Target sensing method and device, equipment, storage medium and program product

Abstract

The invention relates to the technical field of wireless, and provides a target sensing method, device and equipment, a storage medium and a program product, the target sensing method is applied to network equipment, and the method comprises the following steps: determining type information of a sensing target in a sensing result based on the sensing result and RID information under the condition that the sensing result and the RID information exist; wherein the sensing result is obtained by performing target sensing by the network equipment; the RID information is sent by the RID receiver. According to the invention, under the condition of determining that the sensing result and the RID information exist, the RID information can be used as the auxiliary information to assist the base station to carry out data processing in the sensing result, and the type information of the sensing target in the sensing result is accurately determined, so that the sensing result and the RID information are fused, and the sensing accuracy is improved. The method can solve the problem that the general sensing integrated system is easy to mistakenly recognize the target type when detecting the target, thereby improving the credibility of the detection result, and guaranteeing the reliability of the general sensing integrated system.

Description

Technical Field

[0001] This application relates to the field of wireless technology, and in particular to a target sensing method, apparatus, device, storage medium, and program product. Background Technology

[0002] With the further evolution of the 5th generation mobile communication technology (5G), the integration of communication and sensing is considered an essential technical feature of mobile information networks, and has become an important technical direction for the evolution of 5G-Advanced (5G-A) and 6G.

[0003] The integrated sensing base station draws on the radar detection principle. Through the deep integration of communication and sensing functions, it analyzes the direct, reflected, and scattered wireless communication signals. Relying on advanced signal processing algorithms, it endows the communication network with all-round perception capabilities of the environment and target objects. By utilizing a portion of time and frequency resources, it transmits sensing signals and can perceive the motion state of the target through signal processing of the echo signals. It outputs key information such as distance, speed, position, and angle, realizing functions such as target object positioning, ranging, speed measurement, detection, and identification.

[0004] However, in current integrated sensing and communication systems, the sensing and positioning are independent of each other, forming two separate systems and processes. As a result, when facing large-scale implementation, it is easily affected by various existing network factors, which limits its sensing accuracy and detection capabilities. Consequently, it is easy to misidentify the target type when detecting targets, leading to insufficient reliability of the detection results and seriously affecting the reliability of the integrated sensing and communication system. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the related art. To this end, this application proposes a target sensing method, apparatus, device, storage medium, and program product to solve the problem that integrated sensing systems are prone to misidentifying target types when detecting targets, thereby improving the reliability of detection results and ensuring the reliability of the integrated sensing system.

[0006] The target perception method according to a first aspect of this application is applied to a network device, the target perception method comprising: If a sensing result and remote identification (RID) information are determined, the type information of the sensed target in the sensing result is determined based on the sensing result and the RID information; wherein, the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

[0007] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

[0008] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information.

[0009] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information includes: Based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results, the type information of perceived targets in the perception results is determined; the target point cloud data is the point cloud data of perceived targets within the target area; the target area is the preset area range of the point trace data of the identified targets in the RID information.

[0010] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the number of target point cloud data in the perception result, the total number of point cloud data of the perceived target in the perception result, and the dispersion of the point cloud data of the perceived target in the perception result includes: The false alarm confidence level is determined based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results. If the false alarm confidence level is greater than a preset threshold, the type information of the perceived target in the perception result is determined to be a false alarm.

[0011] According to one embodiment of this application, after determining the false alarm confidence level, the method further includes: If the false alarm confidence level is less than or equal to a preset threshold, the type information of the perceived target in the perception result is determined to be an illegal flight target.

[0012] According to one embodiment of this application, after determining that the type information of the perceived target in the perception result is a false alarm, the method further includes: Based on the location information of the perceived target in the perception result, a request to invoke the optoelectronic device is sent to the application function AF network element; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the perceived target. Receive the captured images sent by the AF network element; Based on the captured image, the type information of the perceived target in the perception result is verified to obtain the verification result; Based on the verification results, the perception results are either reported or cleared.

[0013] According to one embodiment of this application, it also includes: If RID information is confirmed to exist but no perception results are found, network optimization is performed.

[0014] According to one embodiment of this application, it also includes: If it is determined that there is a sensing result but no RID information, a request to invoke the optoelectronic device is sent to the AF network element based on the location information of the sensing target in the sensing result; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the sensing target. Receive the captured images sent by the AF network element; Based on the captured images, determine the type information of the perceived target in the perception results.

[0015] According to a second aspect of this application, a target perception method is applied to an AF network element, the target perception method comprising: Based on the perception results and RID information, the type information of the perceived target in the perception results is determined; the perception results are sent by the network device; the RID information is sent by the RID.

[0016] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

[0017] According to one embodiment of this application, determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, and the trajectory of the mismatched perceived target passes the anomaly check, the photoelectric device is invoked based on the location information of the perceived target in the perception result. Based on the images captured by the optoelectronic device, the type information of the perceived target in the perception result is determined.

[0018] The target sensing device according to a third aspect embodiment of this application includes: The first determining module is used to determine the type information of the perceived target in the perceived result based on the perceived result and the remote identification RID information, when it is determined that there is a perceived result and remote identification RID information; wherein the perceived result is obtained by the network device in the process of target perception; and the RID information is sent by the RID receiver.

[0019] The target sensing device according to a fourth aspect embodiment of this application includes: The second determining module is used to determine the type information of the perceived target in the perception result based on the perception result and RID information; the perception result is sent by the network device; the RID information is sent by the RID.

[0020] A network device according to a fifth aspect of this application includes a memory, a transceiver, and a processor; A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer programs from the memory and performing the following operations: If a sensing result and remote identification (RID) information are determined, the type information of the sensed target in the sensing result is determined based on the sensing result and the RID information; wherein, the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

[0021] An electronic device according to a sixth aspect of this application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the target perception methods described above.

[0022] According to a seventh aspect embodiment of the present application, the storage medium is a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the target perception method as described above.

[0023] A computer program product according to an eighth aspect of this application includes a computer program that, when executed by a processor, implements any of the target perception methods described above.

[0024] The above-described one or more technical solutions in the embodiments of this application have at least the following technical effects: By establishing a communication connection between the base station and the RID receiver, the base station can perform target perception to obtain perception results, while the RID receiver can receive RID information. Therefore, the base station can receive RID information simultaneously with target perception. Furthermore, when both perception results and RID information are confirmed, the RID information can be used as auxiliary information to assist the base station in processing the data in the perception results, thereby accurately determining the type of the perceived target. Thus, by fusing perception results and RID information, the problem of misidentifying target types in integrated sensing systems can be solved, thereby improving the reliability of detection results and ensuring the reliability of the integrated sensing system.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is one of the flowcharts of the target perception method provided in the embodiments of this application.

[0028] Figure 2 This is the second flowchart of the target perception method provided in the embodiments of this application.

[0029] Figure 3 This is a schematic diagram of the target sensing method provided in this application, in which the base station senses the target and the RID receiver reports the target's location information.

[0030] Figure 4 This is a schematic diagram of the point cloud data distribution of false alarm targets in the target perception method provided in the embodiments of this application.

[0031] Figure 5 This is a schematic diagram of the point cloud data distribution of unauthorized flying targets in the target perception method provided in the embodiments of this application.

[0032] Figure 6 This is the third flowchart of the target perception method provided in the embodiments of this application.

[0033] Figure 7 This is the fourth flowchart of the target perception method provided in the embodiments of this application.

[0034] Figure 8 This is a schematic diagram of the network device provided in this application.

[0035] Figure 9 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] It should be noted that the current integrated sensing and positioning system and the communication and positioning system are independent of each other, and are two separate systems and processes. They are not deeply integrated, the sensing station itself does not have communication auxiliary information, and the deployment method is a distributed network based on communication, which has the following problems: 1. The sensing base station equipment in the integrated sensing system is limited by engineering constraints such as frequency band, roof aperture, transmission power, and signal modulation, resulting in limited sensing performance and issues such as insufficient sensing accuracy and missed detections. 2. The sensing capability of the integrated sensing system is easily affected by the dynamic and static clutter of the existing network environment, such as birds, airborne objects, leaf clusters, ground vehicle and pedestrian clutter, large buildings, thunderstorms, strong winds and other extreme weather, which makes it difficult to distinguish from real sensing targets such as drones, resulting in false alarms or missed detections. 3. Interference between communication signals and sensing signals between multiple stations in a distributed network leads to an increase in the sensing noise floor, resulting in false targets or missed detections. 4. In scenarios where buildings obstruct coverage or where the network planning is unreasonable, there is a risk of missed detections.

[0038] Considering the various network factors affecting the large-scale implementation of the aforementioned integrated sensing system, its current sensing accuracy and detection capabilities are limited. False alarms and missed detections are prone to occur when detecting targets, leading to insufficient reliability and severely impacting the system's reliability and security. Furthermore, with the development of 5G-A and 6G, new demands are being placed on networks for high-security applications such as low-altitude security and surveillance in border or critical protection areas. These demands include particularly high requirements for false alarm rate (≤1%) and detection rate (≥99%). Therefore, there is an urgent need for solutions to improve sensing reliability and address the current limitations in sensing performance, high false alarm and missed detection rates, and insufficient reliability of integrated sensing systems.

[0039] Based on this, this application proposes a target sensing method, apparatus, device, storage medium, and program product, which can achieve target measurement within different distance ranges by adjusting the power of the reference path.

[0040] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and regulations of the locality and with authorization from the owner of the relevant device.

[0041] It should be noted that with the rapid development of the low-altitude economy, legal drones are widely used in various scenarios, including last-mile delivery services, cross-regional freight transport on fixed routes, drone disaster monitoring and emergency delivery, drone power line inspection, drone security patrol, agricultural plant protection, and so on. Some low-altitude route scenarios have even achieved routine operation, and the frequency of legal drone flights is increasing day by day.

[0042] However, with the widespread application and popularization of drone technology, the safety hazards brought by drones have become increasingly prominent, such as frequent "black flight" incidents, posing a threat to important facilities such as airports and high-speed railways. Therefore, drone regulatory standards and rules (such as the "Interim Regulations on the Flight Management of Unmanned Aerial Vehicles" and the "Safety Requirements for Civil Unmanned Aerial Vehicle Systems") have been formulated and implemented, mandating that unmanned aerial vehicles automatically broadcast Remote Identification (RID) information via wireless local area network or Bluetooth during flight to improve the traceability and security of drones. Currently, RID information for low-altitude scenarios has been incorporated into the mandatory national standards for drones. Therefore, legal drones will broadcast relevant RID information after takeoff, including the remote ID, drone location information (latitude, longitude, and altitude), drone flight status (speed and heading), control console location information (latitude, longitude, and altitude), drone flight timestamp, and emergency status indications.

[0043] It should be further explained that the sensing and detection process of the integrated sensing system includes two parts: sensing signal processing and sensing data processing. The sensing signal processing involves preprocessing the sensing echo signal, such as filtering, and then sampling to obtain the original symbol-level in-phase / quadrature (IQ) data. After removing the cyclic prefix (CP) and performing Fourier transform, the estimated results of the three-dimensional measurements of distance, velocity, and angle are obtained through range-velocity-angle (RVA) spectrum estimation. Based on the distance and angle, the three-dimensional position coordinates of latitude, longitude, and altitude can be obtained, forming the position and velocity estimates for each point, thus outputting the point cloud data of the sensed target.

[0044] The sensing data processing first clusters the point cloud results of the sensed target, aggregating a cluster of point clouds into a single target location. After acquiring the sensing location and velocity results from different base stations, the upper-level fusion processing unit performs multi-cell / cross-site data-level fusion and trajectory association fusion processing to obtain the target's final location, velocity, and other information. Simultaneously, it connects multiple points at different times to form a trajectory. This process can comprehensively calculate the trajectory points at the latest moment based on historical flight path data, predicted points, etc., for continuous trajectory tracking. Finally, the upper-level target recognition algorithm identifies the target's shape, category, etc., completing the final target acquisition, management, and tracking process.

[0045] To address the aforementioned pain points, this application proposes a perception reliability enhancement scheme for low-altitude UAV perception scenarios. By integrating a RID receiver and multi-source access technology, the scheme assists the sensing base station in processing perception data, optimizes the perception processing flow, and effectively enables the identification and management of unauthorized flights, false alarms, and missed detections. This further reduces the false alarm rate and missed detection rate of the integrated sensing system, thereby improving perception performance and reliability.

[0046] This application may include two external deployment methods for external RID receivers: one is to deploy the RID receiver on the network device (specifically, the base station), and the other is to deploy the RID receiver on the application function (AF) network element.

[0047] Figure 1 This is one of the flowcharts illustrating the target perception method provided in the embodiments of this application, such as... Figure 1 As shown, when the RID receiver is deployed on the network device (specifically, a base station), the target perception method includes: Step 110: If it is determined that there are sensing results and remote identification RID information, determine the type information of the sensing target in the sensing results based on the sensing results and RID information; wherein, the sensing results are obtained by the network device in sensing the target; and the RID information is sent by the RID receiver.

[0048] It should be noted that the execution entity of the target perception method provided in this application embodiment can be a network device. The network device in this application can be a base station (also referred to as a sensing base station in this application), which can include multiple cells providing services to terminals. Depending on the specific application scenario, the base station can also be called an access point, or a device in the access network that communicates with wireless terminal devices through one or more sectors on the air interface, or other names. The overall equipment composition of the base station in this application can be considered as an integrated sensing system.

[0049] Specifically, this application connects a receiving device (specifically a RID receiver) to the active antenna unit (AAU) of the sensing base station. The sensing base station can then receive the RID information sent by the RID receiver, wherein the RID information in the RID receiver is the relevant RID information broadcast and reported by a legitimate UAV.

[0050] Specifically, the RID receiver can report RID information to the sensing base station. The exchange fields include, but are not limited to, a remote ID, a timestamp, UAV location information (latitude, longitude, and altitude), UAV flight status (speed and heading), UAV flight trajectory, controller location information (latitude, longitude, and altitude), and whether it is in an emergency state. The sensing base station will fuse the RID interface exchange content with its own sensing results to effectively reduce false alarms and improve the overall system reliability and security.

[0051] Simultaneously, the sensing base station itself can perform target perception and obtain perception results. These results can include the target's trajectory, point cloud data of the target, and the target's location information. The target's location information can be three-dimensional, including latitude, longitude, and altitude.

[0052] The perception results may also include information such as the distance, speed, and angle of the perceived target.

[0053] It should be noted that the process of the sensing base station performing target perception and obtaining perception results can be achieved using traditional or innovative sensing methods, which will not be specifically described in this application.

[0054] Figure 2 This is a second schematic flowchart of the target perception method provided in the embodiments of this application, as shown below. Figure 2As shown, the sensing base station can determine whether there is RID information reported by the RID receiver (i.e., RID reporting result) and whether there is sensing result obtained by active sensing within the current time period.

[0055] If both perception results and RID information are confirmed, the trajectory of the perceived target in the perception results can be matched with the trajectory of the identified target in the RID information to determine whether the perceived target in the perception results is the same as the target reported in the RID results. The specific trajectory matching process can be implemented using Euclidean distance or other methods.

[0056] Furthermore, based on the results of track matching or track mismatch, the type information of the perceived target in the perception results is determined. Among them, the type information of the perceived target in the perception results can be a legal flight target (e.g., a legally flying drone, i.e., a free flight), an illegal flight target (e.g., an illegally flying drone, i.e., a black flight), and a false alarm.

[0057] In one embodiment, when matching the trajectory of the perceived target in the perception result with the trajectory of the identified target in the RID information, the similarity of all reported location coordinates of the two trajectories (i.e., the trajectory of the perceived target and the trajectory of the identified target) at the same timestamp (interpolation is performed if the timestamps are not aligned) can be calculated. If the Euclidean distance between the location coordinates of the perceived target and the location coordinates of the identified target is less than or equal to a threshold U (e.g., 10), they are considered similar. Assuming that both trajectories report N coordinate points at time t, then: , in, The x-axis value represents the location coordinate of the i-th positioning point of the sensed target at time t. The horizontal axis value represents the i-th location coordinate point of the identified target at time t; The vertical axis value represents the i-th positioning coordinate point of the sensed target at time t. This represents the vertical axis value of the i-th location coordinate point of the identified target at time t.

[0058] Furthermore, if the number of points with similar coordinates is M, then the trajectory matching degree is: , When L meets a certain threshold (e.g., 80%), it is determined that the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, that is, the two reported the same target; otherwise, they are determined to be different targets.

[0059] According to the target perception method of this application, by establishing a communication connection between a base station and a RID receiver, the base station can perform target perception to obtain perception results, while the RID receiver can receive RID information. Therefore, the base station can receive RID information sent by the RID receiver while performing target perception. Furthermore, when it is determined that both perception results and RID information exist, the RID information can be used as auxiliary information to assist the base station in data processing of the perception results, thereby accurately determining the type information of the perceived target in the perception results. Thus, by fusing the perception results and RID information, the problem of misidentifying target types in integrated sensing systems can be solved, thereby improving the reliability of the detection results and ensuring the reliability of the integrated sensing system.

[0060] In one embodiment, based on the perception results and RID information, determining the type information of the perceived target in the perception results includes: If the trajectory of the perceived target in the perception results matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception results is determined as a legitimate flight target.

[0061] Specifically, if, after track matching, it is determined that the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, for example, if the matching degree of the two trajectories meets a preset threshold, it indicates that the target in the RID information and the perception result is the same target. Since the RID information is broadcast by a legitimate flight target as required, the type information of the perceived target in the perception result can be determined as a legitimate flight target.

[0062] Furthermore, the sensing base station can report relevant sensing results to the Sensing Function (SF) network element, including the remote ID, timestamp, UAV location information (latitude and longitude, altitude), UAV flight status (speed, heading), UAV flight trajectory, etc.

[0063] When the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, this application can accurately determine the type information of the perceived target in the perception result as a legitimate flight target. Thus, by fusing the perception result and the RID information, the problem of the integrated sensor system easily misidentifying the target type when detecting targets can be solved, thereby improving the reliability of the detection result and ensuring the reliability of the integrated sensor system.

[0064] In one embodiment, based on the perception results and RID information, determining the type information of the perceived target in the perception results includes: If the trajectory of the perceived target in the perception results does not match the trajectory of the identified target in the RID information, the type information of the perceived target in the perception results is determined based on the point cloud data of the perceived target in the perception results and the point trace data of the identified target in the RID information.

[0065] Specifically, if, after track matching, it is determined that the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, for example, the matching degree of the two trajectories does not reach the preset threshold, it indicates that the target in the RID information and the perception result is not the same target. In this case, the sensing base station needs to further determine whether the perceived target in the perception result is a false alarm or an illegal flying target.

[0066] Therefore, the type information of the perceived target in the perception result can be determined based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information.

[0067] Specifically, the confidence level for identifying a perceived target as a false alarm within the reporting period T can be determined. This confidence level can be obtained by weighting the relative positional dispersion of point clouds at each time point within the reporting period and the proportion of point clouds entering the white-fly protection zone.

[0068] Furthermore, based on the confidence level, it can be determined whether the perceived target in the perception result is a false alarm or an illegal flying target.

[0069] In cases where the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, this application can accurately determine the type information of the perceived target in the perception result based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information. Thus, by fusing the perception result and the RID information, the problem of misidentifying the target type when detecting targets in a synesthetic system can be solved, thereby improving the reliability of the detection result and ensuring the reliability of the synesthetic system.

[0070] In one embodiment, based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information, the type information of the perceived target in the perception result is determined, including: Based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results, the type information of perceived targets in the perception results is determined; target point cloud data refers to the point cloud data of perceived targets within the target area; the target area range is the preset area range of the point trace data of the identified targets in the RID information.

[0071] Furthermore, based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results, the type information of perceived targets in the perception results is determined, including: The false alarm confidence level is determined based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results. If the false alarm confidence level is greater than a preset threshold, the type information of the perceived target in the perception result is determined to be a false alarm.

[0072] In addition, after determining the false alarm confidence level, it also includes: If the false alarm confidence level is less than or equal to a preset threshold, the type information of the perceived target in the perception result is determined to be an illegal flight target.

[0073] It should be noted that in the point cloud clustering processing of sensing data by the sensing base station, all sensed targets are presented in point cloud form. The location and other information of the sensed targets are obtained by calculating the centroid of the point cloud clusters. However, in the legitimate UAV information reported by the RID receiver, the location and other information of a sensed target correspond to each moment. For details, please refer to... Figure 3 , Figure 3 This is a schematic diagram of the target sensing method provided in this application, in which the base station senses the target and the RID receiver reports the target's location information.

[0074] When the sensing target of the sensing base station is a false alarm caused by environmental clutter or network interference, the point cloud data distribution of the sensing target in its sensing results is relatively scattered, with an irregular distribution at different times. The relative position between the point cloud data and the location of the target reported by the RID receiver (i.e., the identified target) exhibits randomness. Therefore, when the sensing target of the sensing base station is a false alarm, the point cloud data distribution in its sensing results is as follows: Figure 4 As shown, Figure 4 This is a schematic diagram of the point cloud data distribution of false alarm targets in the target perception method provided in the embodiments of this application.

[0075] When the perceived target is an illegal flight target (such as an illegal drone, i.e., unauthorized flight), because the drone's own system is set with a safe collision avoidance distance (greater than or equal to 10 meters (m), otherwise it is easily destroyed) and the current sensing system's distance resolution is >10m, there is a certain distance between drones. Therefore, the point cloud data of the perceived unauthorized flight target and the position of the legally flying target reported by the RID receiver show certain patterns at different times. When the sensing base station's perception result is unauthorized flight, the distribution of the point cloud data in its perception result is as follows: Figure 5 As shown, Figure 5 This is a schematic diagram of the point cloud data distribution of unauthorized flying targets in the target perception method provided in the embodiments of this application.

[0076] Based on the corresponding relationship between the point cloud data generated by the different sensing targets and the reported positions of unauthorized flyover targets by the RID receiver, false alarms and unauthorized flyovers are judged, and a confidence level label is assigned before being reported to the AF network element. The confidence level can be obtained by weighting the relative position dispersion of the point cloud at each time point within the reporting period and the proportion of point clouds entering the unauthorized flyover protection zone, specifically through the following formula: ; Where C is the confidence level of a perceived target being identified as a false alarm within the reporting period T, with a value ranging from [0,1], and a larger value indicating a higher confidence level; α and β are weighting coefficients, satisfying α+β=1; σ(t) is the dispersion (standard deviation) of the point cloud data of the perceived target at time t; σ(t) max Let σ(t) be the maximum possible value of the dispersion of the point cloud data at time t (the maximum possible value can be obtained using any method such as historical data method, point cloud bounding box method, maximum distance method, density distribution, etc.); σ(t) and σ(t) max The larger the ratio, the higher the dispersion of the point cloud data and the greater the confidence level. M is the number of point clouds (i.e., the number of target point cloud data) that enter the Baifei protection zone (i.e., the preset area range of the point trace data of the target in the RID information) at time t, and N is the total number of point clouds at time t. The larger the ratio of M to N, the more point clouds enter the Baifei protection zone and the greater the confidence level.

[0077] If the confidence level C is greater than a certain threshold (such as 50%), it is judged as a false alarm; otherwise, it is judged as an illegal flight target (i.e., unauthorized flight).

[0078] During the reporting period T, the confidence level for identifying a perceived target as an unauthorized flight is 1-C.

[0079] Based on the point cloud data of the perceived target in the perception results and the point trace data of the identified target in the RID information, this application can accurately determine the type information of the perceived target in the perception results. Therefore, by fusing the perception results and RID information, the problem of the integrated sensor system easily misidentifying the target type when detecting targets can be solved, thereby improving the reliability of the detection results and ensuring the reliability of the integrated sensor system.

[0080] In one embodiment, after determining that the type information of the perceived target in the perception result is a false alarm, the method further includes: Based on the location information of the perceived target in the perception result, a request to invoke the optoelectronic device is sent to the application function AF network element; the request is used to instruct the AF network element to invoke the optoelectronic device to capture images of the perceived target. Receive captured images sent by the AF network element; Based on the captured images, the type information of the perceived target in the perception results is verified to obtain the verification results; Based on the verification results, the perception results are either reported or cleared.

[0081] Specifically, if the type of the sensed target in the sensing result is determined to be a false alarm, the sensing base station can transmit information such as the false alarm result and the confidence level of the false alarm to the AF network element through the SF network element. Simultaneously, the sensing base station can also send a request to invoke the optoelectronic device to the application function AF network element based on the location information of the sensed target in the sensing result. This request can also include the speed and direction information of the sensed target.

[0082] Specifically, the call request can be sent to the SF network element, which will then pass the call request through to the AF network element.

[0083] Furthermore, upon receiving a request, the AF network element can predict the target's location based on its position, velocity, and direction information. It can then use the service platform to invoke optoelectronic devices for secondary confirmation of the target. Specifically, it can use optoelectronic devices to capture images of the predicted target location.

[0084] It should be noted that the service platform, AF network element, and sensing base station in this application can all have image recognition capabilities. Therefore, the service platform can directly identify the images captured by the optoelectronic equipment, thereby determining that the target in the image is a drone or other (such as birds and balloons).

[0085] Therefore, the sensing base station can receive the captured images returned by the AF network element for identification, thereby determining the type information of the sensed target, or it can directly receive the type information of the sensed target returned by the AF network element.

[0086] Therefore, the type information of the perceived target in the perception result can be verified based on the recognition result of the captured image.

[0087] Specifically, if the recognition result of the captured image indicates the presence of a drone in the image, it can be determined that the perceived target in the perception result is not a false alarm but an illegal flying target (i.e., unauthorized flight). At this time, the perception result, the type information of the perceived target, and the confidence level of the type information can be transmitted to the AF network element through the SF network element. The perception result may include the target ID, timestamp, drone location information (latitude and longitude, altitude), drone flight status (speed, heading), drone flight trajectory, and other information.

[0088] At the same time, the detected target can be marked and alerted, and continuously tracked to determine relevant information such as the type and status of unauthorized flights.

[0089] If the recognition result of the captured image shows that there is no drone in the image, it can be determined that the perceived target in the perception result may be a false alarm caused by airborne objects, environmental clutter, network interference, weather, etc. This perception result can be cleared and no longer reported to the AF network element.

[0090] After determining that the type of the sensed target in the sensing results is an illegal flying target, the sensing base station can also transmit information such as whether the sensing result is a false alarm and the confidence level of the false alarm to the AF network element through the SF network element. Simultaneously, the sensing base station can also send a request to the AF network element to invoke optoelectronic devices based on the location information of the sensed target in the sensing results. This request can also include the speed and direction information of the sensed target.

[0091] Specifically, the call request can be sent to the SF network element, which will then pass the call request through to the AF network element.

[0092] Furthermore, upon receiving a request, the AF network element can predict the target's location based on its position, velocity, and direction information. It can then use the service platform to invoke optoelectronic devices for secondary confirmation of the target. Specifically, it can use optoelectronic devices to capture images of the predicted target location.

[0093] Furthermore, the sensing base station can receive the captured images returned by the AF network element for identification, thereby determining the type information of the sensed target, or it can directly receive the type information of the sensed target returned by the AF network element.

[0094] Therefore, the type information of the perceived target in the perception result can be verified based on the recognition result of the captured image.

[0095] Specifically, if the recognition result of the captured image indicates the presence of a drone in the image, it can be determined that the perceived target in the perception result is not a false alarm but an illegal flying target (i.e., unauthorized flight). At this time, the perception result, the type information of the perceived target, and the confidence level of the type information can be transmitted to the AF network element through the SF network element. The perception result may include the target ID, timestamp, drone location information (latitude and longitude, altitude), drone flight status (speed, heading), drone flight trajectory, and other information.

[0096] At the same time, the detected target can be marked and alerted, and continuously tracked to determine relevant information such as the type and status of unauthorized flights.

[0097] If the recognition result of the captured image shows that there is no drone in the image, it can be determined that the perceived target in the perception result may be a false alarm caused by airborne objects, environmental clutter, network interference, weather, etc. This perception result can be cleared and no longer reported to the AF network element.

[0098] Furthermore, if the detected target is determined to be a false alarm, the sensing base station also needs to report the current sensing capability to the network management system. Simultaneously, the signal processing module of the sensing base station analyzes whether the cause of the false alarm is an unreasonable Constant False Alarm Rate (CFAR) threshold setting or a decrease in the Signal-to-Clutter Ratio (SCR), and performs the following corresponding actions based on the analysis results: ① By dynamically adjusting and increasing the CFAR detection threshold, false alarms caused by the system misjudging more noise or clutter as targets due to an excessively low detection threshold are reduced; ② Follow up with confirmation beams with higher gain and narrower beams to further distinguish whether false alarms are caused by ground clutter, vehicles, pedestrians, birds, airborne objects, etc.

[0099] By adaptively adjusting the sensing capabilities of the sensor station using the two methods described above until no false alarms are detected, the false alarm rate of the system is reduced and the system reliability is improved.

[0100] This application can also use photoelectric equipment to perform secondary confirmation of the sensing results, thereby improving the reliability of the detection results and ensuring the reliability of the integrated sensing system.

[0101] In one embodiment, network optimization is performed when it is determined that RID information exists but no perception result exists.

[0102] Specifically, if the sensing base station only receives the RID information reported by the RID receiver but has no sensing result, it can be determined that there is a missed detection, so the sensing base station can perform network optimization.

[0103] Specifically, the sensing base station can optimize the network in the following ways to reduce the false negative rate: 1. Optimize network architecture by enhancing perception coverage; 2. Optimize the algorithm layer by improving detection robustness; 3. Optimize resource allocation by balancing sensing and communication.

[0104] Furthermore, the sensing base station can also transmit RID information to the SF network element through the SF network element. The reported RID information may include the remote ID, timestamp, UAV location information (latitude and longitude, altitude), UAV flight status (speed, heading), UAV flight trajectory, etc.

[0105] Simultaneously, the sensing base station also needs to report any missed detections in the current sensing capability to the network management system. The signal processing module of the sensing base station synchronously analyzes whether the missed detections are due to building obstruction or insufficient SCR, and performs the following corresponding actions based on the analysis results: ① Adjust the beam direction precisely according to the RID information until a sensing trajectory appears. If the missed detection is due to insufficient SCR in the current sensing area, it needs to be enhanced. ② If no sensing trajectory appears, the missed detection may be due to insufficient coverage caused by building obstruction. In this case, it is necessary to report to the network management department for re-network planning to make up for the coverage blind spots.

[0106] In the event of missed detections, this application can reduce the missed detection rate and ensure the reliability of the integrated sensing system by optimizing the network.

[0107] In one embodiment, if it is determined that there is a sensing result but no RID information, a request to invoke the optoelectronic device is sent to the AF network element based on the location information of the sensing target in the sensing result; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the sensing target; Receive captured images sent by the AF network element; Based on the captured images, determine the type information of the perceived target in the perception results.

[0108] Specifically, if the sensing base station does not receive the RID information reported by the RID receiver, but only has the sensing results obtained from its own target perception, the sensing base station can send a request to invoke the optoelectronic device to the application function (AF) network element based on the location information of the perceived target in the sensing results. The request may also include the speed and direction information of the perceived target.

[0109] Specifically, the call request can be sent to the SF network element, which will then pass the call request through to the AF network element.

[0110] Furthermore, upon receiving a request, the AF network element can predict the target's location based on its position, velocity, and direction information. It can then use the service platform to invoke multi-source devices, such as optoelectronic equipment, for secondary confirmation of the target. Specifically, it can invoke optoelectronic equipment to capture images of the predicted target location.

[0111] It should be noted that the service platform, AF network element, and sensing base station in this application can all have image recognition capabilities. Therefore, the service platform can directly identify the images captured by the optoelectronic equipment, thereby determining that the target in the image is a drone or other (such as birds and balloons).

[0112] Therefore, the sensing base station can receive the captured images returned by the AF network element for identification, thereby determining the type information of the sensed target, or it can directly receive the type information of the sensed target returned by the AF network element.

[0113] Therefore, the type information of the perceived target can be determined based on the recognition results of the captured image.

[0114] Specifically, if the recognition result of the captured image indicates the presence of a drone in the image, then the perceived target in the perception result can be determined to be an illegal flight target (i.e., unauthorized flight). At this time, the perception result, the type information of the perceived target, and the confidence level of the type information can be transmitted to the AF network element through the SF network element. The perception result may include the target ID, timestamp, drone location information (latitude and longitude, altitude), drone flight status (speed, heading), drone flight trajectory, and other information.

[0115] At the same time, the detected target can be marked and alerted, and continuously tracked to determine relevant information such as the type and status of unauthorized flights.

[0116] If the recognition result of the captured image shows that there is no drone in the image, it can be determined that the perceived target in the perception result may be a false alarm caused by airborne objects, environmental clutter, network interference, weather, etc. This perception result can be cleared and no longer reported to the AF network element.

[0117] In cases where a sensing result is determined to exist but RID information is not available, this application employs photoelectric equipment for secondary confirmation of the sensing result, thereby improving the reliability of the detection result and ensuring the reliability of the integrated sensing system.

[0118] Figure 6 This is the third flowchart of the target perception method provided in the embodiments of this application, as shown below. Figure 6 As shown, when the RID receiver is deployed on the AF network element side, this target awareness method includes: Step 610: Based on the perception results and RID information, determine the type information of the perceived target in the perception results; the perception results are sent by the network device; the RID information is sent by the RID.

[0119] Among them, based on the perception results and RID information, the type information of the perceived target in the perception results is determined, including: If the trajectory of the perceived target in the perception results matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception results is determined as a legitimate flight target.

[0120] Furthermore, based on the perception results and RID information, the type information of the perceived target in the perception results is determined, including: If the trajectory of the perceived target in the perception results does not match the trajectory of the identified target in the RID information, and the trajectory of the mismatched perceived target passes the anomaly check, the photoelectric device is invoked based on the location information of the perceived target in the perception results. Based on images captured by optoelectronic devices, determine the type information of the perceived target in the perception results.

[0121] It should be noted that the target perception method provided in this application embodiment can be applied to AF network elements. The AF network element can invoke optoelectronic devices through the service platform.

[0122] The RID receiver can report RID information to the AF network element. The interactive fields include, but are not limited to, the remoteid identification code, timestamp, UAV location information (latitude and longitude, altitude), UAV flight status (speed, heading), UAV flight trajectory, controller location information (latitude and longitude, altitude), and whether it is in an emergency state.

[0123] The AF network element transmits relevant information to the SF network element in the current sensing area based on the RID information reported by the RID receiver, facilitating the transmission of relevant data to the sensing base station. Interaction fields include, but are not limited to, remote ID, timestamp, UAV location information (latitude, longitude, altitude), UAV flight status (speed, heading), UAV flight trajectory, controller location information (latitude, longitude, altitude), and whether it is in an emergency state.

[0124] The SF network element transmits relevant information to the sensing base station in the current sensing area based on the RID information issued by the AF network element, facilitating the sensing and processing of relevant data. The interaction fields include, but are not limited to, remote ID, timestamp, UAV location information (latitude, longitude, and altitude), UAV flight status (speed and heading), UAV flight trajectory, controller location information (latitude, longitude, and altitude), and whether it is in an emergency state.

[0125] Figure 7 This is the fourth flowchart of the target perception method provided in the embodiments of this application, as shown below. Figure 7 As shown, in one embodiment, the AF network element can receive the sensing results transmitted by the network device (specifically, the sensing base station) through the SF network element, as well as the RID information actively reported by the RID receiver.

[0126] Furthermore, the AF network element can perform trajectory matching between the trajectory of the sensed target in the sensing results (i.e., the trajectory reported by the sensing base station) and the trajectory of the identified target in the RID information (i.e., the trajectory reported by the RID) to determine whether the trajectory of the sensed target in the sensing results is the same as the trajectory in the RID reporting results. The specific trajectory matching process can be implemented by matching and judging using Euclidean distance, or by other methods.

[0127] Furthermore, based on the results of track matching or track mismatch, the type information of the perceived target in the perception results is determined. Among them, the type information of the perceived target in the perception results can be a legal flight target (e.g., a legally flying drone, i.e., a free flight), an illegal flight target (e.g., an illegally flying drone, i.e., a black flight), and a false alarm.

[0128] In one embodiment, when matching the trajectory of the perceived target in the perception result with the trajectory of the identified target in the RID information, the similarity of all reported location coordinates of the two trajectories (i.e., the trajectory of the perceived target and the trajectory of the identified target) at the same timestamp (interpolation is performed if the timestamps are not aligned) can be calculated. If the Euclidean distance between the location coordinates of the perceived target and the location coordinates of the identified target is less than or equal to a threshold U (e.g., 10), they are considered similar. Assuming that both trajectories report N coordinate points at time t, then: , in, The x-axis value represents the location coordinate of the i-th positioning point of the sensed target at time t. The horizontal axis value represents the i-th location coordinate point of the identified target at time t; The vertical axis value represents the i-th positioning coordinate point of the sensed target at time t. This represents the vertical axis value of the i-th location coordinate point of the identified target at time t.

[0129] Furthermore, if the number of points with similar coordinates is M, then the trajectory matching degree is: , When L meets a certain threshold (e.g., 80%), it is determined that the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, that is, the two reported the same target; otherwise, they are determined to be different targets.

[0130] Furthermore, if, after track matching, it is determined that the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, for example, if the matching degree of the two trajectories meets a preset threshold, it indicates that the target in the RID information and the perception result is the same target. Since the RID information is broadcast by a legitimate flight target as required, the type information of the perceived target in the perception result can be determined as a legitimate flight target (i.e., a free flight).

[0131] Furthermore, the AF network element can report relevant perception results to the service platform, including the remoteid identification code, timestamp, UAV location information (latitude and longitude, altitude), UAV flight status (speed, heading), UAV flight trajectory, etc.

[0132] Furthermore, if, after track matching, it is determined that the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, i.e., the matching degree of the two trajectories does not reach the threshold, and they are judged as different targets, then the AF network element can perform track filtering (i.e., anomaly verification, removing trajectories of targets that are obviously false alarms) on the trajectories in the perception result based on conditions such as flight duration and flight area, thereby quickly removing obviously false alarm targets. It should be noted that the process of determining whether the target corresponding to the trajectory is obviously a false alarm based on conditions such as flight duration and flight area in this application can be implemented in any feasible way, and no specific limitation is made in this application.

[0133] For trajectories that are not eliminated, the target position can be predicted based on information such as the position, angle, and velocity of the corresponding perceived target. In some embodiments, trajectory filtering may not be performed, and target position prediction can be performed for each trajectory of a mismatched perceived target.

[0134] Furthermore, the AF network element calculates PTZ parameters (i.e., horizontal and vertical steering and focusing parameters) based on the capabilities of the optoelectronic device, and sends the parameters to control the optoelectronic device to take pictures of the predicted target location through the camera. At the same time, it receives the pictures taken by the optoelectronic device and performs image identification, thereby determining whether the target in the image is a drone or other (such as birds and balloons).

[0135] Furthermore, if the presence of a drone is confirmed, the perceived target is determined to be an illegal flight target, that is, the drone is judged to be flying illegally. It will be marked and alarmed, and its coordinates and other information will be reported to the business platform, including target ID, timestamp, drone location information (latitude and longitude, altitude), drone flight status (speed, heading), drone flight trajectory, etc. At the same time, image recognition and continuous tracking will be performed to confirm the type and status of illegal flight.

[0136] If it is determined that no drone exists, the perceived target in the perception result may be a false alarm caused by airborne objects, environmental clutter, network interference, weather, etc. In this case, the perception result, including perception ID, timestamp, target location information (latitude and longitude, altitude), target speed, heading, target trajectory, etc., will be cleared and will no longer be reported to the business platform.

[0137] This application targets low-altitude UAV perception scenarios. By combining multiple technologies such as RID and optoelectronic devices, it can accurately identify unauthorized flights, legitimate flights, and false alarms. Targeted operations can further optimize the integrated sensing system, effectively reduce false alarms, and improve the reliability of the integrated sensing system.

[0138] As can be seen from the above embodiments, the target perception method provided in this application is a multi-source technology-assisted perception reliability improvement method. The AF network element side or the sensing base station side of the integrated sensing system obtains the communication connection information of legitimate targets (targets with RID connections) through an external RID receiver. At the same time, multi-source fusion technology is integrated to assist the sensing base station in verifying the positioning estimation, tracking and reporting of the perceived target and other data processing processes. This enables the identification of false alarms, missed detections, legal drones, and illegal drones, as well as the management of perceived targets. Targeted optimization of the perception system for false alarms and missed detections is carried out, effectively reducing the false alarm rate and missed detection rate of the integrated sensing system, improving the accuracy and reliability of the system, and more effectively meeting the perception service needs of low-altitude scenarios with high security requirements, such as low-altitude security and low-altitude supervision.

[0139] The target sensing device provided in this application is described below. The target sensing device described below and the target sensing method described above can be referred to in correspondence.

[0140] Furthermore, this application also provides a target sensing device.

[0141] The target sensing device includes: The first determining module is used to determine the type information of the perceived target in the perceived result based on the perceived result and the remote identification RID information, when it is determined that there is a perceived result and remote identification RID information; wherein the perceived result is obtained by the network device in the process of target perception; and the RID information is sent by the RID receiver.

[0142] The target sensing device of this application establishes a communication connection between a base station and a RID receiver. Since the base station can perform target sensing to obtain sensing results, and the RID receiver can receive RID information, the base station can simultaneously perform target sensing and receive RID information sent by the RID receiver. Furthermore, when the existence of both sensing results and RID information is confirmed, the RID information can be used as auxiliary information to assist the base station in data processing of the sensing results, thereby accurately determining the type information of the sensed target in the sensing results. Therefore, by fusing the sensing results and RID information, the problem of misidentifying target types in integrated sensing systems can be solved, thereby improving the reliability of the detection results and ensuring the reliability of the integrated sensing system.

[0143] In one embodiment, the first determining module is specifically used for: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

[0144] In one embodiment, the first determining module is further configured to: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information.

[0145] In one embodiment, the first determining module is further configured to: Based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results, the type information of perceived targets in the perception results is determined; the target point cloud data is the point cloud data of perceived targets within the target area; the target area is the preset area range of the point trace data of the identified targets in the RID information.

[0146] In one embodiment, the first determining module is further configured to: The false alarm confidence level is determined based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results. If the false alarm confidence level is greater than a preset threshold, the type information of the perceived target in the perception result is determined to be a false alarm.

[0147] In one embodiment, the first determining module is further configured to: If the false alarm confidence level is less than or equal to a preset threshold, the type information of the perceived target in the perception result is determined to be an illegal flight target.

[0148] In one embodiment, the first determining module is further configured to: Based on the location information of the perceived target in the perception result, a request to invoke the optoelectronic device is sent to the application function AF network element; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the perceived target. Receive the captured images sent by the AF network element; Based on the captured image, the type information of the perceived target in the perception result is verified to obtain the verification result; Based on the verification results, the perception results are either reported or cleared.

[0149] In one embodiment, the first determining module is further configured to: If RID information is confirmed to exist but no perception results are found, network optimization is performed.

[0150] In one embodiment, the first determining module is further configured to: If it is determined that there is a sensing result but no RID information, a request to invoke the optoelectronic device is sent to the AF network element based on the location information of the sensing target in the sensing result; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the sensing target. Receive the captured images sent by the AF network element; Based on the captured images, determine the type information of the perceived target in the perception results.

[0151] In one embodiment, the target sensing device further includes: The second determining module is used to determine the type information of the perceived target in the perception result based on the perception result and RID information; the perception result is sent by the network device; the RID information is sent by the RID.

[0152] In one embodiment, the second determining module is further configured to: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

[0153] In one embodiment, the second determining module is further configured to: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, and the trajectory of the mismatched perceived target passes the anomaly check, the photoelectric device is invoked based on the location information of the perceived target in the perception result. Based on the images captured by the optoelectronic device, the type information of the perceived target in the perception result is determined.

[0154] Figure 8 This is a schematic diagram of the structure of a network device according to an embodiment of this application, with reference to... Figure 8 This application embodiment also provides a network device, which may include: a memory 810, a transceiver 820, and a processor 830; The memory 810 is used to store computer programs; the transceiver 820 is used to send and receive data under the control of the processor 830; the processor 830 is used to read the computer program in the memory 810 and perform the following operations: If a sensing result and remote identification (RID) information are determined, the type information of the sensed target in the sensing result is determined based on the sensing result and the RID information; wherein, the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

[0155] Among them, Figure 8In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 830) and memory (memory 810). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 820 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 830 is responsible for managing the bus architecture and general processing, and the memory 810 can store data used by the processor 830 during operation.

[0156] Figure 9 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 9 As shown, the electronic device may include a processor 910, a communications interface 920, a memory 930, and a communication bus 940, wherein the processor 910, the communications interface 920, and the memory 930 communicate with each other via the communication bus 940. The processor 910 can call logical instructions in the memory 930 to execute the following method: if it is determined that there is a sensing result and remote identification (RID) information, based on the sensing result and the RID information, determine the type information of the sensed target in the sensing result; wherein the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

[0157] Alternatively, you can use the following method: Based on the perception results and RID information, the type information of the perceived target in the perception results is determined; the perception results are sent by the network device; the RID information is sent by the RID.

[0158] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0159] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the methods provided in the above embodiments, such as: when it is determined that there is a perception result and remote identification RID information, determining the type information of the perceived target in the perception result based on the perception result and the RID information; wherein the perception result is obtained by the network device performing target perception; and the RID information is sent by the RID receiver.

[0160] Or, including: Based on the perception results and RID information, the type information of the perceived target in the perception results is determined; the perception results are sent by the network device; the RID information is sent by the RID.

[0161] In another aspect, embodiments of this application also provide a computer program product, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to perform the methods provided in the above embodiments, such as: when it is determined that there is a perception result and remote identification RID information, determining the type information of the perceived target in the perception result based on the perception result and the RID information; wherein the perception result is obtained by the network device in performing target perception; and the RID information is sent by the RID receiver.

[0162] Or, including: Based on the perception results and RID information, the type information of the perceived target in the perception results is determined; the perception results are sent by the network device; the RID information is sent by the RID.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate this application and are not intended to limit this application. Although this application has been described in detail with reference to the embodiments, those skilled in the art should understand that various combinations, modifications, or equivalent substitutions of the technical solutions of this application do not depart from the spirit and scope of the technical solutions of this application.

Claims

1. A target perception method, characterized in that, The target perception method, applied to network devices, includes: If a sensing result and remote identification (RID) information are determined, the type information of the sensed target in the sensing result is determined based on the sensing result and the RID information; wherein, the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

2. The target perception method according to claim 1, characterized in that, The step of determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

3. The target perception method according to claim 2, characterized in that, The step of determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information.

4. The target perception method according to claim 3, characterized in that, The step of determining the type information of the perceived target in the perception result based on the point cloud data of the perceived target in the perception result and the point trace data of the identified target in the RID information includes: Based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results, the type information of perceived targets in the perception results is determined; the target point cloud data is the point cloud data of perceived targets within the target area; the target area is the preset area range of the point trace data of the identified targets in the RID information.

5. The target perception method according to claim 4, characterized in that, The determination of the type information of the perceived target in the perception result based on the number of target point cloud data in the perception result, the total number of point cloud data of the perceived target in the perception result, and the dispersion of the point cloud data of the perceived target in the perception result includes: The false alarm confidence level is determined based on the number of target point cloud data in the perception results, the total number of point cloud data of perceived targets in the perception results, and the dispersion of point cloud data of perceived targets in the perception results. If the false alarm confidence level is greater than a preset threshold, the type information of the perceived target in the perception result is determined to be a false alarm.

6. The target perception method according to claim 5, characterized in that, After determining the false alarm confidence level, the method further includes: If the false alarm confidence level is less than or equal to a preset threshold, the type information of the perceived target in the perception result is determined to be an illegal flight target.

7. The target perception method according to claim 5, characterized in that, After determining that the type information of the perceived target in the perception result is a false alarm, the method further includes: Based on the location information of the perceived target in the perception result, a request to invoke the optoelectronic device is sent to the application function AF network element; the request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the perceived target. Receive the captured images sent by the AF network element; Based on the captured image, the type information of the perceived target in the perception result is verified to obtain the verification result; Based on the verification results, the perception results are either reported or cleared.

8. The target perception method according to any one of claims 1 to 7, characterized in that, Also includes: If RID information is confirmed to exist but no perception results are found, network optimization is performed.

9. The target perception method according to any one of claims 1 to 7, characterized in that, Also includes: If it is determined that there is a sensing result but no RID information, a request to call the optoelectronic device is sent to the AF network element based on the location information of the sensing target in the sensing result; The invocation request is used to instruct the AF network element to invoke the optoelectronic device to capture an image of the perceived target; Receive the captured images sent by the AF network element; Based on the captured images, determine the type information of the perceived target in the perception results.

10. A target perception method, characterized in that, Applied to AF network elements, the target perception method includes: Based on the perception results and RID information, the type information of the perceived target in the perception results is determined; the perception results are sent by the network device; the RID information is sent by the RID.

11. The target perception method according to claim 10, characterized in that, The step of determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result matches the trajectory of the identified target in the RID information, the type information of the perceived target in the perception result is determined as a legitimate flight target.

12. The target perception method according to claim 10, characterized in that, The step of determining the type information of the perceived target in the perception result based on the perception result and the RID information includes: If the trajectory of the perceived target in the perception result does not match the trajectory of the identified target in the RID information, and the trajectory of the mismatched perceived target passes the anomaly check, the photoelectric device is invoked based on the location information of the perceived target in the perception result. Based on the images captured by the optoelectronic device, the type information of the perceived target in the perception result is determined.

13. A target sensing device, characterized in that, include: The first determining module is used to determine the type information of the perceived target in the perceived result based on the perceived result and the remote identification RID information, when it is determined that there is a perceived result and remote identification RID information; wherein the perceived result is obtained by the network device in the process of target perception; and the RID information is sent by the RID receiver.

14. A target sensing device, characterized in that, include: The second determining module is used to determine the type information of the perceived target in the perception result based on the perception result and RID information; The perception result is sent by the network device; the RID information is sent by the RID.

15. A network device, characterized in that, Includes memory, transceiver, and processor; Memory, used to store computer programs; Transceiver, used to send and receive data under the control of the processor; Processor, configured to read the computer program in the memory and perform the following operations: If a sensing result and remote identification (RID) information are determined, the type information of the sensed target in the sensing result is determined based on the sensing result and the RID information; wherein, the sensing result is obtained by the network device through target sensing; and the RID information is sent by the RID receiver.

16. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the target perception method as described in any one of claims 1 to 12.

17. A storage medium, said storage medium being a non-transitory computer-readable storage medium, wherein a computer program is stored thereon, characterized in that, When the computer program is executed by a processor, it implements the target perception method as described in any one of claims 1 to 12.

18. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the target perception method according to any one of claims 1 to 12.

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