Target detection, identification and tracking method and device based on wireless signals under cooperation of multiple base stations

Abstract

The invention discloses a target detection, identification and tracking method and device based on wireless signals under cooperation of multiple base stations, and the method comprises the steps: S1, a third-party service provider sends a request to a core network, a strategy control module makes a strategy, and a session management module constructs a session and synchronizes the session; s2, synchronously transmitting signal scanning by the selected base station group, acquiring data and sending the data to a user plane and a sensing module; s3, the sensing module fuses the data, detects and identifies the target, tracks the target, uploads a result to a core network, and then transmits the result to a service provider; s4, judging whether the service provider requests to close the service or not, otherwise, calculating a signal-to-noise ratio and detection times; if the condition is not met, returning to S2; if yes, calculating an average speed, and determining a sensing frequency threshold value; s2 to S4 are repeated until the threshold value is reached, and sensing is stopped; according to the invention, the bottleneck that the sensing resource of a single base station is limited is broken through by sharing the sensing results of multiple base stations, and high-precision and high-reliability target detection tracking performance is realized.

Description

Technical Field

[0001] The present invention belongs to the field of information and communication technology, and in particular relates to a method and device for target detection, recognition and tracking based on wireless signals under multi-base station cooperation. Background Art

[0002] With the rise of smart applications such as smart cities, smart transportation, and unmanned factories, the demand for infrastructure with wireless sensing capabilities has become increasingly urgent. In this context, thanks to the rapid development of 5G-A and 6G technologies, widely deployed communication base stations are gradually integrating wireless sensing functions to sense the surrounding environment and complete sensing tasks by transmitting communication signals. At the same time, using existing communication base stations for environmental perception has also greatly reduced the deployment cost of additional sensing equipment. For complex scenarios such as smart cities and smart transportation, communication base stations that perform wireless sensing tasks need to have high-precision and high-reliability sensing capabilities to ensure the robustness of sensing tasks. However, compared with radar sensing, for sensing based on wireless signals, on the one hand, there are fewer time-frequency resources that can be allocated to sensing tasks; on the other hand, the frequency band of communication signals is lower, the spectrum environment is complex, and the signal-to-noise ratio is low. This makes it difficult for the system to accurately and reliably complete sensing tasks such as target detection and target parameter estimation. For the above reasons, using a single base station communication signal for sensing cannot achieve sufficient accuracy and stability, which needs to be enhanced through multi-base station collaborative sensing. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, so as to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above object, the present invention provides a target detection, recognition and tracking method based on wireless signals under multi-base station cooperation, comprising:

[0005] Step 1: Send a target detection, identification and tracking service request to the core network through a third-party service provider, formulate the target type and area, and then first use the policy control module to formulate service-related policies according to the target detection, identification and tracking service request. The service-related policies include tracking policies, base station group selection policies and target identification policies. Then use the session management module to build a target detection, identification and tracking session and perform multi-base station synchronization. Finally, in the policy control module, select a base station group based on the base station group selection policy to perform the perception task;

[0006] Step 2: Use the base station group to synchronously transmit communication signals to perform perception scanning, obtain perception data, and package the perception data and send it to the user plane module and the perception module;

[0007] Step 3: Use the perception module to perform data fusion and target detection and identification on the perception data. If no target is detected, return to step 2; if a target point cloud cluster is detected, track the target corresponding to the detected target point cloud cluster, and use the target state data of the target corresponding to the target point cloud cluster as the perception result, store and upload the perception result, complete the perception of the current perception round, and upload the perception result corresponding to the current perception round to the third-party service provider through the core network;

[0008] Step 4: Determine whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session; if it has not been sent, calculate the average signal-to-noise ratio corresponding to the target point cloud cluster detected in the first set number of perception rounds. If the average signal-to-noise ratio is less than a preset signal-to-noise ratio threshold or the number of times the target is detected in the first set number of perception rounds is less than half, return to step 2. If the average signal-to-noise ratio is greater than the preset signal-to-noise ratio threshold, execute subsequent steps.

[0009] When the number of perceptions reaches a preset decision cycle, the average speed of the target in the second set number of perception rounds is calculated, and the perception number threshold is determined based on the calculated average speed;

[0010] Repeat steps 2 to 4 until the current sensing round reaches the sensing number threshold, and then stop sensing.

[0011] Optionally, step 1 also includes: when the third-party service provider sends a target detection, identification and tracking service request to the core network, the core network authenticates the third-party service provider through a unified data management module. After the identity authentication is passed, the core network sends the target detection, identification and tracking service to the policy control module for policy selection.

[0012] Optionally, the process of formulating the relevant strategy specifically includes:

[0013] The core network sends the target detection, identification and tracking service request to the policy control module for policy selection, obtains service-related policies and sends the service-related policies to the user plane module, session management module and perception module.

[0014] Optionally, the step 1 also includes: after the session management module constructs the target detection, identification and tracking session, the connection between the third-party service side, the base station side and the user plane module is completed through the session management module.

[0015] Optionally, the step 2 specifically includes:

[0016] If the current sensing round is the first sensing, each base station in the base station group synchronously transmits a communication signal to a designated area for sensing scanning;

[0017] If the current sensing round is not the first sensing, each base station in the base station group synchronously transmits a communication signal to the target area for sensing scanning.

[0018] Optionally, the process of acquiring the perception data specifically includes:

[0019] Each base station in the base station group synchronously transmits a communication signal to perform a perception scan to obtain echo data. The perception information preprocessing module corresponding to each base station performs data cleaning, two-dimensional fast Fourier transform, constant false alarm detection and multiple signal classification on the echo data to obtain the perception data and package it to send to the user plane module and the perception module; the perception data includes distance data, pitch angle data, azimuth angle data and radial velocity four-dimensional point cloud map data.

[0020] Optionally, the communication signal is an orthogonal frequency division multiplexing signal.

[0021] Optionally, the performing data fusion and target detection and recognition on the perception data specifically includes:

[0022] The point cloud images of each base station are fused through the perception module, and target recognition is performed on the fused point cloud images based on clustering algorithm and vector machine classification.

[0023] Optionally, the target tracking of the target corresponding to the detected target point cloud cluster specifically includes: after detecting the target point cloud cluster, performing an initial state estimation on the center of mass of the target point cloud cluster to obtain target state data, and using the extended Kalman algorithm to predict and update the state of the target.

[0024] A device for target detection, identification and tracking based on wireless signals under multi-base station cooperation, applied to a method for target detection, identification and tracking based on wireless signals under multi-base station cooperation, comprising a core network, the core network comprising a session management module and a unified data management module, a user plane module, a policy control module and a perception module that are communicatively connected to the session management module;

[0025] The policy control module is also in communication connection with the perception module;

[0026] The session management module is also communicatively connected to a third-party service provider;

[0027] The user plane module is also communicatively connected to a plurality of base stations, and each of the base stations is provided with a perception information preprocessing module.

[0028] The technical effects of the present invention are:

[0029] The present invention effectively reduces measurement errors and overcomes the problem of obstacle occlusion by fusing point cloud images from different base stations, thereby improving target tracking accuracy and stability.

[0030] The present invention can effectively improve the performance of detection, identification and tracking tasks by evaluating the geographical location, coverage capability and current network load of each base station, and selecting the base station group most suitable for performing detection, identification and tracking tasks in combination with the area or target location.

[0031] The present invention flexibly adjusts the time for reselecting a base station group according to target speed information and target perception signal-to-noise ratio, and can fully achieve a trade-off between computing resource utilization and detection and tracking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic diagram of a scene in an embodiment of the present invention;

[0034] Figure 2 This is a flow chart of the target detection, recognition and tracking method implemented in an embodiment of the present invention;

[0035] Figure 3 It is a structural diagram of a target detection, recognition and tracking device in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0037] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0038] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0039] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0040] Embodiment 1

[0041] like Figure 1 - Figure 3 As shown, this embodiment provides a method for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, including:

[0042] Step 1: Send a target detection, identification and tracking service request to the core network through a third-party service provider, formulate the target type and area, and then first use the policy control module to formulate service-related policies according to the target detection, identification and tracking service request. The service-related policies include tracking policies, base station group selection policies and target identification policies. Then use the session management module to build a target detection, identification and tracking session and perform multi-base station synchronization. Finally, in the policy control module, select a base station group based on the base station group selection policy to perform the perception task;

[0043] Step 2: Use the base station group to synchronously transmit communication signals to perform perception scanning, obtain perception data, and package the perception data and send it to the user plane module and the perception module;

[0044] Step 3: Use the perception module to perform data fusion and target detection and identification on the perception data. If no target is detected, return to step 2; if a target point cloud cluster is detected, track the target corresponding to the detected target point cloud cluster, and use the target state data of the target corresponding to the target point cloud cluster as the perception result, store and upload the perception result, complete the perception of the current perception round, and upload the perception result corresponding to the current perception round to the third-party service provider through the core network;

[0045] Step 4: Determine whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session; if it has not been sent, calculate the average signal-to-noise ratio corresponding to the target point cloud cluster detected in the first set number of perception rounds. If the average signal-to-noise ratio is less than a preset signal-to-noise ratio threshold or the number of times the target is detected in the first set number of perception rounds is less than half, return to step 2. If the average signal-to-noise ratio is greater than the preset signal-to-noise ratio threshold, execute subsequent steps.

[0046] When the number of perceptions reaches a preset decision cycle, the average speed of the target in the second set number of perception rounds is calculated, and the perception number threshold is determined based on the calculated average speed;

[0047] Repeat steps 2 to 4 until the current sensing round reaches the sensing number threshold, and then stop sensing.

[0048] A device for target detection, identification and tracking based on wireless signals under multi-base station cooperation, applied to a method for target detection, identification and tracking based on wireless signals under multi-base station cooperation, comprising a core network, the core network comprising a session management module and a unified data management module, a user plane module, a policy control module and a perception module that are communicatively connected to the session management module;

[0049] The policy control module is also in communication connection with the perception module;

[0050] The session management module is also communicatively connected to a third-party service provider;

[0051] The user plane module is also communicatively connected to a plurality of base stations, and each of the base stations is provided with a perception information preprocessing module.

[0052] In the target detection and tracking scenario based on wireless signals of a single base station, due to the small amount of time-frequency resources allocated to the perception task and the complex spectrum environment of the frequency band, this will directly lead to a low signal-to-noise ratio of the perception signal, resulting in a decrease in the accuracy of target detection and tracking. In response to the above problems, this embodiment proposes a target detection and tracking method and device based on wireless signals under the cooperation of multiple base stations, which breaks through the bottleneck of limited perception resources of a single base station by sharing the perception results of multiple base stations, and achieves high-precision and high-reliability target detection and tracking performance.

[0053] The specific implementation process of this embodiment is as follows:

[0054] Step 1: The core network triggers a target detection and tracking session. The policy control module determines the tracking strategy and base station group selection strategy and sends them to the corresponding modules. Each module executes the corresponding strategy.

[0055] Step 1.1 When a third-party service provider needs to perform detection, identification and tracking tasks for a specified type of target in a specified area, it will send a target detection, identification and tracking service request to the core network. The core network triggers a target detection, identification and tracking session based on the service request. After the tracking service request is sent, the core network will start identity authentication for the third-party service provider through the unified data management module.

[0056] Step 1.2 The detection, identification and tracking service request of the third-party service provider is transmitted from the core network to the policy control module. The policy control module determines the tracking strategy, base station group selection strategy and target identification strategy. Then the corresponding strategy is sent to the corresponding modules, including the user plane module, session management module and perception module.

[0057] In step 1.3, the session management module establishes the session process of target detection, identification and tracking according to the session module policy issued by the policy control module in step 1.2, completes the connection between the third-party service side, the base station side and the user plane module, completes the data path, and performs multi-base station synchronization.

[0058] Step 1.4 The strategy selection module selects a strategy based on the base station group. If the base station group selection is performed for the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the specified area location and the geographical location, coverage capability and current network load information of each base station. If it is not the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the target location and the geographical location, coverage capability and current network load information of each base station.

[0059] Step 2: During each perception, each base station transmits a communication signal to the environment, and the perception information preprocessing module at the base station processes the echo signal locally to obtain a point cloud map.

[0060] Step 2.1 At each sensing time, the selected multiple base stations synchronously scan the designated area and send orthogonal frequency division multiplexing signals, and use the sensing receiver to receive the echo signal at the same time.

[0061] Step 2.2 During each perception, after the perception receiver of each base station obtains the echo signal, the perception information preprocessing module at the base station will preprocess the perception signal to obtain a four-dimensional point cloud map of distance, pitch angle, azimuth and radial velocity.

[0062] Step 2.3: The perception information preprocessing module preprocesses the echo information perceived by the base station side, encapsulates and packages the data, sends it to the user plane module, and then uploads it to the perception module.

[0063] Step 3: The perception module fuses the point cloud images of each base station and performs target recognition and target tracking on the fused point cloud images.

[0064] Step 3.1: After obtaining the point cloud images of these base stations, the perception module performs perception data fusion and target recognition. If a point cloud cluster is recognized as a target of a specified type, the subsequent steps are continued. Otherwise, jump to step 2 for the next perception.

[0065] After obtaining the fused point cloud image in step 3.2, the perception data processing module starts to track the target. After that, the perception module stores all the perception data and uploads the target state as the perception result to the core network.

[0066] Step 3.3 After each sensing is completed, the core network transmits the sensing result back to the third-party service provider.

[0067] Step 4: The core network performs base station group update judgment and session closing judgment.

[0068] Step 4.1 determines whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session and completes the target detection, identification and tracking based on the wireless signal. If it has not been sent, continue to execute the subsequent steps.

[0069] Step 4.2 calculates the average signal-to-noise ratio of the target point cloud cluster tracked in the last 10 perceptions. If the average signal-to-noise ratio is less than the set threshold, or the target is not detected in more than 5 perceptions, jump to step 1.4 to reselect the base station group. Otherwise, continue to perform subsequent steps.

[0070] Step 4.3: After every K times of sensing, calculate the average speed of the target in the last 10 times of sensing. Based on this, determine the number of sensing times N required for the subsequent base station group selection.

[0071] Step 4.4 determines whether the number of sensing times n has reached N. If so, jump to step 1.4 to reselect the base station group. If not, jump to step 2 for the next sensing.

[0072] Based on the above method, this embodiment proposes a method and device for target detection, identification and tracking based on wireless signals under the cooperation of multiple base stations, including a session management module, a unified data management module, a user plane module, a policy control module, a perception information preprocessing module and a perception module. The session management module is used to provide session management for third-party service providers and selection control of user plane modules; the unified data management module is used to implement user contract management, access authorization, authentication information, etc.; the user plane module is responsible for providing message forwarding, processing, session anchor points, etc.; the policy control module is responsible for base station group selection strategy, target identification strategy and target tracking strategy, and sends the corresponding strategies of each module to each module; the perception information preprocessing module is responsible for preprocessing the echo signal at the base station to obtain a point cloud map; the perception module is used to fuse the point cloud maps of multiple base stations, and perform target identification and target tracking.

[0073] This embodiment effectively reduces measurement errors and overcomes the problem of obstacle occlusion by fusing point cloud images from different base stations, thereby improving target tracking accuracy and stability.

[0074] This embodiment evaluates the geographical location, coverage capability and current network load of each base station, and selects the base station group most suitable for performing the detection, identification and tracking task in combination with the region or target location, thereby effectively improving the performance of the detection, identification and tracking task.

[0075] This embodiment flexibly adjusts the time for reselecting the base station group according to the target speed information and the target perception signal-to-noise ratio, and can fully achieve a trade-off between computing resource utilization and detection and tracking performance.

[0076] The technical solution of the specific application example 1 of this embodiment: when the designated target type is a road vehicle, due to its high speed, the decision cycle K needs to be set to 20. At this time, the value is set to be small to ensure that the appropriate base station group is selected in time to perform the detection, identification and tracking task on the target. The specific steps are as follows.

[0077] Step 1: The core network triggers the target detection and tracking session. The policy control module determines the tracking strategy and base station group selection strategy and sends them to the corresponding modules. Each module executes the corresponding strategy.

[0078] Step 1.1 When a third-party service provider needs to perform detection, identification and tracking tasks for road vehicles in a specified area, it will send a target detection, identification and tracking service request to the core network. The core network triggers the target detection, identification and tracking session based on the service request. After the tracking service request is sent, the core network will start the identity verification of the third-party service provider through the unified data management module.

[0079] Step 1.2 The detection, identification and tracking service request of the third-party service provider is transmitted from the core network to the policy control module. The policy control module determines the tracking strategy, base station group selection strategy and target identification strategy. Then the corresponding strategy is sent to the corresponding modules, including the user plane module, session management module and perception module.

[0080] In step 1.3, the session management module establishes the session process of target detection, identification and tracking according to the session module policy issued by the policy control module in step 1.2, completes the connection between the third-party service side, the base station side and the user plane module, completes the data path, and performs multi-base station synchronization.

[0081] Step 1.4 The strategy selection module selects a strategy based on the base station group. If the base station group selection is performed for the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the specified area location and the geographical location, coverage capability and current network load information of each base station. If it is not the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the target location and the geographical location, coverage capability and current network load information of each base station.

[0082] Step 2: During each perception, each base station transmits a communication signal to the environment, and the perception information preprocessing module at the base station processes the echo signal locally to obtain a point cloud map.

[0083] Step 2.1 At each sensing time, the selected multiple base stations synchronously scan the designated area and send orthogonal frequency division multiplexing signals, and use the sensing receiver to receive the echo signal at the same time.

[0084] Step 2.2 During each perception, after the perception receiver of each base station obtains the echo signal, the perception information preprocessing module at the base station will preprocess the perception signal to obtain a four-dimensional point cloud map of distance, pitch angle, azimuth and radial velocity.

[0085] Step 2.3: The perception information preprocessing module preprocesses the echo information perceived by the base station side, encapsulates and packages the data, sends it to the user plane module, and then uploads it to the perception module.

[0086] Step 3: The perception module fuses the point cloud images of each base station and performs target recognition and target tracking on the fused point cloud images.

[0087] Step 3.1: After obtaining the point cloud images of these base stations, the perception module performs perception data fusion and target recognition. If a point cloud cluster is recognized as a target of a specified type, the subsequent steps are continued. Otherwise, jump to step 2 for the next perception.

[0088] After obtaining the fused point cloud image in step 3.2, the perception data processing module starts to track the target. After that, the perception module stores all the perception data and uploads the target status as the perception result to the core network.

[0089] Step 3.3 After each sensing is completed, the core network transmits the sensing result back to the third-party service provider.

[0090] Step 4: The core network performs base station group update judgment and session closing judgment.

[0091] Step 4.1 determines whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session and completes the target detection, identification and tracking based on wireless signals. If it has not been sent, continue to execute the subsequent steps.

[0092] Step 4.2 calculates the average signal-to-noise ratio of the target point cloud cluster tracked in the last 10 perceptions. If the average signal-to-noise ratio is less than the set threshold, or the target is not detected in more than 5 perceptions, jump to step 1.4 to reselect the base station group. Otherwise, continue to perform subsequent steps.

[0093] Step 4.3: After every 20 perceptions, calculate the average speed of the target in the last 10 perceptions. Based on this, determine the number of perceptions N required for the subsequent base station group selection.

[0094] Step 4.4 determines whether the number of sensing times n has reached N. If so, jump to step 1.4 to reselect the base station group. If not, jump to step 2 for the next sensing.

[0095] The technical solution of the specific application example 2 of this embodiment: when the designated target type is a pedestrian, due to its slow speed, the decision cycle K needs to be set to 50. At this time, the value is set to a large value to ensure that the network computing resources are reduced as much as possible under the premise of timely selecting the appropriate base station group. The specific steps are as follows.

[0096] Step 1: The core network triggers the target detection and tracking session. The policy control module determines the tracking strategy and base station group selection strategy and sends them to the corresponding modules. Each module executes the corresponding strategy.

[0097] Step 1.1 When a third-party service provider needs to perform a detection, recognition and tracking task for pedestrians in a specified area, it will send a target detection, recognition and tracking service request to the core network. The core network triggers a target detection, recognition and tracking session based on the service request. After the tracking service request is sent, the core network will start the identity authentication of the third-party service provider through the unified data management module.

[0098] Step 1.2 The detection, identification and tracking service request of the third-party service provider is transmitted from the core network to the policy control module. The policy control module determines the tracking strategy, base station group selection strategy and target identification strategy. Then the corresponding strategy is sent to the corresponding modules, including the user plane module, session management module and perception module.

[0099] In step 1.3, the session management module establishes the session process of target detection, identification and tracking according to the session module policy issued by the policy control module in step 1.2, completes the connection between the third-party service side, the base station side and the user plane module, completes the data path, and performs multi-base station synchronization.

[0100] Step 1.4 The strategy selection module selects a strategy based on the base station group. If the base station group selection is performed for the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the specified area location and the geographical location, coverage capability and current network load information of each base station. If it is not the first time, the strategy selection module will select the base station group that is most suitable for performing the detection, identification and tracking task based on the target location and the geographical location, coverage capability and current network load information of each base station.

[0101] Step 2: During each perception, each base station transmits a communication signal to the environment, and the perception information preprocessing module at the base station processes the echo signal locally to obtain a point cloud map.

[0102] Step 2.1 At each sensing time, the selected multiple base stations synchronously scan the designated area and send orthogonal frequency division multiplexing signals, and use the sensing receiver to receive the echo signal at the same time.

[0103] Step 2.2 During each perception, after the perception receiver of each base station obtains the echo signal, the perception information preprocessing module at the base station will preprocess the perception signal to obtain a four-dimensional point cloud map of distance, pitch angle, azimuth and radial velocity.

[0104] Step 2.3: The perception information preprocessing module preprocesses the echo information perceived by the base station side, encapsulates and packages the data, sends it to the user plane module, and then uploads it to the perception module.

[0105] Step 3: The perception module fuses the point cloud images of each base station and performs target recognition and target tracking on the fused point cloud images.

[0106] Step 3.1: After obtaining the point cloud images of these base stations, the perception module performs perception data fusion and target recognition. If a point cloud cluster is recognized as a target of a specified type, the subsequent steps are continued. Otherwise, jump to step 2 for the next perception.

[0107] After obtaining the fused point cloud image in step 3.2, the perception data processing module starts to track the target. After that, the perception module stores all the perception data and uploads the target state as the perception result to the core network.

[0108] Step 3.3 After each sensing is completed, the core network transmits the sensing result back to the third-party service provider.

[0109] Step 4: The core network performs base station group update judgment and session closing judgment.

[0110] Step 4.1 determines whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session and completes the target detection, identification and tracking based on the wireless signal. If it has not been sent, continue to execute the subsequent steps.

[0111] Step 4.2 calculates the average signal-to-noise ratio of the target point cloud cluster tracked in the last 10 perceptions. If the average signal-to-noise ratio is less than the set threshold, or the target is not detected in more than 5 perceptions, jump to step 1.4 to reselect the base station group. Otherwise, continue to perform subsequent steps.

[0112] Step 4.3: After every 50 perceptions, calculate the average speed of the target in the last 10 perceptions. Based on this, determine the number of perceptions N required for the subsequent base station group selection.

[0113] Step 4.4 determines whether the number of sensing times n has reached N. If so, jump to step 1.4 to reselect the base station group. If not, jump to step 2 for the next sensing.

[0114] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A target detection, recognition and tracking method based on wireless signals under multi-base station cooperation, characterized in that: include: Step 1: Send a target detection, identification and tracking service request to the core network through a third-party service provider, formulate the target type and area, and then first use the policy control module to formulate service-related policies according to the target detection, identification and tracking service request. The service-related policies include tracking policies, base station group selection policies and target identification policies. Then use the session management module to build a target detection, identification and tracking session and perform multi-base station synchronization. Finally, in the policy control module, select a base station group based on the base station group selection policy to perform the perception task; Step 2: Use the base station group to synchronously transmit communication signals to perform perception scanning, obtain perception data, and package the perception data and send it to the user plane module and the perception module; Step 3: Use the perception module to perform data fusion and target detection and identification on the perception data. If no target is detected, return to step 2; if a target point cloud cluster is detected, track the target corresponding to the detected target point cloud cluster, and use the target state data of the target corresponding to the target point cloud cluster as the perception result, store and upload the perception result, complete the perception of the current perception round, and upload the perception result corresponding to the current perception round to the third-party service provider through the core network; Step 4: Determine whether the third-party service provider has sent a service shutdown request to the core network. If it has been sent, the core network ends the target detection, identification and tracking session; If not sent, calculate the average signal-to-noise ratio corresponding to the target point cloud cluster detected in the first set number of perception rounds. If the average signal-to-noise ratio is less than the preset signal-to-noise ratio threshold or the number of times the target is detected in the first set number of perception rounds is less than half, return to step 2. If the average signal-to-noise ratio is greater than the preset signal-to-noise ratio threshold, execute the subsequent steps. When the number of perceptions reaches a preset decision cycle, the average speed of the target in the second set number of perception rounds is calculated, and the perception number threshold is determined based on the calculated average speed; Repeat steps 2 to 4 until the current sensing round reaches the sensing number threshold, and then stop sensing.

2. According to claim 1, a method for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, characterized in that: The step 1 also includes: when the third-party service provider sends a target detection, identification and tracking service request to the core network, the core network authenticates the third-party service provider through a unified data management module. After the identity authentication is passed, the core network sends the target detection, identification and tracking service to the policy control module for policy selection.

3. According to claim 1, a method for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, characterized in that: The process of formulating the relevant strategies specifically includes: The core network sends the target detection, identification and tracking service request to the policy control module for policy selection, obtains service-related policies and sends the service-related policies to the user plane module, session management module and perception module.

4. According to claim 1, a method for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, characterized in that: The step 1 also includes: after the session management module builds the target detection, identification and tracking session, the session management module completes the connection between the third-party service side, the base station side and the user plane module.

5. The target detection, recognition and tracking method based on wireless signals under multi-base station cooperation according to claim 1 is characterized in that: The step 2 specifically includes: If the current sensing round is the first sensing, each base station in the base station group synchronously transmits a communication signal to the designated area for sensing scanning; If the current sensing round is not the first sensing, each base station in the base station group synchronously transmits a communication signal to the target area for sensing scanning.

6. The target detection, recognition and tracking method based on wireless signals under multi-base station cooperation according to claim 1 is characterized in that: The process of acquiring the perception data specifically includes: Each base station in the base station group synchronously transmits a communication signal to perform a perception scan to obtain echo data. The perception information preprocessing module corresponding to each base station performs data cleaning, two-dimensional fast Fourier transform, constant false alarm detection and multiple signal classification on the echo data to obtain the perception data and package it to send to the user plane module and the perception module; the perception data includes distance data, pitch angle data, azimuth angle data and radial velocity four-dimensional point cloud map data.

7. The target detection, recognition and tracking method based on wireless signals in multi-base station cooperation according to claim 1 is characterized in that: The communication signal is an orthogonal frequency division multiplexing signal.

8. The target detection, recognition and tracking method based on wireless signals in multi-base station cooperation according to claim 1 is characterized in that: The performing data fusion and target detection and recognition on the perception data specifically includes: The point cloud images of each base station are fused through the perception module, and target recognition is performed on the fused point cloud images based on clustering algorithm and vector machine classification.

9. The method for target detection, recognition and tracking based on wireless signals in multi-base station cooperation according to claim 1, characterized in that: The target tracking of the target corresponding to the detected target point cloud cluster specifically includes: after the target point cloud cluster is detected, the initial state estimation of the centroid of the target point cloud cluster is performed to obtain target state data, and the extended Kalman algorithm is used to predict and update the state of the target.

10. A device for target detection, recognition and tracking based on wireless signals under multi-base station cooperation, applied to a method for target detection, recognition and tracking based on wireless signals under multi-base station cooperation according to any one of claims 1 to 9, characterized in that: Including core network; The core network includes a session management module and a unified data management module, a user plane module, a policy control module and a perception module that are communicatively connected to the session management module; the policy control module is also communicatively connected to the perception module; the session management module is also communicatively connected to a third-party service provider; The user plane module is also communicatively connected to a plurality of base stations, and each of the base stations is provided with a perception information preprocessing module.

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