y = ∑yi × Ryi; (8)

[0084] Z = ∑zi × Rzi; (9)

[0085] Where (xi, yi, zi) represent the location information obtained from data source i. Rxi is the relative weight of data source i along the x-axis, Ryi is the relative weight of data source i along the y-axis, and Rzi is the relative weight of data source i along the z-axis.

[0086] In the positioning method provided in this application embodiment, at different time points, the positioning information of the sensing target obtained by multiple devices and the weights of the sensing data of the sensing target obtained by the sensing base station on each coordinate axis can be determined based on the sensing accuracy of the sensing base station on each coordinate axis. Then, based on these weights, the data are fused to obtain the location information of the sensing target. This allows the weights to be determined based on the network status of different data sources at different time points, making the fusion result closer to the true value. Thus, it is not limited by the local observation perspective of a single base station or environmental obstructions, improving sensing accuracy and reliability.

[0087] In some embodiments, combined with Figure 2 ,like Figure 4 As shown, prior to step 201 above, the positioning method provided in this application embodiment further includes the following step 301.

[0088] Step 301: The positioning device determines the sensing accuracy of each coordinate axis of the sensing base station in the coordinate system corresponding to the i-th time point based on the azimuth angle, elevation angle, distance accuracy, azimuth angle accuracy, elevation angle accuracy, and distance between the sensing target and the sensing base station.

[0089] In some embodiments, the azimuth angle refers to the angle (0°~360°) of the sensing target rotating clockwise in the horizontal plane relative to the north direction of the base station.

[0090] In some embodiments, the pitch angle refers to the angle at which the sensing target tilts upward relative to the horizontal plane of the base station.

[0091] In some embodiments, the aforementioned distance accuracy refers to how accurate the distance value of the perceived target measured by the base station is, such as ±0.5m, the smaller the value, the more accurate it is.

[0092] In some embodiments, the azimuth accuracy mentioned above refers to the measurement error of the azimuth angle, such as ±0.1°, the smaller the value, the more accurate the direction.

[0093] In some embodiments, the pitch angle accuracy mentioned above refers to the measurement error of the pitch angle, such as ±0.2°, the smaller the value, the more accurate the pitch angle.

[0094] For example, the methods for determining the sensing accuracy Dx1, Dy1 and Dz1 of the integrated sensing base station are shown in the following formulas (10), (11) and (12):

[0095] Dx1=cos 2 φcos 2 θDr+R 2 cos 2 φsin 2 θDθ+R 2 sin 2 φcos 2 θDφ; (10)

[0096] Dy1=cos 2 φsin 2 θDr+R 2 cos 2 φcos 2 θDθ+R 2 sin 2 φsin 2 θDφ; (11)

[0097] Dz1=sin 2 φDr+R 2 cos 2 φDφ; (12)

[0098] Where θ is the azimuth angle, φ is the elevation angle, and can be the average of the past time T for the current target. Dr is the range accuracy, R is the distance between the target and the base station, Dθ is the azimuth accuracy, and Dφ is the elevation accuracy.

[0099] Dr represents the distance precision, and its calculation formula is shown in formula (13) below:

[0100] Dr=C 2 / (8×B 2 (×SNR); (13)

[0101] Where C is 3 × 10⁸ m / s, B is the system bandwidth, and the signal-to-noise ratio (SNR) can be the average of past time T. R is the distance between the target object and the base station, which can also be the average of past time T.

[0102] Dθ is the azimuth accuracy, and its calculation formula is shown in the following formula (14):

[0103] Dθ=3λ 2 / [L×SNR×Nx×(Nx 2 -1)×2π 2 ×dx 2 ×cos 2 φcos 2 θ]; (14)

[0104] Where L is the number of coherent accumulations, SNR is the signal-to-noise ratio, Nx is the number of horizontal array elements, and dx is the horizontal array element spacing.

[0105] Dφ represents the pitch angle accuracy, and its calculation formula is shown in the following formula (15):

[0106] Dφ=3λ 2 / [L×SNR×Ny×(Ny 2 -1)×2π 2 ×dy 2 ×cos 2 φ]; (15)

[0107] Where Ny is the number of elements in the vertical direction, and dy is the spacing between elements in the vertical direction.

[0108] For example, the absolute weight of the i-th data source on each coordinate axis is determined based on the perception accuracy, and the calculation formula is shown in the following formulas (16), (17) and (18):

[0109] Wxit=(Axi×Faci) / Dxi; (16)

[0110] Wyit=(Ayi×Faci) / Dyi; (17)

[0111] Wzit=(Azi×Faci) / Dzi; (18)

[0112] If data source j has no positioning result at a certain moment, the corresponding absolute weight is reset to 0. Dxi is the perception accuracy of data source i on the x-axis, Dyi is the perception accuracy of data source i on the y-axis, and Dzi is the perception accuracy of data source i on the z-axis. Axi is the x-axis accuracy weight correction factor, Ayi is the y-axis accuracy weight correction factor, and Azi is the z-axis accuracy weight correction factor.

[0113] Specifically, Fac1 is determined using the following formula (19):

[0114] Fac1=2 / (1+e a×（E-Eth） ); (19)

[0115] Here, E measures the positioning accuracy of the sensing base station, calculated based on measured data: A drone flight test is conducted within the base station's coverage area to obtain real-time kinematic (RTK) data (true value data). After time alignment, the absolute value (in meters) of the position difference between the RTK data and the integrated sensing data at the same time point is calculated. The difference at the Cumulative Distribution Function (CDF) Xc is recorded as E (Xc can be 95% for example). The E value varies for different base stations. Eth represents the required positioning accuracy, set based on scenario requirements, for example, it can be set to 1 meter. 'a' is the steepness factor, which can be set to 10 for example.

[0116] In some embodiments, for a sensing base station with a large amount of test data, the relationship between the feature vector (x, y, z, SNR, φ, θ) and Dx1, Dy1, and Dz1 can be determined by machine learning, and then Dx1, Dy1, and Dz1 can be determined by inputting the feature vector (x, y, z, SNR, φ, θ).

[0117] In this way, by using multiple pieces of information such as angle, signal-to-noise ratio, and distance to jointly determine the perception accuracy, the impact of different network conditions, environmental interference (such as weather, obstacles, etc.), coverage blind spots, and signal-to-noise ratio within the cell area on the perception results is reduced, making the perception accuracy more accurate.

[0118] In some embodiments, combined with Figure 2 ,like Figure 5 As shown, prior to step 201 above, the positioning method provided in this application embodiment may further include the following steps 401 and 402.

[0119] Step 401: The positioning device acquires multiple data sources.

[0120] In some embodiments, the aforementioned multiple data sources include drones, cameras, and radar, etc.

[0121] In some embodiments, based on the coverage of different data sources and different devices, different data source devices covering the same area are grouped together, and data from different data sources within the same group are merged.

[0122] Step 402: The positioning device performs format conversion and time alignment processing on multiple data sources to obtain multiple processed data sources.

[0123] In some embodiments, the i-th data source is included among the multiple data sources processed as described above.

[0124] In some embodiments, the above-described format conversion and time alignment processing may include format conversion processing and time alignment processing.

[0125] In some embodiments, the above-described format conversion process refers to the coordinate system conversion of the geographic location, that is, converting the latitude and longitude information (longitude, latitude, and altitude) of each data source into a geocentric coordinate system (X, Y, Z). The conversion method is the same as existing methods (not an innovative point) and will not be described in detail here.

[0126] In some embodiments, the above-mentioned time alignment processing refers to using the time information of a certain data source as a reference to perform interpolation processing on the data from other data sources to achieve time alignment.

[0127] For example, the time information in the sensing results of the integrated sensing base station can be used as a benchmark. The purpose of time alignment is to align the location information obtained from different data sources in time. For instance, the data obtained from the sensing base station is in 640ms intervals, with sensing location information at time points of 12:38:45:100, 12:38:45:740, 12:38:46:380, and 12:38:47:020. TDOA data has sensing location information at 12:38:45:100, 12:38:45:200, 12:38:45:300, 12:38:45:400, and 12:38:45. Location information obtained from system positioning is available at time points 12:500, 12:38:45:600, 12:38:45:700, and 12:38:45:800. An interpolation algorithm is used to obtain location information at time point 12:38:45:740, and so on, ensuring that all data sources have location information at time points 12:38:45:100, 12:38:45:740, 12:38:46:380, and 12:38:47:020. This allows for the fusion of location information from different data sources at the same time point. The interpolation algorithm can be linear interpolation, Lagrange interpolation, or other algorithms, which will not be elaborated here.

[0128] In this way, by converting the formats of multiple different data sources, it is easier to perform calculations on different data sources. Then, time alignment processing is performed on the converted data sources to obtain the location information of multiple data sources at the same point in time, which facilitates the fusion of location information of different data sources at the same point in time.

[0129] The positioning method of this application will be described below through specific embodiments.

[0130] The implementation process of the positioning method provided in this application includes the following S1 to S4:

[0131] S1. Data Integration: Based on the coverage of different data sources and devices, different data source devices covering the same area are grouped together, and data from different data sources within the same group are integrated.

[0132] S2. Format Conversion (Preparation Stage): Format conversion refers to the coordinate system conversion of geographical locations, that is, converting the latitude and longitude information (longitude, latitude, and altitude) of various data sources into a geocentric coordinate system (X, Y, Z). The conversion method is the same as existing methods (not an innovative point), and will not be described in detail here.

[0133] S3. Time Alignment: Using the time information from a specific data source as a benchmark, interpolation is performed on data from other data sources to achieve time alignment. An example is using the time information from the sensing results of a sensor-integrated base station as the benchmark. The purpose of time alignment is to align location information obtained from different data sources in time. For instance, data obtained from a sensor-integrated base station is in 640ms intervals, with sensing location information at time points of 12:38:45:100, 12:38:45:740, 12:38:46:380, and 12:38:47:020. TDOA data has sensing location information at 12:38:45:100, 12:38:45:200, 12:38:45:300, 12:38:45:400, and 12:38:45... Location information obtained from system positioning is available at time points 12:500, 12:38:45:600, 12:38:45:700, and 12:38:45:800. An interpolation algorithm is used to obtain location information at time point 12:38:45:740, and so on, ensuring that all data sources have location information at time points 12:38:45:100, 12:38:45:740, 12:38:46:380, and 12:38:47:020. This allows for the fusion of location information from different data sources at the same time point. The interpolation algorithm can be linear interpolation, Lagrange interpolation, or other algorithms, which will not be elaborated here.

[0134] S4. Update the first absolute weights Wxi, Wyi, and Wzi of different data sources with time T, and calculate the relative weights Rxi, Ryi, and Rzi. Assuming the current time is t, the position information (X, Y, Z) from time (tT) to time t is fused using the above formulas (7), (8), and (9) (T can be 640ms or 1s for example).

[0135] X = ∑xi × Rxi; (7)

[0136] Y = ∑yi × Ryi; (8)

[0137] Z = ∑zi × Rzi; (9)

[0138] (xi, yi, zi) represents the location information obtained from data source i. Rxi is the relative weight of data source i along the x-axis, Ryi is the relative weight of data source i along the y-axis, and Rzi is the relative weight of data source i along the z-axis. Wxi is the absolute weight of data source i along the x-axis, Wyi is the absolute weight of data source i along the y-axis, and Wzi is the absolute weight of data source i along the z-axis. For example, (x1, y1, z1) represents the location information obtained from the sensing base station, where Wx1 is the absolute weight of the sensing base station along the x-axis, Rx1 is the relative weight of the sensing base station along the x-axis, Wy1 is the absolute weight of the sensing base station along the y-axis, Ry1 is the relative weight of the sensing base station along the y-axis, Wz1 is the absolute weight of the sensing base station along the z-axis, and Rz1 is the relative weight of the sensing base station along the z-axis; (x2, y2, z2) represents the TDOA location information obtained from the base station, where Wx2 is the absolute weight of the TDOA data along the x-axis, Rx2 is the relative weight of the TDOA data along the x-axis, and Wy2 is the absolute weight of the TDOA data along the z-axis. The absolute weight of data A on the y-axis, Ry2 is the relative weight of data TDOA on the y-axis, Wz2 is the absolute weight of data TDOA on the z-axis, and Rz2 is the relative weight of data TDOA on the z-axis; (x3, y3, z3) is the position information of RID data, Wx3 is the absolute weight of data RID on the x-axis, Rx3 is the relative weight of data RID on the x-axis, Wy3 is the absolute weight of data RID on the y-axis, Ry3 is the relative weight of data RID on the y-axis, Wz3 is the absolute weight of data RID on the z-axis, and Rz3 is the relative weight of data RID on the z-axis.

[0139] The relative weight Ri is calculated as shown in formulas (4), (5), and (6) above:

[0140] Rxi = Wxi / ∑Wxi; (4)

[0141] Ryi = Wyi / ∑Wyi; (5)

[0142] Rzi = Wzi / ∑Wzi; (6)

[0143] The calculation methods for the first absolute weights Wxi, Wyi, and Wzi are shown in formulas (1), (2), and (3) above:

[0144] Wxi=(1-k)×Wxit'+k×Wxi'; (1)

[0145] Wyi=(1-k)×Wyit'+k×Wyi'; (2)

[0146] Wzi=(1-k)×Wzit'+k×Wzi'; (3)

[0147] Wxi, Wyi, and Wzi are the absolute weights of data source i on the x, y, and z axes, respectively. Wxit, Wyit, and Wzit are the absolute weights of data source i on the x, y, and z axes calculated at the current time t. Wxi', Wyi', and Wzi' are the absolute weights of the ij-th data source on the x, y, and z axes calculated at time (tT). k is the smoothing coefficient, which is a positive number less than or equal to 1. The calculation formulas for Wxit, Wyit, and Wzit are shown in formulas (7), (8), and (9) above.

[0148] If data source j has no location result at a certain time, the corresponding absolute weight is reset to 0.

[0149] Dxi represents the perceived accuracy of data source i along the x-axis, Dyi represents the perceived accuracy of data source i along the y-axis, and Dzi represents the perceived accuracy of data source i along the z-axis. Axi is the x-axis accuracy weight correction factor, Ayi is the y-axis accuracy weight correction factor, and Azi is the z-axis accuracy weight correction factor.

[0150] Faci is the accuracy factor.

[0151] The methods for determining the sensing accuracy Dx1, Dy1, and Dz1 of the integrated sensing base station are as shown in formulas (10), (11), and (12) above:

[0152] Dx1=cos 2 φcos 2 θDr+R 2 cos 2 φsin 2 θDθ+R 2 sin 2 φcos 2 θDφ; (10)

[0153] Dy1=cos 2 φsin 2 θDr+R 2 cos 2 φcos 2 θDθ+R2 sin 2 φsin 2 θDφ; (11)

[0154] Dz1=sin 2 φDr+R 2 cos 2 φDφ; (12)

[0155] θ is the azimuth angle, and φ is the elevation angle. The average value of the current target object over a past time T can be taken.

[0156] The parameters in the above formula are explained as follows:

[0157] Dr represents the distance precision, as shown in formula (13) above: Dr = C 2 / (8×B 2 (×SNR).

[0158] C is 3 × 10⁸ m / s, B is the system bandwidth, SNR is the signal-to-noise ratio, which can be taken as the average value over a past time T. R is the distance between the target object and the base station, which can also be taken as the average value over a past time T.

[0159] Dθ represents the azimuth accuracy, and its calculation formula is shown in formula (14) above:

[0160] Dθ=3λ 2 / [L×SNR×Nx×(Nx 2 -1)×2π 2 ×dx 2 ×cos 2 φcos 2 θ];

[0161] L is the number of coherent accumulations, SNR is the signal-to-noise ratio, Nx is the number of elements in the horizontal direction, and dx is the spacing between elements in the horizontal direction.

[0162] Dφ represents the pitch angle accuracy, and its calculation formula is shown in formula (15) above:

[0163] Dφ=3λ 2 / [L×SNR×Ny×(Ny 2 -1)×2π 2 ×dy 2 ×cos 2 φ];

[0164] Ny represents the number of elements in the vertical direction, and dy represents the spacing between elements in the vertical direction.

[0165] The method for determining Fac1 is shown in the above formula (19): Fac1 = 2 / (1+e a×（E-Eth） );

[0166] Here, E measures the positioning accuracy of the sensing base station, calculated based on measured data: A drone flight test is conducted within the base station's coverage area to obtain RTK data (true value data). After time alignment, the absolute value (in meters) of the position difference between the RTK data and the integrated sensing data at the same time point is calculated, and the difference at CDF Xc is recorded as E (Xc can be 95% for example). The E value varies for different base stations. Eth represents the required positioning accuracy, set based on scenario requirements, for example, it can be set to 1 meter. 'a' is the steepness factor, which can be set to 10 for example.

[0167] For a sensing base station with a large amount of test data, the relationship between the feature vector (x, y, z, SNR, φ, θ) and Dx1, Dy1, and Dz1 can be determined by machine learning. Then, Dx1, Dy1 and Dz1 can be determined by inputting the feature vector (x, y, z, SNR, φ, θ).

[0168] Thus, this application determines the weights based on the network status of different data sources at different time points, making the fusion result closer to the true value; at the same time, it combines the signal-to-noise ratio, angle, and distance to determine the sensing accuracy of the integrated sensing base station, and combines the positioning accuracy information of the sensing base station to jointly determine the absolute weights; based on the absolute weights, the relative weights are calculated, and then based on the relative weights, the data sources are fused to obtain the location information of the sensing target, making the fusion result closer to the true value, thereby improving the sensing accuracy and sensing reliability.

[0169] It should be noted that the descriptions of each step S1 to S4 in this embodiment can be found in the descriptions in the above embodiments, and will not be repeated here.

[0170] It should be noted that the above-described method embodiments, or the various possible implementations of the method embodiments, can be executed individually, or, provided there is no conflict, they can be combined with each other. The specific implementation can be determined according to actual usage requirements, and this application embodiment does not impose any restrictions on this.

[0171] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0172] This application embodiment can divide the positioning device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0173] In some embodiments, this application also provides a positioning device. The positioning device may include one or more functional modules for implementing the positioning method of the above method embodiments.

[0174] For example, Figure 6 This is a schematic diagram of a positioning device provided in an embodiment of this application. Figure 6 As shown, the positioning device 900 includes: a determining module 901, a processing module 902, and an acquiring module 903.

[0175] The determining module 901 is used to determine the absolute weight of the i-th data source on each coordinate axis based on the sensing accuracy of each coordinate axis of the sensing base station at the i-th time point. The i-th data source includes: the positioning information of the sensing target obtained by multiple devices and the sensing data of the sensing target obtained by the sensing base station at the i-th time point; i is a positive integer. The determining module is also used to determine the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source. The processing module 902 is used to perform fusion processing on the i-th data source based on the relative weight of the i-th data source on each coordinate axis to obtain the position information of the sensing target at the i-th time point.

[0176] The positioning device provided in this application can determine the weights of the positioning information of the perceived target obtained by multiple devices and the perceived data of the perceived target obtained by the sensing base station on each coordinate axis at different time points, based on the sensing accuracy of the sensing base station on each coordinate axis. Then, based on these weights, the data are fused to obtain the location information of the perceived target. This allows the weights to be determined based on the network status of different data sources at different time points, making the fusion result closer to the true value. Thus, it is not limited by the local observation perspective of a single base station or environmental obstructions, improving sensing accuracy and reliability.

[0177] In some embodiments, the determining module 901 is specifically used to determine a first absolute weight based on the absolute weight of the i-th data source on each coordinate axis and the absolute weight of the ij-th data source on each coordinate axis; where j is a positive integer less than i; and to determine the relative weight of the i-th data source on each coordinate axis based on the first absolute weight.

[0178] In some other embodiments, the determining module 901 is further configured to determine the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point before determining the absolute weight of the i-th data source on each coordinate axis based on the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point.

[0179] In some other embodiments, the acquisition module 903 is used to acquire multiple data sources; the processing module 903 is also used to perform format conversion and time alignment processing on the multiple data sources to obtain the processed multiple data sources.

[0180] It should be noted that the positioning device can implement all the processes implemented in the above method embodiments and achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0181] In the case where the functions of the integrated modules described above are implemented in hardware, this application provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 7 As shown, the electronic device 90 includes: a processor 92, a communication interface 93, and a bus 94. Optionally, the electronic device 90 may also include a memory 91.

[0182] Processor 92 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 92 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0183] Communication interface 93 is used to connect with other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0184] The memory 91 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0185] As one possible implementation, the memory 91 can exist independently of the processor 92. The memory 91 can be connected to the processor 92 via a bus 94 and is used to store instructions or program code. When the processor 92 calls and executes the instructions or program code stored in the memory 91, it can implement the positioning method provided in the embodiments of this application.

[0186] In another possible implementation, memory 91 can also be integrated with processor 92.

[0187] Bus 94 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 94 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0188] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0189] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described positioning method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0190] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0191] This application also provides a readable storage medium storing a program or instructions that, when executed by a computer, implement the positioning method provided in the above embodiments. It is understood that all or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware; the readable storage medium can be any of the foregoing embodiments or memory; the readable storage medium can also be an external storage device of the service invocation device, such as a pluggable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, flash card, etc., equipped on the service invocation device. Further, the readable storage medium can include both internal storage units of the service invocation device and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the service invocation device. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0192] This application also provides a computer program product, which is stored in a storage medium and implements the positioning method provided in the above embodiments when the computer program product is executed by a computer.

[0193] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0194] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0195] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A positioning method, characterized in that, The method includes: Based on the sensing accuracy of each coordinate axis of the sensing base station in the coordinate system corresponding to the i-th time point, the absolute weight of the i-th data source in each coordinate axis is determined; wherein, the i-th data source includes: the positioning information of the sensing target obtained by multiple devices and the sensing data of the sensing target obtained by the sensing base station at the i-th time point; i is a positive integer; Based on the absolute weights of the i-th data source on each coordinate axis, determine the relative weights of the i-th data source on each coordinate axis. Based on the relative weights of the i-th data source on each coordinate axis, the i-th data source is fused to obtain the position information of the perceived target at the i-th time point.

2. The positioning method according to claim 1, characterized in that, The location information includes at least one of the following: Time Difference of Arrival (TDOA) positioning data from base stations, Remote Identification Number (RID) data from drones, location information obtained by cameras, and location information obtained by radar.

3. The positioning method according to claim 1, characterized in that, The step of determining the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis includes: The first absolute weight is determined based on the absolute weights of the i-th data source on each coordinate axis and the absolute weights of the ij-th data source on each coordinate axis; where j is a positive integer less than i. Based on the first absolute weight, the relative weight of the i-th data source on each coordinate axis is determined.

4. The positioning method according to any one of claims 1 to 3, characterized in that, Before determining the absolute weights of the i-th data source on each coordinate axis based on the sensing accuracy of the sensing base station at the i-th time point in the coordinate system, the method further includes: Based on the azimuth angle, elevation angle, distance accuracy, azimuth angle accuracy, elevation angle accuracy, and distance between the sensing target and the sensing base station, the sensing accuracy of each coordinate axis of the sensing base station in the coordinate system corresponding to the i-th time point is determined.

5. The positioning method according to any one of claims 1 to 3, characterized in that, The method further includes: Obtain multiple data sources; The multiple data sources are subjected to format conversion and time alignment to obtain the processed multiple data sources; Among the processed multiple data sources, the i-th data source is included.

6. A positioning device, characterized in that, include: Determine the module and the processing module; The determining module is used to determine the absolute weight of the i-th data source on each coordinate axis based on the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point; wherein, the i-th data source includes: the positioning information of the sensing target obtained by multiple devices and the sensing data of the sensing target obtained by the sensing base station at the i-th time point; i is a positive integer; The determining module is further configured to determine the relative weight of the i-th data source on each coordinate axis based on the absolute weight of the i-th data source on each coordinate axis; The processing module is used to perform fusion processing on the i-th data source based on the relative weights of the i-th data source on each coordinate axis to obtain the position information of the perceived target at the i-th time point.

7. The positioning device according to claim 6, characterized in that, The determining module is specifically used to determine a first absolute weight based on the absolute weight of the i-th data source on each coordinate axis and the absolute weight of the ij-th data source on each coordinate axis; where j is a positive integer less than i; and to determine the relative weight of the i-th data source on each coordinate axis based on the first absolute weight.

8. The positioning device according to claim 6 or 7, characterized in that, The determining module is further configured to, before determining the absolute weights of the i-th data source on each coordinate axis in the coordinate system corresponding to the i-th time point based on the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point, determine the sensing accuracy of the sensing base station on each coordinate axis in the coordinate system corresponding to the i-th time point, based on the azimuth angle of the sensing base station, the elevation angle of the sensing base station, the distance accuracy of the sensing base station, the azimuth angle accuracy of the sensing base station, the elevation angle accuracy of the sensing base station, and the distance between the sensing target and the sensing base station.

9. The positioning device according to claim 6 or 7, characterized in that, The positioning device also includes an acquisition module; The acquisition module is used to acquire multiple data sources; The processing module is also used to perform format conversion and time alignment processing on the multiple data sources to obtain the processed multiple data sources.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the positioning method as described in any one of claims 1-5.

11. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a computer, implement the positioning method as described in any one of claims 1-5.

12. A computer program product, characterized in that, The computer program product is stored in a storage medium, and when executed by a computer, the computer program product implements the positioning method as described in any one of claims 1-5.

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