A fusion positioning method based on 5G-A base station and UWB base station

Abstract

This application provides a fusion positioning method based on 5G-A base stations and UWB base stations, comprising: acquiring environmental information of the target area to be deployed, and determining the deployment quantity and deployment location information of UWB base stations and 5G-A base stations respectively based on the environmental information and the effective ranging radius of the UWB base stations; generating a deployment network based on the first deployment quantity and first deployment location information of the UWB base stations and the second deployment quantity and second deployment location information of the 5G-A base stations; acquiring at least three valid communication signals corresponding to the target to be located from the deployment network; and determining the current location information of the target to be located based on the valid communication signals and the deployment location information of the corresponding base stations. This application achieves fusion positioning by using 5G-A base stations and UWB base stations, fully utilizing the characteristics of each type of base station, reducing base station deployment costs, and improving positioning accuracy.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a fusion positioning method based on 5G-A base stations and UWB base stations. Background Technology

[0002] Existing technology 1: UWB (Ultra Wide Band) positioning technology

[0003] Ultra-wideband (UWB) positioning technology has been widely used in indoor positioning systems due to its high accuracy and strong anti-interference capabilities. Based on the propagation characteristics of radio waves, UWB positioning technology enables precise time measurement, thereby calculating the target's location. Commonly used positioning algorithms in UWB positioning systems include Angle of Arrival (AOA), Received Signal Strength Indicator (RSSI), Time of Arrival (TOA), and Time Difference of Arrival (TDoA).

[0004] Angle of arrival (AOA) positioning algorithms typically require a line-of-sight (LOS) distance between the base station and the target; otherwise, the signal will be blocked or attenuated, leading to increased measurement errors. Although only two receivers are needed and time synchronization is not required, the deployment of antenna arrays is expensive.

[0005] The Received Signal Strength Indicator (RSSI) positioning algorithm estimates the distance between the target and the receiver by measuring the strength of the received signal and determines the target's location by measuring the distances between multiple receivers. However, it is easily interfered with by other signals, resulting in low accuracy.

[0006] The Time-of-Arrival (TOA) algorithm calculates distance by measuring the propagation time of a signal from the transmitter to the receiver, and then determines the target's two-dimensional coordinates using the locations of at least three base stations. This requires the target device and the base stations to maintain high clock synchronization; any clock deviation will directly affect the propagation time measurement, thus introducing positioning errors. Even a tiny clock error can lead to significant distance estimation errors; a time resolution of 0.1 ns corresponds to a spatial error of 3 cm. In the complex environment of the Industrial Internet, achieving real-time clock synchronization between devices and base stations is not a simple task.

[0007] The Time Difference of Arrival (TDoA) algorithm is an upgraded version of Time of Arrival (TOA). It calculates the target's location by measuring the time difference between the arrival times of the device at different base stations and converting it into a distance difference. Compared to TOA, TDoA does not require synchronizing the target device's clock; it only needs to keep the base station clocks synchronized, which is sufficient in most practical scenarios. Moreover, it offers high accuracy. Therefore, this application intends to adopt the TDoA algorithm as the primary algorithm.

[0008] Cung Lian Sang and his team at Bielefeld University [CungLian Sang, Adams M, Hörmann T, et al. Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods[J]. Sensors,2020, 20(8): 2403.] and [Cung Lian Sang, Adams M, Hesse M, et al. Bidirectional UWB Localization: A Review on an Elastic Positioning Scheme for GNSS-DeprivedZones[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1-15.] proposed an extended time-of-flight error estimation UWB ranging method based on the IEEE 802.15.4 standard in 2018, taking into account propagation delay, transmission time, and reception time delay. Simulation results show that the two-way ranging method DS-TWR (double-side two-way ranging) is superior to the NLOS and Multi-Path Conditions in UWB Localization. The performance is superior to the single-path ranging method. Based on this, in 2020, the performance of the Time of Arrival (TOA), Time Difference of Arrival (TDoA), and Received Signal Strength (RISS) algorithms was compared. The study found that in dynamic multipath environments, the TDoA algorithm can significantly suppress clock offset errors through multi-base station cooperation, achieving an average positioning accuracy of 0.15 meters, which is better than TOA's 0.25 meters. In 2023, combined with the two-way ranging method DA-TWR and a dynamic anchor point selection strategy, sub-meter-level positioning accuracy was achieved in an underground parking lot, and even under extreme conditions where non-line-of-sight (NLOS) accounted for 70%, the error could still be limited to within 0.8 meters.

[0009] Matteo Ridolfi and his team at Ghent University [Ridolfi M, Kaya A, Berkvens R, et al. Self-calibration and Collaborative Localization for UWBPositioning Systems[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2):1022-1050.] proposed a self-calibration network architecture based on collaborative localization in 2021. Its core principle is to optimize the position of fixed anchor points by using trajectory data from mobile nodes (such as robots), thereby reducing the cost of manual calibration. Experiments show that this method achieves good results within 100m... 2 Within the area, the anchor point position calibration error was reduced from 1.2 meters to 0.3 meters.

[0010] Zhang Yutong and her team from Nanjing University of Posts and Telecommunications [Zhang Yutong, Luo Shitao, Qu Tao. Design and Implementation of UWB-Based Positioning System for Underground Parking Lots [J]. Journal of Electronics and Information Technology, 2022, 44(5): 123-130.] designed a two-dimensional positioning system for parking lots based on three UWB base stations in 2022. They used a trilateration method combined with Kalman filtering to solve the dynamic occlusion problem. Experimental results showed that the static positioning error of the system was less than 0.058 meters, and the dynamic trajectory tracking error was less than 0.5 meters. The reliability of the algorithm was verified by comparison using Matlab.

[0011] Yan Jiaqi, Zhou You, Zhao Cong. Research on Multipath Suppression of Improved TDoA Algorithm in Corridor Environment [J]. Journal of Harbin Institute of Technology, 2020, 52(7): 89-95.], proposed an improved TDoA algorithm in 2020, which suppresses multipath interference by introducing the sliding window averaging method and residual weighted optimization. Experiments show that in corridor environment, the positioning error is reduced from 0.6 meters in the traditional method to 0.25 meters.

[0012] Existing technology 2: 5G-A (5G-Advanced, 5G is more advanced) positioning technology

[0013] 5G-A is a further enhanced version of 5G networks. Through technologies such as bandwidth aggregation of in-band carrier positioning reference signal (PRS) and sounding reference signal (SRS), and New Radio (NR) phase measurement, it can achieve sub-meter or even centimeter-level positioning accuracy. Simultaneously, 5G-A combines communication and sensing functions, giving base stations radar-like sensing capabilities, supporting ranging, angle measurement, velocity measurement, and location tracking of targets such as aircraft and vehicles. Through enhanced network architecture and optimization technologies, it achieves millisecond-level low latency and high reliability. Combined with satellite systems such as BeiDou, it can realize a seamless indoor and outdoor positioning system, supporting precise positioning in complex terrain.

[0014] In 2021, Huawei completed the world's first integrated sensing test, with a base station detection range of over 500 meters, a vehicle / pedestrian positioning accuracy of 0.5 meters at the lane level, and a detection accuracy of 100% for vehicles and pedestrians.

[0015] In 2024, the China Unicom Smart City Research Institute, in collaboration with the Purple Mountain Laboratories, Jiangsu Unicom, and the China Academy of Information and Communications Technology (CAICT), successfully deployed the nation's first commercial 5G-A high-precision positioning system for the high-end manufacturing industry at Suzhou Huaxing Yuanchuang Technology Co., Ltd. This system, based on Uplink Time Difference of Arrival (UTDoA) positioning technology, a hybrid positioning model constructed by fusing wireless fingerprinting and field strength, and combined with a Kalman filter optimization algorithm, achieved a significant breakthrough in positioning accuracy, improving from traditional meter-level to sub-meter-level accuracy, with most areas exceeding 0.5 meters. This provided real-time, precise positioning services for material carts, AGV robots, and factory personnel in automated assembly workshops. Furthermore, in February 2025, at the 9th Asian Winter Games, it was first applied to staff scheduling at the venues. Utilizing precise indoor positioning capabilities, the system could track staff locations in real time, thereby optimizing scheduling, improving work efficiency, and ensuring the smooth progress of all preparations for the Games.

[0016] However, existing UWB and 5G-A positioning technologies are each limited in specific positioning scenarios, making it difficult to balance positioning accuracy and base station deployment costs.

[0017] It should be noted that the information in the background section above is only used to enhance the understanding of the background technology of this application, and therefore may include technical information that does not constitute technical information known or easily inferred by a person skilled in the art. Summary of the Invention

[0018] In view of the above problems, this application is made to provide a fusion positioning method and apparatus based on 5G-A base stations and UWB base stations to overcome or at least partially solve the above problems, comprising:

[0019] A fusion positioning method based on 5G-A base stations and UWB base stations, the method involving 5G-A base stations and UWB base stations;

[0020] The method includes:

[0021] Obtain environmental information of the target area to be deployed, and determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station;

[0022] A deployment network is generated based on the first deployment quantity and first deployment location information corresponding to the UWB base stations and the second deployment quantity and second deployment location information corresponding to the 5G-A base stations;

[0023] When the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station;

[0024] The current location information of the target to be located is determined based on the valid communication signal and the deployment location information of the corresponding base station.

[0025] Further, the step of acquiring environmental information of the target area to be deployed, and determining the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station, respectively, includes:

[0026] The first deployment quantity and the first deployment location information are determined based on the environmental information and the effective ranging radius, respectively.

[0027] The number of blind spot areas and the location information of the blind spot areas are determined based on the environmental information, the first deployment location information, and the effective ranging radius.

[0028] The second deployment quantity is determined based on the number of blind spot areas;

[0029] The second deployment location information is determined based on the location information of the blind spot area.

[0030] Further, the UWB base station includes edge base stations and central base stations; the step of determining the first deployment quantity and the first deployment location information based on the environmental information and the effective ranging radius includes:

[0031] The deployment range information is determined based on the environmental information; wherein the deployment range includes at least one square plane;

[0032] The position information of the four vertices corresponding to the square plane is determined based on the deployment range information;

[0033] The number of first sub-deployments and the location information of the first sub-deployments corresponding to the edge base station are determined based on the vertex location information.

[0034] The number of second sub-deployments corresponding to the central base station is determined based on the number of the first sub-deployments;

[0035] The location information of the second sub-deployment corresponding to the central base station is determined based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of the second sub-deployments.

[0036] Further, the number of the second sub-deployments is twice the number of the first sub-deployments; each edge base station corresponds to two central base stations; the step of determining the location information of the second sub-deployment corresponding to the central base station based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of the second sub-deployments includes:

[0037] Based on the environmental information, determine the diagonal information corresponding to the square plane;

[0038] The second sub-deployment location information is determined based on the first sub-deployment location information, the effective ranging radius, and the diagonal information.

[0039] Further, the step of determining the number of blind spot areas and the location information of blind spot areas based on the environmental information, the first deployment location information, and the effective ranging radius includes:

[0040] The effective ranging area information is determined based on the first deployment location information and the effective ranging radius;

[0041] The number and location information of blind spots are determined based on the environmental information and the effective ranging area information.

[0042] Further, the step of determining the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station includes:

[0043] When there are at least four valid communication signals, the corresponding signal strength is determined based on the valid communication signals.

[0044] The signal strengths are sorted, and three ranging target signals are determined based on the sorting results;

[0045] The current position information is determined based on the ranging target signal.

[0046] Furthermore, the method is used to predict the trajectory of a located target; the method also includes:

[0047] Obtain the current time information corresponding to the current location information, and determine the current motion state information of the located target based on multiple current location information and the current time information;

[0048] The corresponding motion prediction algorithm is determined based on the current motion state information;

[0049] Based on the current location information and the current motion state information, the motion prediction algorithm determines the motion trajectory prediction information.

[0050] Further, the current motion state information includes: the velocity, acceleration, and angular velocity of the located target at adjacent time points; the motion prediction algorithm includes: Kalman filter algorithm (KF), extended Kalman filter algorithm (EKF), and unscented Kalman filter algorithm (UKF); the step of determining the corresponding motion prediction algorithm based on the current motion state information includes:

[0051] The corresponding target motion model is determined based on the current motion state information; wherein, the target motion model includes at least one of the following: constant velocity model CV, constant acceleration model CA, constant rotation speed and velocity model CTRV, and constant rotation speed and acceleration model CTRA;

[0052] The corresponding motion prediction algorithm is determined based on the target motion model.

[0053] A fusion positioning device based on 5G-A base station and UWB base station, the device involving 5G-A base station and UWB base station;

[0054] The device includes:

[0055] The base station deployment module is used to acquire environmental information of the target area to be deployed, and to determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station.

[0056] A network generation module is used to generate a deployment network based on the first deployment quantity and first deployment location information corresponding to the UWB base station and the second deployment quantity and second deployment location information corresponding to the 5G-A base station.

[0057] The signal acquisition module is used to acquire at least three valid communication signals corresponding to the target from the deployment network when the target to be located enters the target area; wherein the valid communication signals are emitted by the 5G-A base station and / or the UWB base station;

[0058] The positioning module is used to determine the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station.

[0059] A computer device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any embodiment of this application.

[0060] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any embodiment of this application.

[0061] This application has the following advantages:

[0062] In the embodiments of this application, given that existing UWB and 5G-A positioning technologies are each limited in specific positioning scenarios, making it difficult to balance positioning accuracy and base station deployment costs, this application provides a solution for fusion positioning using 5G-A base stations and UWB base stations. Specifically, the solution involves: acquiring environmental information of the target area to be deployed; determining, based on the environmental information and the effective ranging radius of the UWB base stations, the first deployment quantity and first deployment location information of the UWB base stations, and the second deployment quantity and second deployment location information of the 5G-A base stations; generating a deployment network based on the first deployment quantity and first deployment location information of the UWB base stations and the second deployment quantity and second deployment location information of the 5G-A base stations; when the target to be located enters the target area, acquiring at least three valid communication signals corresponding to the target from the deployment network; wherein the valid communication signals are emitted by the 5G-A base station and / or the UWB base station; and determining the current location information of the target based on the valid communication signals and the deployment location information of the corresponding base stations. This application achieves integrated positioning by combining 5G-A base stations and UWB base stations, fully utilizing the characteristics of each, reducing base station deployment costs, and improving positioning accuracy. Attached Figure Description

[0063] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating the steps of a fusion positioning method based on a 5G-A base station and a UWB base station, provided in one embodiment of this application.

[0065] Figure 2 This is a schematic diagram of the location of a UWB base station in one embodiment of this application;

[0066] Figure 3 This is a schematic diagram of the effective ranging area of ​​a UWB base station in one embodiment of this application;

[0067] Figure 4 This is a schematic diagram of the site selection of a 5G-A base station in one embodiment of this application;

[0068] Figure 5 This is a schematic diagram of the effective ranging area of ​​a 5G-A base station in one embodiment of this application;

[0069] Figure 6 This is a schematic diagram of the effective ranging area of ​​a UWB base station and a 5G-A base station combined in one embodiment of this application;

[0070] Figure 7 This is a comparison diagram of base station positioning simulation results in one embodiment of this application;

[0071] Figure 8 This is a schematic diagram of the simulation results in a linear motion scenario according to one embodiment of this application;

[0072] Figure 9 This is a schematic diagram of the simulation results under an irregular curve motion scenario in one embodiment of this application;

[0073] Figure 10 It corresponds to Figure 9 Region partitioning diagram of the simulation results of irregular curve motion;

[0074] Figure 11 This is a flowchart illustrating the steps involved in implementing fusion positioning and dynamic tracking in one embodiment of this application;

[0075] Figure 12 This is a structural block diagram of a fusion positioning device based on a 5G-A base station and a UWB base station provided in one embodiment of this application;

[0076] Figure 13 This is a structural block diagram of a computer device provided in one embodiment of this application. Detailed Implementation

[0077] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. 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 without inventive effort are within the scope of protection of this application.

[0078] The inventors discovered through analysis of existing technology that:

[0079] Traditional UWB positioning systems suffer from several significant drawbacks in practical deployment, severely limiting their stability and applicability. First, UWB signal transmission is extremely sensitive to metallic structures in the environment, especially in industrial settings such as large equipment areas, metal pipes, and machining areas, where strong signal reflection and diffraction are prone to occur, leading to multipath effects and ranging errors. Second, due to regulatory constraints on UWB transmission power, its signal coverage is weak, with typical communication distances usually not exceeding 30 meters, making it difficult to meet the positioning needs of medium to large-scale industrial plants. Furthermore, UWB systems require high node deployment density, resulting in a large number of base stations, complex deployment, and increased system construction and maintenance costs, creating a high implementation threshold in actual projects.

[0080] Traditional 5G-A positioning methods face numerous technical bottlenecks in real-world industrial environments. First, 5G-A positioning heavily relies on base station density and network planning quality, making it difficult to achieve uniform coverage in complex factory areas due to cost and environmental constraints. Second, under non-line-of-sight conditions, especially in scenarios with obstructions from warehouse shelves or high walls, signal propagation paths are susceptible to multipath interference and shadowing effects, leading to significantly increased ranging errors and a drop in positioning accuracy to over 1 meter. Furthermore, 5G-A positioning systems place high demands on the collaborative capabilities and synchronization accuracy of terminal devices. Some low-power terminals struggle to maintain energy consumption control and clock synchronization over extended periods, further impacting system stability and tracking continuity.

[0081] Therefore, existing positioning technologies still face many challenges in practical applications: on the one hand, UWB technology has nanosecond-level time resolution, but its coverage is limited and it is highly sensitive to metal blockage, which seriously affects signal stability; on the other hand, although 5G-A positioning is highly dependent on base station coverage and is susceptible to multipath interference in non-line-of-sight scenarios, the positioning error often exceeds 1 meter.

[0082] Based on the above analysis, one of the core technical concepts of this application is to provide a fusion positioning method based on 5G-A base stations and UWB base stations to achieve high-precision positioning of industrial Internet terminals. Through multimodal signal processing and dynamic optimization mechanisms, it solves the problems of multipath interference, signal blockage and energy consumption balance, and achieves centimeter-level high-precision positioning to meet the needs of complex industrial environments for accurate positioning.

[0083] Reference Figure 1 This application illustrates a fusion positioning method based on 5G-A base stations and UWB base stations according to an embodiment of the present application, the method involving 5G-A base stations and UWB base stations;

[0084] The method includes:

[0085] S110. Obtain environmental information of the target area to be deployed, and determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station.

[0086] S120. Generate a deployment network based on the first deployment quantity and first deployment location information corresponding to the UWB base station and the second deployment quantity and second deployment location information corresponding to the 5G-A base station;

[0087] S130. When the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station.

[0088] S140. Determine the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station.

[0089] In the embodiments of this application, given that existing UWB and 5G-A positioning technologies are each limited in specific positioning scenarios, making it difficult to balance positioning accuracy and base station deployment costs, this application provides a solution for fusion positioning using 5G-A base stations and UWB base stations. Specifically, the solution involves: acquiring environmental information of the target area to be deployed; determining, based on the environmental information and the effective ranging radius of the UWB base stations, the first deployment quantity and first deployment location information of the UWB base stations, and the second deployment quantity and second deployment location information of the 5G-A base stations; generating a deployment network based on the first deployment quantity and first deployment location information of the UWB base stations and the second deployment quantity and second deployment location information of the 5G-A base stations; when the target to be located enters the target area, acquiring at least three valid communication signals corresponding to the target from the deployment network; wherein the valid communication signals are emitted by the 5G-A base station and / or the UWB base station; and determining the current location information of the target based on the valid communication signals and the deployment location information of the corresponding base stations. This application achieves integrated positioning by combining 5G-A base stations and UWB base stations, fully utilizing the characteristics of each, reducing base station deployment costs, and improving positioning accuracy.

[0090] The following will further explain a fusion positioning method based on 5G-A base stations and UWB base stations in this exemplary embodiment.

[0091] As described in step S110, environmental information of the target area to be deployed is obtained, and the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station are determined based on the environmental information and the effective ranging radius of the UWB base station.

[0092] It should be noted that the target to be located needs to be within the effective measurement distance of at least 3 base stations simultaneously in order to calculate the coordinates. That is, an effective ranging area can be formed by three or more overlapping circles with the base station as the center and the effective measurement distance as the radius. Therefore, in the deployment network, the total number of the 5G-A base stations and the UWB base stations is not less than 3.

[0093] As described in step S120, a deployment network is generated based on the first deployment quantity and first deployment location information corresponding to the UWB base station and the second deployment quantity and second deployment location information corresponding to the 5G-A base station.

[0094] It should be noted that the environmental information of the target area to be deployed may include the boundary location information of the target area; the larger the area of ​​the target area, the greater the number of the first deployment of the UWB base station and the number of the second deployment of the 5G-A base station.

[0095] As an example, refer to Figure 2 The UWB base stations may include 12 units, each with an effective ranging radius of 35m. They can be deployed in groups of three, forming an equilateral triangle (with sides of 35m, the same as the effective ranging radius), with each group centrally symmetrically positioned within the target area. This deployment method aims to minimize spatial GDOP (Geometric Dilution of Precision) and achieve efficient ranging coverage within the area.

[0096] As described in step S130, when the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station.

[0097] It should be noted that the effective communication signal may include the ranging signals of each base station in the deployed network, which can be used for fusion positioning.

[0098] In one embodiment of this application, the specific process of step S110, which involves "obtaining environmental information of the target area to be deployed, and determining the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station," can be further explained in conjunction with the following description.

[0099] As described in the following steps, the first deployment quantity and the first deployment location information are determined based on the environmental information and the effective ranging radius, respectively;

[0100] The number of blind spot areas and the location information of the blind spot areas are determined based on the environmental information, the first deployment location information, and the effective ranging radius.

[0101] The second deployment quantity is determined based on the number of blind spot areas;

[0102] The second deployment location information is determined based on the location information of the blind spot area.

[0103] It should be noted that the blind spot area corresponds to the deployment location of the 5G-A base station, so as to effectively supplement the UWB base station.

[0104] As an example, the blind spot area and the 5G-A base station can be in one-to-one correspondence, that is, the number of second deployments is equal to the number of blind spot areas, and the second deployment location of the 5G-A base station is within the range of the blind spot area.

[0105] In one embodiment of this application, the UWB base station includes an edge base station and a central base station; the specific process of "determining the first deployment quantity and the first deployment location information based on the environmental information and the effective ranging radius" can be further explained in conjunction with the following description.

[0106] As described in the following steps, the deployment scope information is determined based on the environmental information; wherein the deployment scope includes at least one square plane;

[0107] The position information of the four vertices corresponding to the square plane is determined based on the deployment range information;

[0108] The number of first sub-deployments and the location information of the first sub-deployments corresponding to the edge base station are determined based on the vertex location information.

[0109] The number of second sub-deployments corresponding to the central base station is determined based on the number of the first sub-deployments;

[0110] The location information of the second sub-deployment corresponding to the central base station is determined based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of the second sub-deployments.

[0111] It should be noted that the first deployment quantity includes the first sub-deployment quantity and the second sub-deployment quantity; the first deployment location information includes the first sub-deployment location information and the second sub-deployment location information.

[0112] As an example, the four vertices of the square plane can correspond one-to-one with the edge base stations; that is, the number of the first sub-deployments is equal to the number of vertices of the square plane, and the first sub-deployment position of the edge base station is located at a vertex of the square plane. The 5G-A base station can be deployed at the midpoint of the four sides of the square plane. Within a square plane, there can be 4 edge base stations, 8 central base stations, and 4 5G-A base stations.

[0113] In one embodiment of this application, the number of second sub-deployments is twice the number of first sub-deployments; each edge base station corresponds to two central base stations; the specific process of "determining the location information of the second sub-deployment corresponding to the central base station based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of second sub-deployments" can be further explained in conjunction with the following description.

[0114] As described in the following steps, the diagonal information corresponding to the square plane is determined based on the environmental information;

[0115] The second sub-deployment location information is determined based on the first sub-deployment location information, the effective ranging radius, and the diagonal information.

[0116] It should be noted that the central base station can be deployed at a distance of one effective ranging radius from the corresponding edge base station. Two central base stations corresponding to the same edge base station are symmetrically distributed along the diagonal passing through the corresponding edge base station, and the distance between them can also be one effective ranging radius. Thus, the UWB base stations are arranged in a centrally symmetrical manner on the square plane, with one edge base station and two central base stations forming a group. The edge base stations are respectively located at the four vertices of the square plane, and every two central base stations and one edge base station form an equilateral triangle.

[0117] As an example, when the effective ranging radius of the UWB base station is 35m, the side length of the square plane can be 67-85m. Figure 3 The diagram illustrates the base station deployment within a square plane with sides of 80m. If the side length of the square plane is too large, the signal from the deployed central base station may not cover the central area of ​​the square plane, resulting in blind spots. In such cases, additional central base stations or 5G-A base stations may need to be deployed. Conversely, if the side length of the square plane is too small, the deployed UWB base station can completely cover the target area, eliminating the need for additional 5G-A base stations.

[0118] In one embodiment of this application, the specific process of "determining the number of blind spot areas and the location information of blind spot areas based on the environmental information, the first deployment location information and the effective ranging radius" can be further explained in conjunction with the following description.

[0119] As described in the following steps, the effective ranging area information is determined based on the first deployment location information and the effective ranging radius;

[0120] The number and location information of blind spots are determined based on the environmental information and the effective ranging area information.

[0121] It should be noted that, with the first deployment location corresponding to the UWB base station as the center and the effective ranging radius corresponding to the UWB base station as the radius, a circle is drawn, and the area covered by at least three circles at the same time is taken as the effective ranging area. When the effective ranging area corresponding to the UWB base station cannot completely cover the target area, at most a blind spot area covered by only two circles at the same time can be determined by the environmental information corresponding to the target area and the effective ranging area information.

[0122] In one embodiment of this application, the specific process of "determining the current location information of the target to be located based on the effective communication signal and the deployment location information of the corresponding base station" in step S140 can be further explained in conjunction with the following description.

[0123] As described in the following steps, when the valid communication signals include at least four, the corresponding signal strength is determined based on the valid communication signals;

[0124] The signal strengths are sorted, and three ranging target signals are determined based on the sorting results;

[0125] The current position information is determined based on the ranging target signal.

[0126] It should be noted that when the target to be located moves into the effective ranging radius of at least four base stations (UWB base stations and / or 5G-A base stations), all of the above base stations can obtain effective communication signals to calculate the distance from the corresponding base station to the target to be located. However, since different signal strengths may lead to differences in signal quality, it is necessary to select the three ranging target signals with the best signal strength from at least four effective communication signals in order to calculate the current position information of the target to be located.

[0127] As an example, a ranging error model can also be introduced. By using the signal strength and ranging error model, the quality of all available base station signals can be evaluated, and three sets of ranging data with the best signal quality can be selected to participate in the final positioning fusion calculation, so as to avoid the adverse effects of low-quality signals on positioning accuracy.

[0128] In a specific embodiment of this application, the deployment of the base station and the localization of the target to be located can be achieved through simulation via the following steps:

[0129] Step 1: System architecture and base station deployment design:

[0130] The simulation scenario is designed in a 100m*100m two-dimensional simulation space, and the space to be tested is a central 80*80m square region D={(x,y)|-40 <x<40|-4-<y<40}。

[0131] The coordinates of the point to be measured can only be calculated if it is within the effective range of at least 3 base stations at the same time. That is, the effective range area is the area where three or more circles overlap with the base station as the center and the effective range as the radius.

[0132] Regarding UWB base station deployment, the base stations are arranged in an acute-angled triangle layout, with multiple UWB base stations distributed at the four corners of the area to be tested. The spacing between individual base stations is set at 35 meters, forming an equilateral triangle structure. Figure 2As shown. This deployment method aims to minimize spatial GDOP and achieve efficient ranging coverage within the area.

[0133] In MATLAB, simulate the distribution of base stations and the effective ranging radius. Fill the effective ranging region with three or more overlapping circles with color, such as... Figure 3 As shown, the uncolored areas are positioning blind spots, and the distance cannot be calculated solely by UWB base station ranging.

[0134] Regarding the deployment of 5G-A base stations, the base stations are arranged in a diamond shape at the midpoint of the four sides of the area to be measured, with a ranging radius of 70 meters. Figure 4 As shown in the diagram. This layout complements the UWB system, effectively avoiding positioning blind spots within the area and achieving continuous coverage across the entire region.

[0135] In MATLAB, simulate the distribution of base stations and the effective ranging radius. Fill the effective ranging region with three or more overlapping circles with color, such as... Figure 5 As shown.

[0136] Similarly, the uncolored areas represent positioning blind spots, where distances cannot be calculated solely through 5G-A base station ranging. However, this complements the positioning blind spots of UWB, enabling effective positioning across the entire area. Figure 6 As shown.

[0137] Step 2, Design of the fusion positioning algorithm:

[0138] First, the distance information between the target and each base station is obtained using the Double-Sided Two-Way Ranging (DS-TWR) method, and the initial position is calculated using the TDoA (Time Difference of Arrival) principle. Second, the Chan algorithm (a TDoA positioning analytical algorithm) is used for linear position estimation, and the estimation result is used as the initial value. Then, the LM nonlinear optimization algorithm is introduced to iteratively refine the positioning result, thereby improving the positioning accuracy. Finally, based on the signal strength and ranging error model, the quality of all available base station signals is evaluated, and three sets of ranging data with the best signal quality are selected to participate in the final positioning fusion calculation to avoid the adverse effects of low-quality signals on positioning accuracy.

[0139] Step 3, Design of performance evaluation metrics:

[0140] RMSE (Root Mean Square Error) is used as the core evaluation index to quantify the overall error performance of different positioning systems. At the same time, confidence interval analysis is introduced to statistically evaluate the positioning accuracy within a set accuracy range (20cm, 30cm, 40cm) to comprehensively evaluate the accuracy and reliability of the positioning system.

[0141] Step 4, Simulation Verification and Result Analysis:

[0142] In the simulation experiment of fusion positioning, the optimal signal filtering strategy was used for positioning, and a total of 1000 independent experiments were conducted. For example... Figure 7 As shown, the experimental results indicate that the system's RMSE is 0.1907 meters; the positioning accuracy reaches 81.3% within a 20-centimeter accuracy range and 97.0% within a 30-centimeter accuracy range, verifying the high-precision positioning performance of the network deployed by this invention.

[0143] In this specific embodiment, a fusion deployment method based on UWB and 5G-A base stations is proposed through integrated base station deployment and complementary positioning architecture design. This method combines an acute-angled triangular layout of UWB base stations with a diamond-shaped distribution of 5G-A base stations to achieve full spatial coverage and signal complementarity. By optimizing base station site selection, this method effectively reduces the impact of the geometrical precision factor (GDOP) and eliminates positioning blind spots in complex industrial environments where a single technology is used. Simultaneously, the system leverages the high-precision ranging capabilities of UWB and the wide-area coverage advantages of 5G-A to provide stable and reliable sub-meter or even centimeter-level high-precision positioning services across the entire area.

[0144] In this specific embodiment, a complementary fusion positioning system is constructed by rationally deploying UWB and 5G-A base stations: the UWB base stations adopt an acute-angled triangular layout to minimize the spatial geometric factor GDOP and achieve local centimeter-level high-precision positioning; the 5G-A base stations are distributed in a rhombus shape at the edge of the area to provide wide-area coverage. The two work together to solve the positioning blind spot problem inherent in single technologies, while also meeting the requirements for high-precision and wide-area positioning, significantly improving the stability and adaptability of terminal positioning in industrial internet scenarios.

[0145] In this specific embodiment, a fusion positioning method based on Chan's algorithm initial value estimation and LM nonlinear optimization is proposed. After obtaining the initial position estimate through Chan's algorithm, iterative optimization is performed using the LM algorithm, effectively suppressing the impact of ranging errors on the final positioning result. Simultaneously, a signal quality screening mechanism is introduced, selecting the data from the three optimal base stations for calculation, further reducing errors caused by multipath, non-line-of-sight, and other interference factors. In 1000 simulation experiments, the RMSE reached 0.1907m, the accuracy reached 81.3% at 20cm precision, and the accuracy reached as high as 97.0% at 30cm precision.

[0146] In addition to static positioning scenarios, traditional static positioning algorithms are unable to meet the real-time and continuous requirements of mobile devices in dynamic target tracking tasks, which leads to trajectory deviation problems. Existing fusion positioning algorithms also lack the ability to adapt to dynamic environmental changes (such as occlusion, changes in terminal battery level, etc.), which limits the stability of the overall system and the improvement of positioning accuracy.

[0147] In one embodiment of this application, the method is used to predict the motion trajectory of a located target; the method further includes:

[0148] Obtain the current time information corresponding to the current location information, and determine the current motion state information of the located target based on multiple current location information and the current time information;

[0149] The corresponding motion prediction algorithm is determined based on the current motion state information;

[0150] Based on the current location information and the current motion state information, the motion prediction algorithm determines the motion trajectory prediction information.

[0151] It should be noted that after obtaining the current location information through fusion positioning, the motion trajectory of the located target can also be predicted through motion prediction algorithms.

[0152] In one embodiment of this application, the current motion state information includes: the velocity, acceleration, and angular velocity of the located target at adjacent time points; the motion prediction algorithm includes: Kalman filter algorithm (KF), extended Kalman filter algorithm (EKF), and unscented Kalman filter algorithm (UKF); the specific process of "determining the corresponding motion prediction algorithm based on the current motion state information" can be further explained in conjunction with the following description.

[0153] As described in the following steps, a corresponding target motion model is determined based on the current motion state information; wherein, the target motion model includes at least one of: constant velocity model CV, constant acceleration model CA, constant rotation speed and velocity model CTRV, and constant rotation speed and acceleration model CTRA;

[0154] The corresponding motion prediction algorithm is determined based on the target motion model.

[0155] It should be noted that, considering that UWB and 5G-A base stations essentially only have distance measurement capabilities and cannot directly obtain target motion state information, it is necessary to use the position information of adjacent time points to locally estimate the dynamic state quantities such as velocity, acceleration and angular velocity of the located target, thereby determining the corresponding target motion model, and then selecting the corresponding motion prediction algorithm for trajectory prediction based on the target motion model.

[0156] The Kalman filter (KF) is a recursive state estimation algorithm for linear Gaussian systems, proposed by Rudolf E. Kalman in 1960. It provides optimal state estimates by minimizing the mean square value of the estimation error through an iterative prediction-update process, combining the system's dynamic model and observational data.

[0157] The Extended Kalman Filter (EKF) is an extension of the KF algorithm for nonlinear systems. It uses Taylor expansion to locally linearize the nonlinear model, thereby transforming the nonlinear problem into a linear one.

[0158] The Unscented Kalman Filter (UKF) is another nonlinear filtering method. It uses an Unscented Transform (UT) to directly approximate the statistical properties of the nonlinear function with a set of deterministic sampling points (Sigma points), thus avoiding explicit linearization.

[0159] The constant velocity (CV) model is a motion model that assumes the target moves at a constant speed and ignores the effects of acceleration and turning.

[0160] The constant acceleration (CA) model is a motion model that assumes the target moves with constant acceleration and the velocity changes linearly with time.

[0161] The Constant Turn Rate and Velocity (CTRV) model is a motion model that assumes the target moves at a constant speed and constant angular velocity, and is suitable for describing turning trajectories.

[0162] The Constant Turn Rate and Acceleration (CTRA) model is a motion model that assumes the target moves with a constant angular velocity and acceleration, and is suitable for describing acceleration or deceleration scenarios during turning.

[0163] In a specific embodiment of this application, the motion trajectory prediction of a located target can be achieved through simulation via the following steps:

[0164] Step 1, Simulation and Modeling of Linear Motion Systems:

[0165] Considering that UWB and 5G-A base stations essentially only have distance measurement capabilities and cannot directly obtain target motion state information, it is necessary to use the position information of adjacent time points to locally estimate the target's dynamic state quantities such as velocity, acceleration, and angular velocity.

[0166] For the CV motion model, assuming the target is moving at a constant velocity in a straight line, the velocity components of the target in each coordinate axis direction are calculated using the difference between the current and previous coordinates. The calculation method is as follows:

[0167]

[0168]

[0169] in, This represents the velocity component of the target in the x-direction. This represents the target's current x-coordinate value. This represents the x-coordinate value of the target at the previous moment. This represents the velocity component of the target in the y-direction. This represents the target's y-coordinate at the current moment. This represents the y-coordinate value of the target at the previous moment. It represents the time interval between the current moment and the previous moment.

[0170] For the CA model, assuming the target undergoes uniformly accelerated linear motion, the average velocity and acceleration are calculated by combining the current position information with the previous two time points to obtain the target state estimate. The calculation method is as follows:

[0171]

[0172]

[0173] in, This represents the acceleration component of the target in the x-direction. This represents the velocity component of the target in the x-direction at the current moment. This represents the velocity component of the target in the x-direction at the previous moment. This represents the acceleration component of the target in the y-direction. This represents the velocity component of the target in the y-direction at the current moment. This represents the velocity component of the target in the y-direction at the previous moment. It represents the time interval between the current moment and the previous moment.

[0174] The estimated velocity and acceleration are constructed into a state vector, which is then input into a linear KF framework. The KF framework is used for state prediction and observation updates to achieve real-time tracking of the target.

[0175] Step 2, Simulation and Modeling of Nonlinear Motion Systems:

[0176] For the CTRV model, which is applicable to scenarios where the target performs circular motion, the trajectory center is determined by three-point fitting, and then the target heading angle and rotational speed are estimated. For the CTRA model, which is applicable to scenarios of curvilinear accelerated motion, it is assumed that the target approximately satisfies the CTRV model in a short period of time. By estimating the angular velocity, the linear velocity and acceleration are inversely deduced.

[0177] Then, the EKF and UKF methods are introduced to adapt to the nonlinear system modeling. Among them, EKF completes local linearization by performing a first-order Taylor expansion on the nonlinear model; UKF adopts the Sigma-point sampling method, without calculating the Jacobian matrix, and can reach the level of Taylor second-order expansion in terms of accuracy. The embodiments of this application can automatically switch between the CTRV or CTRA model according to the change of the target acceleration, further enhancing the adaptive ability and robustness of the system.

[0178] Step 3, simulation verification and performance evaluation:

[0179] First, a simulation environment is constructed to verify the tracking performance of the proposed system. In a rectangular test area with an area of 35 m × 35 m, typical trajectory forms including linear motion, vertical turning, and irregular curve motion are designed. Using the target real-time position information provided in the public dataset as input, the positioning and tracking system is driven to conduct simulation experiments. The simulation results in the linear motion scenario are as Figure 8 shown. Figure 8 In it, parts (a), (b), and (c) respectively correspond to different local regions of the trajectory simulation curve. Among them, part (a) represents a local enlarged view of the linear motion stage in the region with coordinates {(x,y)∣5.8 < x < 16, 5.8 < y < 6}; part (b) represents a local enlarged view of the vertical turning motion stage in the region with coordinates {(x,y)∣16.6 < x < 17.8, 32 < y < 35}; part (c) represents a local enlarged view of the linear motion stage in the region with coordinates {(x,y)∣17.2 < x < 17.8, 10 < y < 30}.

[0180] In the linear motion stage, since KF is based on the linear assumption of the constant velocity CV model and highly coincides with the actual motion characteristics of the target, it shows the smallest positioning error. In contrast, EKF and UKF introduce unnecessary model approximation errors in scenarios with relatively small curvature changes due to the introduction of the nonlinear state transition model, resulting in a significant increase in trajectory deviation. After entering the vertical turning stage, the performance of the KF algorithm deteriorates due to its inability to adapt to the fast turning dynamics, and the performance of UKF is slightly better than that of EKF, reflecting a stronger adaptability to nonlinear dynamics.

[0181] Subsequently, a second set of simulation scenarios is set: in a rectangular area of 2.5 m × 3 m, the target moves along an irregular curved path. The simulation results are as Figure 9As shown.

[0182] In this scenario, the KF algorithm maintains a relatively small error when handling local polyline trajectories, while the UKF algorithm exhibits better robustness and stability when dealing with drastic curve changes, with its overall positioning error controlled within 0.3 m. The EKF algorithm, limited by the linearization error caused by the first-order Taylor expansion, shows significant positioning deviations in areas with drastic changes in path curvature, with a maximum error approaching 0.5 m.

[0183] The irregular curve scenario is further divided into multiple functional areas, and their error performance is as follows: Figure 10 As shown.

[0184] In region A (linear motion), all algorithms performed stably with minimal systematic error;

[0185] In region B (vertical corner), KF significantly outperforms UKF and EKF in terms of accuracy;

[0186] In region C (high-speed turnaround), the errors of all algorithms increased, but UKF was still able to keep the error within 0.3m;

[0187] In region D (where the curve changes drastically), the UKF maintains stable performance with an error consistently below 0.3 m, while the EKF exhibits significant error fluctuations, reaching a maximum of 0.5 m.

[0188] In this specific embodiment, a dynamic target tracking system is constructed by integrating multimodal dynamic target tracking with adaptive filtering algorithms. This system combines motion models such as CV, CA, CTRV, and CTRA with Kalman filtering algorithms such as KF, EKF, and UKF, enabling it to automatically select the optimal filtering strategy based on different motion states. State parameters such as velocity, acceleration, and angular velocity are estimated by collecting position information at adjacent time points, and a nonlinear state estimation method is introduced to improve trajectory prediction accuracy. The system exhibits strong robustness, still capable of position prediction using motion models even under non-line-of-sight or signal loss conditions, ensuring the continuity and real-time performance of tasks such as AGV scheduling and personnel tracking in industrial internet scenarios.

[0189] Meanwhile, based on static positioning, this invention constructs a dynamic target tracking system. Combining various motion models such as CV, CA, CTRV, and CTRA with Kalman filtering algorithms such as KF, EKF, and UKF, it can adaptively select the optimal filtering strategy according to different motion states. Even under conditions of signal loss or non-line-of-sight, it can predict the target position through the motion model, ensuring the continuity and robustness of the system. Experiments show that in irregular curved motion, the UKF error remains within 0.3m, which is superior to the 0.5m error performance of EKF, making it suitable for industrial internet applications with high real-time requirements such as AGV scheduling and personnel tracking.

[0190] In a specific embodiment of this application, reference is made to Figure 11 One of the core technical concepts of this invention lies in providing a robust, efficient, and highly adaptable high-precision positioning method integrating 5G-A and UWB in complex industrial internet scenarios. Through multimodal signal processing and dynamic optimization mechanisms, it solves problems related to multipath interference, signal obstruction, and energy consumption balance, achieving centimeter-level high-precision positioning to enable real-time accurate positioning and reliable tracking of terminal devices. In complex industrial environments, high-precision positioning technology plays a crucial supporting role in intelligent manufacturing and refined logistics management, meeting the demand for precise positioning in complex industrial environments. The method includes the following steps:

[0191] Step 1: Constructing a Static Fusion Positioning System and Conducting Simulation Verification: Based on the ranging radius and performance characteristics of UWB and 5G-A base stations, reasonable site selection and deployment are carried out to construct a complete static fusion positioning system. By optimizing the base station layout, UWB base stations are distributed in an acute-angled triangle, and 5G-A base stations are deployed in a diamond shape, achieving effective global positioning of the fusion positioning system. A static fusion positioning scheme combining the Chan algorithm and the LM algorithm is proposed. The first-step linear estimation solution of the Chan algorithm is used to provide the initial position estimate for the LM algorithm, and the positioning accuracy is improved through iterative updates. Simulations compare the positioning results using all ranging values ​​and the optimal ranging value. The root mean square error (RMSE) and confidence intervals are used to evaluate the accuracy and reliability of different positioning schemes.

[0192] Step Two: Build a Dynamic Target Tracking System and Conduct Simulation Verification: Based on the static positioning system, a dynamic target tracking system is constructed to address the motion state of equipment in industrial internet scenarios. Simulation methods for linear and nonlinear motion systems are designed separately, estimating state parameters such as velocity, acceleration, and angular velocity of moving objects by collecting coordinate information from adjacent positions. Combining motion models such as CV, CA, CTRV, and CTRA with algorithms such as linear Kalman filter (KF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), real-time tracking and position prediction of dynamic targets are achieved. Simulation experiments are conducted using publicly available datasets to verify the effectiveness and reliability of the system under different motion scenarios, and the performance of different Kalman filter algorithms under different motion modes is analyzed.

[0193] The above is a description of the method embodiments of this application. As for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For relevant parts, please refer to the description of the method embodiments.

[0194] Reference Figure 12 This application illustrates a fusion positioning device based on a 5G-A base station and a UWB base station according to an embodiment of the present application. The device involves a 5G-A base station and a UWB base station.

[0195] The device includes:

[0196] The base station deployment module 1210 is used to acquire environmental information of the target area to be deployed, and to determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station.

[0197] The network generation module 1220 is used to generate a deployment network based on the first deployment quantity and first deployment location information corresponding to the UWB base station and the second deployment quantity and second deployment location information corresponding to the 5G-A base station.

[0198] The signal acquisition module 1230 is used to acquire at least three valid communication signals corresponding to the target from the deployment network when the target to be located enters the target area; wherein the valid communication signals are emitted by the 5G-A base station and / or the UWB base station.

[0199] The positioning module 1240 is used to determine the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station.

[0200] In one embodiment of this application, the base station deployment module 1210 includes:

[0201] The first deployment submodule is used to determine the number of the first deployments and the location information of the first deployments based on the environmental information and the effective ranging radius, respectively.

[0202] The blind spot area determination submodule is used to determine the number of blind spot areas and the location information of blind spot areas based on the environmental information, the first deployment location information and the effective ranging radius;

[0203] The second deployment quantity determination submodule is used to determine the second deployment quantity based on the number of blind spot areas;

[0204] The second deployment location determination submodule is used to determine the second deployment location information based on the blind spot area location information.

[0205] In one embodiment of this application, the UWB base station includes an edge base station and a central base station; the first deployment submodule includes:

[0206] A deployment range determination unit is used to determine deployment range information based on the environmental information; wherein the deployment range includes at least one square plane;

[0207] A vertex position determination unit is used to determine the position information of the four vertices corresponding to the square plane based on the deployment range information.

[0208] The first sub-deployment unit is used to determine the number of first sub-deployments and the location information of the first sub-deployment corresponding to the edge base station based on the vertex location information.

[0209] The second sub-deployment quantity determination unit is used to determine the second sub-deployment quantity corresponding to the central base station based on the first sub-deployment quantity;

[0210] The second sub-deployment location determination unit is used to determine the second sub-deployment location information corresponding to the central base station based on the environmental information, the first sub-deployment location information, the effective ranging radius, and the number of second sub-deployments.

[0211] In one embodiment of this application, the number of second sub-deployments is twice the number of first sub-deployments; each edge base station corresponds to two central base stations; the second sub-deployment location determination unit includes:

[0212] The diagonal determination subunit is used to determine the diagonal information corresponding to the square plane based on the environmental information.

[0213] The second sub-deployment location determination sub-unit is used to determine the second sub-deployment location information based on the first sub-deployment location information, the effective ranging radius, and the diagonal information.

[0214] In one embodiment of this application, the blind spot region determination submodule includes:

[0215] An effective ranging area determination unit is used to determine effective ranging area information based on the first deployment location information and the effective ranging radius;

[0216] The blind spot area determination unit is used to determine the number of blind spot areas and the location information of blind spot areas based on the environmental information and the effective ranging area information.

[0217] In one embodiment of this application, the positioning module 1240 includes:

[0218] The signal strength determination submodule is used to determine the corresponding signal strength based on the valid communication signals when there are at least four valid communication signals.

[0219] The signal sorting submodule is used to sort the signal strengths and determine the three ranging target signals based on the sorting results.

[0220] The positioning submodule is used to determine the current position information based on the ranging target signal.

[0221] In one embodiment of this application, the device is used to predict the motion trajectory of a located target; the device further includes:

[0222] The current motion state information determination module is used to obtain the current time information corresponding to the current location information, and determine the current motion state information of the located target based on multiple current location information and the current time information;

[0223] A motion prediction algorithm determination module is used to determine the corresponding motion prediction algorithm based on the current motion state information;

[0224] The motion trajectory prediction module is used to determine motion trajectory prediction information based on the current location information and the current motion state information through the motion prediction algorithm.

[0225] In one embodiment of this application, the current motion state information includes: the velocity, acceleration, and angular velocity of the located target at adjacent time points; the motion prediction algorithm includes: Kalman filter algorithm (KF), extended Kalman filter algorithm (EKF), and unscented Kalman filter algorithm (UKF); the motion prediction algorithm determination module includes:

[0226] The target motion model determination submodule is used to determine the corresponding target motion model based on the current motion state information; wherein, the target motion model includes at least one of the following: constant velocity model CV, constant acceleration model CA, constant rotation speed and velocity model CTRV, and constant rotation speed and acceleration model CTRA;

[0227] The motion prediction algorithm determination submodule is used to determine the corresponding motion prediction algorithm based on the target motion model.

[0228] Reference Figure 13 This diagram illustrates a structural block diagram of a computer device according to an embodiment of this application. The computer device 12 is adapted to implement embodiments of the present invention and may specifically include the following:

[0229] The computer device 12 is manifested as a general-purpose computing device. Components of the computer device 12 may include, but are not limited to: one or more processing units 16, system memory 28, and a bus 18 connecting different system components (including system memory 28 and processing units 16). The computer device 12 may be a device connected to the bus.

[0230] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0231] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0232] System memory 28 may include computer system readable media in the form of volatile memory, such as RAM 30 (Random Access Memory) and / or cache 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). Although Figure 13 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0233] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0234] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through I / O interface 22 (input / output interface). Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network (e.g., the Internet)) through network adapter 20. Figure 13 As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 13 As not shown, it can be used in conjunction with computer device 12 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0235] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing a fusion positioning method based on 5G-A base station and UWB base station provided in any embodiment of the present invention.

[0236] That is, when the program is executed by the processor, it performs the following: acquiring environmental information of the target area to be deployed, and determining the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station;

[0237] A deployment network is generated based on the first deployment quantity and first deployment location information corresponding to the UWB base stations and the second deployment quantity and second deployment location information corresponding to the 5G-A base stations;

[0238] When the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station;

[0239] The current location information of the target to be located is determined based on the valid communication signal and the deployment location information of the corresponding base station.

[0240] Computer device 12 is merely an example and should not impose any limitation on the functionality and scope of use of embodiments of the present invention.

[0241] One embodiment of this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a fusion positioning method based on 5G-A base stations and UWB base stations as provided in any embodiment of this application.

[0242] That is, when the program is executed by the processor, it performs the following: acquiring environmental information of the target area to be deployed, and determining the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station;

[0243] A deployment network is generated based on the first deployment quantity and first deployment location information corresponding to the UWB base stations and the second deployment quantity and second deployment location information corresponding to the 5G-A base stations;

[0244] When the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station;

[0245] The current location information of the target to be located is determined based on the valid communication signal and the deployment location information of the corresponding base station.

[0246] Computer storage media may take the form of any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, RAM, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable CD-ROM, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0247] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0248] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, radio frequency (RF), etc., or any suitable combination thereof.

[0249] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LANs or WANs—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0250] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0251] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. 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 terminal device that includes said element.

[0252] The above provides a detailed description of the fusion positioning method and apparatus based on 5G-A base stations and UWB base stations provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A fusion positioning method based on 5G-A base stations and UWB base stations, characterized in that, The method involves 5G-A base stations and UWB base stations; The method includes: Obtain environmental information of the target area to be deployed, and determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station; A deployment network is generated based on the first deployment quantity and first deployment location information corresponding to the UWB base stations and the second deployment quantity and second deployment location information corresponding to the 5G-A base stations; wherein, three UWB base stations are deployed in a group and arranged in an equilateral triangle at the vertices of the triangle, the UWB base stations include at least one group, and the side length of the equilateral triangle is equal to the effective ranging radius of the UWB base stations. When the target to be located enters the target area, at least three valid communication signals corresponding to the target to be located are obtained from the deployment network; wherein, the valid communication signals are emitted by the 5G-A base station and / or the UWB base station; The current location information of the target to be located is determined based on the valid communication signal and the deployment location information of the corresponding base station.

2. The method according to claim 1, characterized in that, The step of acquiring environmental information of the target area to be deployed, and determining the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station, respectively, includes: The first deployment quantity and the first deployment location information are determined based on the environmental information and the effective ranging radius, respectively. The number of blind spot areas and the location information of the blind spot areas are determined based on the environmental information, the first deployment location information, and the effective ranging radius. The second deployment quantity is determined based on the number of blind spot areas; The second deployment location information is determined based on the location information of the blind spot area.

3. The method according to claim 2, characterized in that, The UWB base station includes edge base stations and central base stations; the step of determining the first deployment quantity and the first deployment location information based on the environmental information and the effective ranging radius includes: The deployment range information is determined based on the environmental information; wherein the deployment range includes at least one square plane; The position information of the four vertices corresponding to the square plane is determined based on the deployment range information; The number of first sub-deployments and the location information of the first sub-deployments corresponding to the edge base station are determined based on the vertex location information. The number of second sub-deployments corresponding to the central base station is determined based on the number of the first sub-deployments; The location information of the second sub-deployment corresponding to the central base station is determined based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of the second sub-deployments.

4. The method according to claim 3, characterized in that, The number of the second sub-deployments is twice the number of the first sub-deployments; each edge base station corresponds to two central base stations; the step of determining the location information of the second sub-deployment corresponding to the central base station based on the environmental information, the location information of the first sub-deployment, the effective ranging radius, and the number of the second sub-deployments includes: Based on the environmental information, determine the diagonal information corresponding to the square plane; The second sub-deployment location information is determined based on the first sub-deployment location information, the effective ranging radius, and the diagonal information.

5. The method according to claim 4, characterized in that, The step of determining the number of blind spot areas and the location information of blind spot areas based on the environmental information, the first deployment location information, and the effective ranging radius includes: The effective ranging area information is determined based on the first deployment location information and the effective ranging radius; The number and location information of blind spots are determined based on the environmental information and the effective ranging area information.

6. The method according to claim 1, characterized in that, The step of determining the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station includes: When there are at least four valid communication signals, the corresponding signal strength is determined based on the valid communication signals. The signal strengths are sorted, and three ranging target signals are determined based on the sorting results; The current position information is determined based on the ranging target signal.

7. The method according to claim 1, characterized in that, The method is used to predict the trajectory of a located target; the method further includes: Obtain the current time information corresponding to the current location information, and determine the current motion state information of the located target based on multiple current location information and the current time information; The corresponding motion prediction algorithm is determined based on the current motion state information; Based on the current location information and the current motion state information, the motion prediction algorithm determines the motion trajectory prediction information.

8. The method according to claim 7, characterized in that, The current motion state information includes: the velocity, acceleration, and angular velocity of the located target at adjacent time points; the motion prediction algorithm includes: Kalman filter algorithm (KF), extended Kalman filter algorithm (EKF), and unscented Kalman filter algorithm (UKF); the step of determining the corresponding motion prediction algorithm based on the current motion state information includes: The corresponding target motion model is determined based on the current motion state information; wherein, the target motion model includes at least one of the following: constant velocity model CV, constant acceleration model CA, constant rotation speed and velocity model CTRV, and constant rotation speed and acceleration model CTRA; The corresponding motion prediction algorithm is determined based on the target motion model.

9. A fusion positioning device based on 5G-A base station and UWB base station, characterized in that, The device relates to 5G-A base stations and UWB base stations; The device includes: The base station deployment module is used to acquire environmental information of the target area to be deployed, and to determine the first deployment quantity and first deployment location information of the UWB base station and the second deployment quantity and second deployment location information of the 5G-A base station based on the environmental information and the effective ranging radius of the UWB base station. A deployment network generation module is used to generate a deployment network based on the first deployment quantity and first deployment location information corresponding to the UWB base stations and the second deployment quantity and second deployment location information corresponding to the 5G-A base stations; wherein, three UWB base stations are deployed in a group and arranged in an equilateral triangle at the vertices of the triangle, the UWB base stations include at least one group, and the side length of the equilateral triangle is equal to the effective ranging radius of the UWB base stations. The signal acquisition module is used to acquire at least three valid communication signals corresponding to the target from the deployment network when the target to be located enters the target area; wherein the valid communication signals are emitted by the 5G-A base station and / or the UWB base station; The positioning module is used to determine the current location information of the target to be located based on the valid communication signal and the deployment location information of the corresponding base station.

10. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 8.

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