Multi-modal multi-user positioning composition and beam management method and device and storage medium

Abstract

The invention provides a multi-mode multi-user positioning composition and beam management method and device and a storage medium, and the method comprises the steps: estimating the multipath parameters of a propagation channel from a base station to a user according to the downlink pilot signal of each target user from the base station to the user, each target user completes initial self position estimation and environment feature estimation in combination with a self IMU measurement value, and feeds back to the base station; the base station or the cloud performs feature fusion based on the environment feature estimation of each target user, constructs a global radio map, and generates radio map prior information of each target user; a sensing result of the fixed binocular camera is obtained, and the base station or the cloud end fuses the initial self-position estimation based on the sensing result to obtain final user position estimation; and the base station or the cloud generates a multi-user beam management angle range according to the global radio map and the final user position estimation.

Description

Technical Field

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

[0002] Communication and perception integration is developing into a key technology for millimeter wave wireless communications. Among them, simultaneous positioning and mapping technology has become one of the key frameworks of millimeter wave communication systems. The core function of simultaneous positioning and mapping technology is to simultaneously locate users and create radio maps, which usually encapsulate key environmental features describing millimeter wave multipath propagation. The implementation schemes of simultaneous positioning and mapping technology range from geometry-based methods to random finite set methods and belief propagation algorithms. Despite its potential, simultaneous positioning and mapping technology is currently usually deployed at the user end, which may lead to frequent updates of radio maps due to the mobility of millimeter wave devices and limited sensing capabilities of terminals.

[0003] To address the above challenges, the concept of multi-user simultaneous localization mapping has become an important research direction. The method revolves around three key steps: first, the client performs simultaneous localization 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 mapping process. The dynamic nature of the user means that they will encounter a variety of environmental features, resulting in only partial overlap of local radio maps. Coupled with the changes in user access time and sensing capabilities, these conditions bring significant design challenges to the multi-user simultaneous localization mapping system.

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

[0005] However, there are few studies on the fusion design of camera perception results and multi-user simultaneous localization and composition technology. At the same time, few studies consider how to use the global radio map of multi-user simultaneous localization and composition technology and user position estimation 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, so as to at least have the technical effect of simultaneously obtaining multi-user position estimates, 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, and completes the initial self-position estimation and environmental feature estimation based on its own IMU measurement value, and feeds back to the base station;

[0009] 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 of 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, fuses 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 the current estimate of its own position and the current environmental characteristics, and simultaneously downloads the beam management angle range to perform beam management.

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

[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, completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and feeds back to the base station;

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

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

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

[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 estimate of the current 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 a computer program is stored in the storage medium, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.

[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 scheme considers the fusion of multi-user composition results, and the resulting global radio map composition result has higher accuracy;

[0023] 2. Most of the existing mobile communication user positioning solutions are based on RF pilot estimation. This solution considers the fusion optimization of multi-user positioning results based on binocular cameras, and the multi-user positioning results obtained are more accurate;

[0024] 3. Most of the existing multi-user beam management schemes are based on channel estimation results. This scheme considers multi-user beam management based on radio map 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 the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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] 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, and completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and 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, constructs a global radio map, and generates radio map prior information of each target user based on the initial self-position estimate and the estimated environmental features of each target user 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, fuses 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 according to the global radio map and the end user position estimate;

[0035] S5, each target user downloads radio map prior information from the base station or the cloud, updates the current estimate of its own position and the current environmental characteristics, 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, the probability model corresponding to this parameter estimation is:

[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, 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 estimate of the wireless environment characteristics can form a local radio map, which is represented as a 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 location estimation variance The feature visibility vector v to 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 correspondence 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 implementation manner, in step S3, the base station or the cloud obtains the perception result of the fixed binocular camera, and based on the perception result, fuses the initial self-position estimate to obtain the step of estimating the final user position, specifically including the following:

[0057] The stereo camera perception result is first detected by YOLO on the RGB photo, and then the target is located based on the stereo 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:

[0058]

[0059] Where (u, v) represents the center pixel coordinates of the target, u cell With v cell are the segmentation intervals of the horizontal and vertical coordinates respectively. Based on positioning error modeling, 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 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 implementation, in steps S4 and S5, firstly, all available beam pairing prior information of each user is generated based on the user position estimation and the global radio map to form a prior information set U j (t), which includes the prior information u of multiple single pathsj,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, is the single-path gain inferred based on the signal path loss model. On this basis, based on the method of maximizing the user sum rate, the multipath beam pairing prior information of each user is screened one by one, and the single-path beam pairing prior information u is allocated 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 best beam pairing. The beam tracking codebook is expressed as

[0066]

[0067] in For the quantized single-path beam angle, after completing the beam management, each user will use the downloaded global radio map as a priori to complete the 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, completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and feeds back to the base station;

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

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

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

[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 estimate of the current 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 perform the following steps through a computer program:

[0077] 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, and completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and 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, constructs a global radio map, and generates radio map prior information of each target user based on the initial self-position estimate and the estimated environmental features of each target user 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, fuses 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 according to the global radio map and the end user position estimate;

[0081] S5, each target user downloads radio map prior information from the base station or the cloud, updates the current estimate of its own position and the current environmental characteristics, and simultaneously downloads the beam management angle range to perform beam management.

[0082] Alternatively, a person skilled in the art may understand 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 3The structure of the electronic device is not limited. Figure 3 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 3 Different configurations are 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 examples, 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 above-mentioned 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 used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 306 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0085] In addition, the electronic device further comprises: a display 308 for displaying the current simulation result; 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] 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, and completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and 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, constructs a global radio map, and generates radio map prior information of each target user based on the initial self-position estimate and the estimated environmental features of each target user 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, fuses 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 according to the global radio map and the end user position estimate;

[0092] S5, each target user downloads radio map prior information from the base station or the cloud, updates the current estimate of its own position and the current environmental characteristics, and simultaneously downloads the beam management angle range to perform beam management.

[0093] Optionally, the storage medium is also 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 hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0095] The serial numbers of the above-mentioned 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 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 may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0097] In the above embodiments of the present application, the description of each embodiment has its own emphasis. 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 the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that 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, which can be electrical or other forms.

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

[0100] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of 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 principle of the present application, and these improvements and modifications should also be regarded 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, once knowing the basic creative concept, can make other changes and modifications according to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling 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 it. 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 recorded 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 composition 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, and completes the initial self-position estimation and environmental feature estimation based on its own IMU measurement value, and 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 of each target user based on the initial self-position estimate and the estimated environmental features of each target user 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 the current estimate of its own position and the current environmental characteristics, and simultaneously downloads the beam management angle range to perform 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, the probability model corresponding to this parameter estimation is: 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, 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 estimate of the wireless environment characteristics can form a local radio map, which is represented as a 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 the 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 location estimation variance The feature visibility vector v to 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 feature estimation of each user is based on the distance between each other to achieve data association and Gaussian fusion. In the optimization stage, the environmental feature estimation of each user is based on prior information to achieve data association correspondence and Gaussian fusion. For the 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 collection is pruned by feature pruning. Make restrictions as follows where r cut is the pruning threshold.

5. The multi-modal multi-user positioning composition and beam management method according to claim 4, characterized in that: The base station or cloud obtains the perception result of the fixed binocular camera, and based on the perception result, fuses the initial self-position estimate to obtain the step of final user position estimate, specifically including the following: The stereo camera perception result is first detected by YOLO on the RGB photo, and then the target is located based on the stereo 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 coordinates of the target, u cell With v cell are the segmentation intervals of the horizontal and vertical coordinates respectively. Based on 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 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 the cloud generates a multi-user beam management angle range according to 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 the current estimate of its own position and the estimate of the current environmental characteristics, and downloads the beam management angle range at the same time, and performs beam management steps, which specifically include the following: First, based on the user position estimation and the global radio map, all available beam pairing prior information for each user is generated to form 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, is the single-path gain inferred based on the signal path loss model. On this basis, based on the method of maximizing the user sum rate, the multipath beam pairing prior information of each user is screened one by one, and the single-path beam pairing prior information u is allocated 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 best beam pairing. The beam tracking codebook is expressed as in For the quantized single-path beam angle, after completing the beam management, each user will use the downloaded global radio map as a priori to complete the self-positioning and environmental feature estimation at the next moment.

7. A multi-modal multi-user positioning patterning and beam management device, using the method according to 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, completes the initial self-position estimation and environmental feature estimation in combination with its own IMU measurement value, and feeds back to the base station; A global radio map construction module is configured to perform feature fusion based on the environmental feature estimation of each target user by the base station or the cloud to construct a global radio map, and generate radio map prior information of each target user according to the initial self-position estimation and the environmental feature estimation of each target user fed back by the user; A perception module is configured to obtain the perception results of the fixed binocular camera at the base station or the cloud, and based on the perception results, fuse the initial self-position estimates of each target user to obtain the final user position estimate; A beam management angle generation module configured to generate a multi-user beam management angle range based on the global radio map and the end user position estimate at the base station or the cloud; 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 estimate of the current 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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