Multimodal multi-user positioning composition and beam management method, device and storage medium

Abstract

The present application provides a multimodal multi-user positioning composition and beam management method, device and storage medium, including estimating the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to each target user, wherein each target user completes an initial self-position estimate and environmental feature estimate in combination with its own IMU measurement value, and feeds back to the base station; the base station or cloud performs feature fusion based on the environmental feature estimate of each target user, constructs a global radio map, and generates radio map prior information for each target user; obtains the perception results of a fixed binocular camera, and the base station or cloud fuses the initial self-position estimate based on the perception results to obtain the final user position estimate; the base station or cloud generates a multi-user beam management angle range based on the global radio map and the final user position estimate.

Description

Technical Field

[0001] The present application relates to the interdisciplinary technical field of millimeter wave beam management and sensing technology, and in particular to a multimodal multi-user positioning composition and beam management method, device and storage medium. Background Art

[0002] The integration of communication and perception is developing into a key technology for millimeter-wave wireless communications. Simultaneous localization and mapping (SLM) has become a key framework for millimeter-wave communication systems. Its core functionality is to simultaneously locate users and create radio maps, which typically encapsulate key environmental features describing millimeter-wave multipath propagation. Implementation options for SLM range from geometry-based approaches to randomized finite set methods and belief propagation algorithms. Despite its potential, SLM is currently typically deployed at the user end, which can lead to frequent radio map updates due to the mobility of millimeter-wave devices and the limited sensing capabilities of terminals.

[0003] To address the above challenges, the concept of multi-user simultaneous localization and mapping has become an important research direction. The method revolves around three key steps: first, the client performs simultaneous localization and mapping locally, and then uploads its estimated wireless environment features to the base station or the cloud; next, the base station or the cloud fuses these line environment feature estimates to create a global radio map; finally, the user downloads the global radio map to improve their local simultaneous localization and mapping process. The dynamic nature of the user means that they will encounter a variety of environmental features, resulting in local radio maps that can only partially overlap. Coupled with the changes in user access time and sensing capabilities, these conditions bring significant design challenges to the multi-user simultaneous localization and mapping technology system.

[0004] Millimeter-wave communication systems utilize directional transmission through beamforming, a technique that, while effective, requires significant beam management overhead. Recent research has investigated using historical beam measurements, user-side motion, and environmental characteristics to simplify beam management and reduce overhead. Multimodal communication and perception-integrated systems, equipped with a variety of sensors, have introduced innovative beam tracking strategies, promising improved accuracy by leveraging additional sensor data. Notably, binocular cameras are considered adept at capturing user-side motion and are often used to assist in direct path tracking.

[0005] However, there is little research on the integration of camera perception results with multi-user simultaneous localization and composition techniques. At the same time, few studies have considered how to use the global radio map and user position estimation of multi-user simultaneous localization and composition techniques to form multi-user beam management. Summary of the Invention

[0006] Each exemplary embodiment of the present application provides a multi-modal multi-user positioning composition and beam management method, which has at least the technical effect of simultaneously obtaining multi-user position estimates and radio maps and completing multi-user beam management.

[0007] Each exemplary embodiment of the present application provides a multi-modal multi-user positioning patterning and beam management method, comprising the following steps:

[0008] Each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user. It then uses its own IMU measurement values ​​to complete its initial position estimation and environmental feature estimation, and then feeds back to the base station.

[0009] The base station or cloud performs feature fusion based on the estimated environmental features of each target user to construct a global radio map, and generates radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental features fed back by the user;

[0010] The base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, integrates the initial self-position estimates of the target users to obtain the final user position estimate;

[0011] The base station or the cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate;

[0012] Each target user downloads radio map prior information from the base station or the cloud, updates its current location estimate and the current environment feature estimate, and simultaneously downloads the beam management angle range for beam management.

[0013] In another aspect of the present application, a multi-modal multi-user positioning composition and beam management method and apparatus is also proposed, comprising:

[0014] The initial estimation generation module is configured so that each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user, combines its own IMU measurement value to complete the initial self-position estimation and environmental feature estimation, and feeds it back to the base station;

[0015] A global radio map construction module is configured to perform feature fusion based on the estimated environmental characteristics of each target user at the base station or the cloud to construct a global radio map, and generate radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental characteristics fed back by the user;

[0016] A perception module is configured to obtain perception results from a fixed binocular camera at a base station or in the cloud, and based on the perception results, fuses the initial self-position estimates of each target user to obtain a final user position estimate;

[0017] a beam management angle generation module configured to generate a multi-user beam management angle range at a base station or a cloud based on the global radio map and the end user position estimate;

[0018] The update module is configured to download radio map prior information from the base station or the cloud for each target user, update the current estimate of its own position and the current estimate of environmental characteristics, and simultaneously download the beam management angle range to perform beam management.

[0019] In another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0020] In another aspect of the present application, an electronic device is proposed, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0021] This application has the following beneficial effects:

[0022] 1. Most existing radio map construction methods rely on single-user implementation at the user end. This solution considers the fusion of multi-user composition results, resulting in a more accurate global radio map composition result.

[0023] 2. Existing mobile communication user positioning solutions are mostly based on radio frequency pilot estimation. This solution considers the fusion and optimization of multi-user positioning results based on binocular cameras, resulting in more accurate multi-user positioning results.

[0024] 3. Most existing multi-user beam management schemes are based on channel estimation results. This scheme considers multi-user beam management based on radio maps and user position estimation, which can achieve higher multi-user and spectrum efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 This is a schematic diagram of the overall process of this application;

[0027] Figure 2 This is a flowchart of the application;

[0028] Figure 3 This is a schematic diagram of an optional electronic device structure of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the preferred embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0030] like Figure 1 and Figure 2 As shown, a multi-modal multi-user positioning composition and beam management method is characterized by comprising the following steps:

[0031] In step S1, each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user. It then uses its own IMU measurements to complete its initial position estimation and environmental feature estimation, and then feeds back to the base station.

[0032] S2: The base station or the cloud performs feature fusion based on the estimated environmental features of each target user to construct a global radio map, and generates radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental features fed back by the user;

[0033] S3, the base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, integrates the initial self-position estimates of each target user to obtain the final user position estimate;

[0034] S4, the base station or the cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate;

[0035] In S5, each target user downloads radio map prior information from the base station or the cloud, updates its current location estimate and the current environment feature estimate, and simultaneously downloads the beam management angle range to perform beam management.

[0036] In an optional embodiment, if Figure 1 As shown, the estimation of the multipath parameters of the propagation channel from the base station to the user in step S1 specifically includes the following:

[0037] The departure angle AoD of the base station-user downlink pilot signal leaving the base station and the arrival angle AoA of the signal arriving at the user end are estimated by the probability model corresponding to this parameter:

[0038]

[0039] in and They are AoA and AoD estimates, both modeled as Gaussian random variables. The user IMU measurement value includes its own position and velocity prediction value. Based on the channel multipath parameters and IMU measurement, the user can complete the estimation of its own position and environmental characteristics, which is expressed as

[0040]

[0041] in is the user location estimate,

[0042] The horizontal and vertical coordinates of the two are modeled as Gaussian random variables. The user's estimation of the wireless environment characteristics can form a local radio map, which is represented by the set The set contains multiple environmental features p j,l′ , as shown below

[0043]

[0044] where x VA,j,l′ is the characteristic position, r j,l′ is the feature confidence.

[0045] In an optional embodiment, if Figure 1-Figure 3 As shown, in step S2, the global radio map stored in the base station or the cloud is represented by a set Q(t) of multiple global environmental features, as follows

[0046]

[0047] Among them, the global environment feature q i Includes: Feature position x VA,i , feature position estimation variance The feature visibility vector v for the user i , feature confidence r i and the time t at which the feature appears i .

[0048] In an optional implementation, the fusion of environmental features fed back by multiple users is divided into an initial stage and an optimization stage. In the initial stage, the environmental features of each user are estimated based on the distance between each other to achieve data association and Gaussian fusion. In the optimization stage, the environmental features of each user are estimated based on prior information to achieve data association and Gaussian fusion. For environmental features with successful data association, the fusion process is as follows:

[0049]

[0050] v cand [j]=r j,l′

[0051] r cand =max{v cand}

[0052] t cand =t

[0053] For the environmental features that are not successfully associated, their confidence is corrected. After the fusion is completed, the set Q(t) is restricted by feature pruning, as shown below

[0054]

[0055] where r cut is the pruning threshold.

[0056] In an optional embodiment, in step S3, the base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, integrates the initial self-position estimate to obtain the final user position estimate, specifically including the following steps:

[0057] The binocular camera perception results are first detected by YOLO on the RGB photos, and then the target is located based on the binocular imaging principle in combination with the depth photos. At the same time, the target positioning result error is corrected through offline training. can be modeled as a lookup table function:

[0058]

[0059] Where (u, v) represents the center pixel coordinate of the target, u cell With v cell Based on the positioning error modeling, the multi-target positioning estimation results It can be described as a Gaussian probability distribution model with known variance:

[0060]

[0061] The multi-user positioning results and the camera multi-target positioning results are associated with each other based on the distance and Gaussian fusion is performed to obtain the final multi-user position estimation. The fusion process after correct data association is described as

[0062] Among them I j is the target index successfully associated with the j-th user.

[0063] In an optional embodiment, in steps S4 and S5, all available beam pairing prior information for each user is first generated based on the user position estimate and the global radio map, forming a prior information set U j(t), which includes the prior information u of multiple single paths j,l , described as

[0064]

[0065] in is the channel single path parameter calculated based on the user location and wireless environment characteristics, S j,l is the angle range calculated based on the two probability models, The single-path gain is calculated based on the signal path loss model. On this basis, the multipath beam pairing prior information of each user is screened one by one in a way that maximizes the user sum rate, and the single-path beam pairing prior information u is assigned to each user. j,opt Based on this prior information, each user completes the angle-limited beam tracking based on the single-path beam pairing prior information and obtains the optimal beam pairing. The beam tracking codebook is expressed as

[0066]

[0067] in For the quantized single-path beam angle, after completing beam management, each user will use the downloaded global radio map as a priori to complete self-positioning and environmental feature estimation at the next moment.

[0068] According to another aspect of the present application, a multi-user positioning patterning and beam management device is also provided, including:

[0069] The initial estimation generation module is configured so that each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user, combines its own IMU measurement value to complete the initial self-position estimation and environmental feature estimation, and feeds it back to the base station;

[0070] A global radio map construction module is configured to perform feature fusion based on the estimated environmental characteristics of each target user at the base station or the cloud to construct a global radio map, and generate radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental characteristics fed back by the user;

[0071] A perception module is configured to obtain perception results from a fixed binocular camera at a base station or in the cloud, and based on the perception results, fuse the initial self-position estimates of each target user to obtain a final user position estimate;

[0072] a beam management angle generation module configured to generate a multi-user beam management angle range at a base station or a cloud based on the global radio map and the end user position estimate;

[0073] The update module is configured to download radio map prior information from the base station or the cloud for each target user, update the current estimate of its own position and the current estimate of environmental characteristics, and simultaneously download the beam management angle range to perform beam management.

[0074] According to another aspect of the embodiment of the present application, an electronic device for a multi-modal multi-user positioning pattern and beam management method is also provided, and the electronic device can be but is not limited to being applied in a server. Figure 3 As shown, the electronic device includes a memory 302 and a processor 304. The memory 302 stores a computer program, and the processor 304 is configured to execute the steps in any of the above method embodiments through the computer program.

[0075] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0076] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0077] In step S1, each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user. It then uses its own IMU measurements to complete its initial position estimation and environmental feature estimation, and then feeds back to the base station.

[0078] S2: The base station or the cloud performs feature fusion based on the estimated environmental features of each target user to construct a global radio map, and generates radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental features fed back by the user;

[0079] S3, the base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, integrates the initial self-position estimates of each target user to obtain the final user position estimate;

[0080] S4, the base station or the cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate;

[0081] In S5, each target user downloads radio map prior information from the base station or the cloud, updates its current location estimate and the current environment feature estimate, and simultaneously downloads the beam management angle range to perform beam management.

[0082] Alternatively, those skilled in the art will appreciate that Figure 3 The structure shown is for illustration only, and the electronic device may also be a terminal device such as a mobile Internet device (MID) or a PAD. Figure 3It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 3 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 3 Different configurations shown.

[0083] The memory 302 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 302 may further include a memory remotely located relative to the processor 304, and these remote memories may be connected to the terminal via a network. Examples of the aforementioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. As an example, Figure 3 As shown, the memory 302 may include, but is not limited to, an initial estimation generation module and a global radio map construction module, etc., which will not be described in detail in this example.

[0084] Optionally, the transmission device 306 is configured to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one embodiment, the transmission device 306 includes a network interface controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In one embodiment, the transmission device 306 is a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0085] In addition, the electronic device further includes: a display 308 for displaying current simulation results; and a connection bus 310 for connecting various module components in the electronic device.

[0086] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0087] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0088] In step S1, each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user. It then uses its own IMU measurements to complete its initial position estimation and environmental feature estimation, and then feeds back to the base station.

[0089] S2: The base station or the cloud performs feature fusion based on the estimated environmental features of each target user to construct a global radio map, and generates radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental features fed back by the user;

[0090] S3, the base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, integrates the initial self-position estimates of each target user to obtain the final user position estimate;

[0091] S4, the base station or the cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate;

[0092] In S5, each target user downloads radio map prior information from the base station or the cloud, updates its current location estimate and the current environment feature estimate, and simultaneously downloads the beam management angle range to perform beam management.

[0093] Optionally, the storage medium is further configured to store a computer program for executing the steps included in the method in the above embodiment, which will not be described in detail in this embodiment.

[0094] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing the hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0095] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0096] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above-mentioned computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application.

[0097] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0100] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] The above is only a preferred embodiment of the present application. It should be noted that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be considered as the scope of protection of the present application.

[0102] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications based on these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-modal multi-user positioning patterning and beam management method, characterized in that: The steps include: Each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user. It then uses its own IMU measurement values ​​to complete its initial position estimation and environmental feature estimation, and then feeds back to the base station. The base station or the cloud performs feature fusion based on the estimated environmental features of each target user to construct a global radio map, and generates radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental features fed back by the user; The base station or the cloud obtains the perception results of the fixed binocular camera, and based on the perception results, fuses the initial self-position estimates of each target user to obtain the final user position estimate; The base station or the cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate; Each target user downloads radio map prior information from the base station or the cloud, updates its current location estimate and the current environment feature estimate, and simultaneously downloads the beam management angle range for beam management.

2. The multi-modal multi-user positioning patterning and beam management method according to claim 1, characterized in that: The estimating of the multipath parameters of the propagation channel from the base station to the user specifically includes the following steps: The departure angle AoD of the base station-user downlink pilot signal leaving the base station and the arrival angle AoA of the signal arriving at the user end are estimated by the probability model corresponding to this parameter: in and They are AoA and AoD estimation respectively, both are modeled as Gaussian random variables. The user IMU measurement value includes its own position and velocity prediction value. Based on the channel multipath parameters and IMU measurement, the user can complete the estimation of its own position and environmental characteristics, which is expressed as in is the user location estimate, The horizontal and vertical coordinates of the two are modeled as Gaussian random variables. The user's estimation of the wireless environment characteristics can form a local radio map, which is represented by the set The set contains multiple environmental features p j,l′ , as shown below where x VA,j,l′ is the characteristic position, r j,l′ is the feature confidence.

3. The multi-modal multi-user positioning patterning and beam management method according to claim 2, characterized in that: The global radio map stored in the base station or cloud is represented by a collection of multiple global environmental features. as follows Among them, the global environment feature q i Includes: Feature position x VA,i , feature position estimation variance The feature visibility vector v for the user i , feature confidence r i and the time t at which the feature appears i .

4. The multi-modal multi-user positioning patterning and beam management method according to claim 3, characterized in that: The fusion of environmental features fed back by multiple users is divided into the initial stage and the optimization stage, in which: In the initial stage, the environmental features of each user are estimated based on the distance between each other to achieve data association and Gaussian fusion. In the optimization stage, the environmental features of each user are estimated based on prior information to achieve data association and Gaussian fusion. For environmental features with successful data association, the fusion process is as follows v cand [j]=r j,l′ r cand =max{v cand } t cand =t For the environmental features that are not successfully associated, their confidence is corrected. After the fusion is completed, the set is pruned by feature pruning. Make restrictions as follows where r cut is the pruning threshold.

5. The multi-modal multi-user positioning patterning and beam management method according to claim 4, characterized in that: The base station or cloud obtains the perception results of the fixed binocular camera, and based on the perception results, fuses the initial self-position estimate to obtain the final user position estimate, specifically including the following steps: The binocular camera perception results are first detected by YOLO on the RGB photo, and then the target is located based on the binocular imaging principle in combination with the depth photo. At the same time, the target positioning result error is corrected through offline training. can be modeled as a lookup table function: Where (u, v) represents the center pixel coordinate of the target, u cell With v cell The segmentation intervals of the horizontal and vertical coordinates are respectively, based on the positioning error modeling, multi-target positioning estimation results It can be described as a Gaussian probability distribution model with known variance: The multi-user positioning results and the camera multi-target positioning results are associated with each other based on the distance and Gaussian fusion is performed to obtain the final multi-user position estimation. The fusion process after correct data association is described as Among them I j is the target index successfully associated with the j-th user.

6. The multi-modal multi-user positioning patterning and beam management method according to claim 5, characterized in that: The base station or cloud generates a multi-user beam management angle range based on the global radio map and the end user position estimate; each target user downloads radio map prior information from the base station or cloud, updates its current position estimate and current environment feature estimate, and simultaneously downloads the beam management angle range to perform beam management, specifically including the following steps: First, all available beam pairing prior information for each user is generated based on the user position estimate and the global radio map, forming a prior information set This includes the prior information u of multiple single paths j,l , described as in is the channel single path parameter calculated based on the user location and wireless environment characteristics, S j,l is the angle range calculated based on the two probability models, The single-path gain is calculated based on the signal path loss model. On this basis, the multipath beam pairing prior information of each user is screened one by one in a way to maximize the user sum rate, and the single-path beam pairing prior information u is assigned to each user. j,opt Based on this prior information, each user completes the angle-limited beam tracking based on the single-path beam pairing prior information and obtains the optimal beam pairing. The beam tracking codebook is expressed as in For the quantized single-path beam angle, after completing beam management, each user will use the downloaded global radio map as a priori to complete self-positioning and environmental feature estimation at the next moment.

7. A multi-modal multi-user positioning patterning and beam management device, applying the method according to any one of claims 1 to 6, characterized in that: include: The initial estimation generation module is configured so that each target user estimates the multipath parameters of the propagation channel from the base station to the user based on the downlink pilot signal from the base station to the user, combines its own IMU measurement value to complete the initial self-position estimation and environmental feature estimation, and feeds it back to the base station; A global radio map construction module is configured to perform feature fusion based on the estimated environmental characteristics of each target user at the base station or the cloud to construct a global radio map, and generate radio map prior information for each target user based on the initial self-position estimate of each target user and the estimated environmental characteristics fed back by the user; A perception module is configured to obtain perception results from a fixed binocular camera at a base station or in the cloud, and based on the perception results, fuse the initial self-position estimates of each target user to obtain a final user position estimate; a beam management angle generation module configured to generate a multi-user beam management angle range at a base station or a cloud based on the global radio map and the end user position estimate; The update module is configured to download radio map prior information from the base station or the cloud for each target user, update the current estimate of its own position and the current estimate of environmental characteristics, and simultaneously download the beam management angle range to perform beam management.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.

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