Low-altitude target rapid identification and tracking method and system

Abstract

The invention provides a low-altitude target rapid identification and tracking method and system, and the method comprises the steps: obtaining radar detection data in a detection range, which are collected by a millimeter wave radar detection module, and first attitude data of the millimeter wave radar detection module in real time through a terahertz wireless near-body communication module; when it is judged that a suspected target appears in the detection range according to the radar detection data, the azimuth and distance of the suspected target are calculated in real time according to the first attitude data and the radar detection data; performing real-time calculation according to the azimuth and distance of the suspected target to obtain second attitude data required by detection of the infrared camera module, and adjusting the attitude of the infrared camera module in real time according to the second attitude data; image data collected by the infrared camera module are obtained in real time, and whether a suspected target in the image data is a real target or not is recognized through an AI recognition algorithm. According to the scheme, high-precision and real-time detection of the unmanned aerial vehicle can be achieved, the environmental adaptability is higher, and potential safety hazards can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle identification, and in particular to a method and system for rapid identification and tracking of low-altitude targets. Background Art

[0002] In recent years, the development and popularization of drone technology has attracted widespread attention in various fields. However, the widespread use of drones has also brought many security issues, especially in the military, public safety and protection of critical infrastructure. Effectively detecting and monitoring drones has become an important technical problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for rapid identification and tracking of low-altitude targets, so as to achieve high-precision and real-time detection of unmanned aerial vehicles, and have stronger environmental adaptability, which is conducive to reducing safety hazards.

[0004] The technical solution provided by the present invention is as follows:

[0005] The present invention provides a method for rapid identification and tracking of low-altitude targets, which is used for identification and tracking of unmanned aerial vehicles, and comprises the following steps:

[0006] Acquire radar detection data within a detection range collected by a millimeter-wave radar detection module and first posture data of the millimeter-wave radar detection module in real time through a terahertz wireless close-range communication module;

[0007] When it is determined according to the radar detection data that a suspected target appears within the detection range, the direction and distance of the suspected target are obtained by real-time calculation according to the first posture data and the radar detection data;

[0008] Calculating in real time according to the orientation and distance of the suspected target to obtain second posture data required for detection by the infrared camera module, and adjusting the posture of the infrared camera module in real time according to the second posture data;

[0009] The image data collected by the infrared camera module is acquired in real time through the terahertz wireless close-range communication module, and the AI ​​recognition algorithm is used to identify whether the suspected target in the image data is a real target.

[0010] This solution complements millimeter-wave radar detection and infrared camera detection. When the millimeter-wave radar detects a suspected target, infrared camera is used for target recognition. This can make up for the blind spots and shortcomings of the millimeter-wave radar, accurately identify targets such as drones in the air, and improve detection accuracy. In addition, data is transmitted through terahertz wireless close-range communication, which can utilize the wider bandwidth and higher frequency of the terahertz frequency band to greatly shorten the synchronization time of the entire system, and can complete the capture of the object and the transmission of the video within 10ms, thereby improving detection efficiency.

[0011] In some embodiments, when judging according to the radar detection data that a suspected target appears within the detection range, the method further includes:

[0012] The warning information is sent to the display device through the terahertz wireless close-range communication module.

[0013] In some implementations, after the calculation obtains the position and distance of the suspected target, the method further includes:

[0014] Acquiring current posture data of the infrared camera module through the terahertz wireless close-range communication module;

[0015] When the current posture data changes, it is determined that the user has an intention to find a target, and the second posture data required for detection by the infrared camera module is obtained by calculation according to the orientation and distance of the suspected target.

[0016] In some embodiments, it further comprises:

[0017] The image data collected by the infrared camera module is sent to the display device for display through the terahertz wireless close-range communication module, and the position of the suspected target is marked in the display screen;

[0018] The azimuth difference between the azimuth of the suspected target and the current aiming direction of the infrared camera module is calculated based on the current posture data and the second posture data, and a guidance prompt for adjusting the aiming direction of the infrared camera module is set and displayed based on the azimuth difference and the position of the suspected target marked in the display screen.

[0019] In some embodiments, the identifying whether the suspected target in the image data is a real target by using an AI recognition algorithm specifically includes:

[0020] Build an AI recognition algorithm model for target recognition and train the target recognition;

[0021] Dividing the image data into dynamic feature vectors and static feature vectors, and performing MPCA attention processing on the dynamic feature vectors and the static feature vectors;

[0022] Concatenate the processed dynamic feature vector and the static feature vector to obtain a first feature vector;

[0023] Processing the first feature vector by using the loss function output result in the training process to obtain a second feature vector associated with the target;

[0024] The second feature vector is input into the AI ​​recognition algorithm model to determine whether the suspected target is a real target.

[0025] In some embodiments, after identifying whether the suspected target in the image data is a real target by using an AI recognition algorithm, the method further includes:

[0026] When the suspected target is determined to be a real target, the distance and speed of the real target are actually calculated based on the radar detection data and the image data, and are displayed and marked through the display device.

[0027] In a second aspect, an identification and tracking system based on the identification and tracking method according to the first aspect comprises:

[0028] A smart helmet, the smart helmet comprising a helmet body, the millimeter-wave radar detection module being movably mounted on the helmet body, and the millimeter-wave radar detection module comprising a first posture sensor;

[0029] A sight, one end of which is equipped with the infrared camera module, and a second attitude sensor is installed on the sight;

[0030] A computing terminal is communicatively connected with the millimeter wave radar detection module, the infrared camera module, the first posture sensor and the second posture sensor through a terahertz wireless close-range communication module.

[0031] In some embodiments, AR glasses and a projection device disposed toward the AR glasses are disposed on the helmet body. The projection device is communicatively connected to the computing terminal for projecting a projection image sent by the computing terminal to the AR glasses.

[0032] In some implementations, the computing terminal is a shoulder-held terminal or a backpack-held terminal.

[0033] In some embodiments, the sight is a handheld sight or the sight is mounted on a striking device.

[0034] According to a method and system for rapid identification and tracking of low-altitude targets provided by the present invention, by complementing millimeter-wave radar detection and infrared camera detection, when the millimeter-wave radar detects a suspected target, infrared camera is used for target identification, which can make up for the blind spots and shortcomings of the millimeter-wave radar, accurately identify targets such as drones in the air, and improve detection accuracy; and by transmitting data through terahertz wireless close-range communication, the characteristics of the terahertz frequency band with a larger bandwidth and higher frequency can be utilized, so that the synchronization time of the entire system is greatly shortened, and the capture of the object and the transmission of the video can be completed within 10ms, thereby improving detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The preferred implementation scheme will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present solution.

[0036] Figure 1 It is a schematic diagram of the overall process of an embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of the structure of a smart helmet of an identification and tracking system according to an embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of the sighting structure of the identification and tracking system according to one embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the computing terminal structure of the identification and tracking system of one embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0041] In order to simplify the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0042] In recent years, the development and popularization of drone technology has attracted widespread attention in various fields. However, the widespread use of drones has also brought many security issues, especially in the military, public safety and protection of critical infrastructure. Effectively detecting and monitoring drones has become an important technical problem that needs to be solved urgently.

[0043] During low-altitude scanning, radar detection, infrared detection, etc. can be used. Radar detection, such as millimeter-wave radar, can provide strong support for low-altitude drone detection. In terms of accuracy, millimeter-wave radar uses high-frequency signals to achieve accurate distance measurement. Its short wavelength enables it to have a high resolution when measuring target distance and relative speed, and can effectively identify and track small drones; and millimeter-wave radar is insensitive to changes in ambient light and weather conditions, and is not easily affected by bad weather such as rain, snow, and fog. It can maintain stable detection performance, allowing millimeter-wave radar to operate in various weather conditions, ensuring the role of early warning detection of multiple targets and angles. Infrared detection technology has strong penetration ability and high resolution. It can easily filter out most interference in the white light state through spectral analysis of the infrared band, greatly improving the accuracy and efficiency of detection. This solution uses radar detection and infrared detection, and makes the two complement each other, which can achieve high-precision detection of drones, and has stronger environmental adaptability, is not affected by bad weather such as rain, snow, and fog, and can maintain stable detection performance. At the same time, when realizing the complementary combination of radar detection and infrared detection, the detection data is first obtained in real time through the millimeter-wave radar. When the millimeter-wave radar detects a suspected target, the target is identified through infrared photography. This can fully utilize the advantages of the two detection methods and optimize the detection logic. In addition, when transmitting data, terahertz wireless close-range communication can be used. Terahertz wireless close-range communication relies on its ultra-high speed and extremely low latency to ensure the real-time transmission of data and detection results within this system. The terahertz frequency band has a larger bandwidth and higher frequency, which can greatly shorten the synchronization time of the entire system and complete the capture of objects and transmission of videos within 10ms. The following will be described in detail with reference to the accompanying drawings:

[0044] In one embodiment, referring to the accompanying drawings Figure 1 A low-altitude target rapid identification and tracking method for UAV identification and tracking includes the following steps:

[0045] S100, acquiring in real time, through the terahertz wireless close-range communication module, radar detection data within a detection range collected by the millimeter-wave radar detection module, and first posture data of the millimeter-wave radar detection module;

[0046] S200, when it is determined according to the radar detection data that a suspected target appears within the detection range, obtaining the direction and distance of the suspected target by real-time calculation according to the first posture data and the radar detection data;

[0047] S300, obtaining second posture data required for detection by the infrared camera module according to the position and distance of the suspected target in real time by calculation, and adjusting the posture of the infrared camera module in real time according to the second posture data;

[0048] S400, obtaining image data collected by the infrared camera module in real time through the terahertz wireless close-range communication module, and identifying whether a suspected target in the image data is a real target through an AI recognition algorithm.

[0049] In a specific implementation, the millimeter wave radar detection module includes a millimeter wave radar, which is a radar system that uses electromagnetic waves in the millimeter wave (30GHz to 300GHz) frequency band for detection and measurement. The millimeter wave radar detection module is installed on a detection device, such as a detection vehicle; when operated by a single person, the millimeter wave radar detection module can be installed on a portable device, such as a smart helmet.

[0050] The infrared camera module includes an infrared camera, and the infrared camera module can be installed together with the millimeter wave radar detection module or installed independently. When this solution is used for security protection, the infrared camera module can also be installed on the sight, which can be handheld or installed on the striking equipment to carry out safe strikes.

[0051] The terahertz (THz) band is located between microwaves and infrared rays, with a frequency range of approximately 0.1 to 10 THz. Terahertz waves have the advantages of broadband, short wavelength, high resolution and good penetration ability, which give them significant advantages in high-precision detection and high-speed data transmission. Transmitting data through the terahertz wireless close-range communication module can increase the data transmission speed and realize the real-time detection. In addition, the use of terahertz wireless close-range communication can reduce a large number of cables, reduce the equipment space of the entire detection system, reduce the weight, and extend the battery life in battery-powered mode. In addition, the relative positions of the devices in the system can also be more flexible. In order to realize the communication between the system's computing terminal and the millimeter-wave radar detection module and infrared camera module, the terahertz wireless close-range communication module can be installed on the computing terminal, the millimeter-wave radar detection module and the infrared camera module.

[0052] In addition, this solution first obtains detection data in real time through the millimeter-wave radar, that is, the millimeter-wave radar detection module is always working. When the millimeter-wave radar detects a suspected target, the infrared camera is used to identify the target. The adjustment of the aiming direction of the infrared camera module can be automatic or manual. In order to facilitate the adjustment of the aiming direction of the infrared camera module, a position sensor can be installed on the millimeter-wave radar detection module and the infrared camera module. The first posture data of the millimeter-wave radar detection module and the radar detection data collected by the millimeter-wave radar detection module can be used to calculate the direction and distance of the suspected target in real time. According to the direction and distance of the suspected target, the second posture data required for accurate detection by the infrared camera module can be calculated in real time, so that the posture of the infrared camera module can be adjusted in real time according to the second posture data.

[0053] This solution can use AI recognition algorithms for accurate identification, and use the edge computing unit constructed by the AI ​​algorithm network to analyze and process infrared images, so as to realize automatic identification and tracking of drone targets. In other implementations, other deep learning algorithms can also be used, which are not limited by this application.

[0054] In a specific implementation, after the AI ​​recognition algorithm is used to identify whether the suspected target in the image data is a real target, the method further includes:

[0055] When a suspected target is determined to be a real target, the distance and speed of the real target are actually calculated based on the radar detection data and image data, and displayed and marked through a display device (for example, the target is framed in the image) for precise monitoring. Even intuitive aiming reticles can be given to assist in aiming based on the model and performance of the strike equipment. The user can move the sight as needed to align the aiming line with the target as quickly as possible to carry out the strike.

[0056] This solution complements millimeter-wave radar detection and infrared camera detection. When the millimeter-wave radar detects a suspected target, infrared camera is used for target identification. This can make up for the blind spots and shortcomings of the millimeter-wave radar, accurately identify targets such as drones in the air, and improve detection accuracy. In addition, terahertz wireless close-range communication is used for data transmission, which can utilize the wider bandwidth and higher frequency of the terahertz frequency band to greatly shorten the synchronization time of the entire system, and can complete the capture of the object and the transmission of the video within 10ms, thereby improving detection efficiency. In addition, the use of terahertz wireless close-range communication can reduce a large number of cables, thereby reducing the equipment space and weight of the entire detection system.

[0057] In a specific implementation, the present solution also includes a display device, which may be a display screen, etc. When a single person is detecting and a smart helmet is used, the display device may also be an eyepiece or AR glasses of the smart helmet for the convenience of user observation. When a suspected target appears within the detection range according to radar detection data, it also includes: sending warning information to the display device through a terahertz wireless close-range communication module, such as a sound warning, a screen warning, etc., and a vibrator may also be set on the smart helmet to remind the user by vibration.

[0058] In one embodiment, the present solution can adjust the aiming direction of the infrared camera module automatically or manually. After calculating the direction and distance of the suspected target, it can also include: obtaining the current posture data of the infrared camera module through the terahertz wireless close-range communication module; when the current posture data changes, it is determined that the user has the intention to find the target, and at this time, the second posture data required for the infrared camera module to detect can be calculated based on the direction and distance of the suspected target.

[0059] When specifically adjusting the infrared camera module, the image data collected by the infrared camera module is sent to the display device for display through the terahertz wireless close-range communication module, and the position of the suspected target is marked in the display screen; the azimuth difference between the azimuth of the suspected target and the current aiming direction of the infrared camera module is calculated based on the current posture data and the second posture data, and the guidance prompt for adjusting the aiming direction of the infrared camera module is set and displayed based on the azimuth difference and the position of the suspected target marked in the display screen, so as to help the user aim the sight at the direction of the target as soon as possible so as to acquire the target image.

[0060] In one embodiment, the AI ​​recognition algorithm is used to identify whether a suspected target in the image data is a real target, specifically including:

[0061] Build an AI recognition algorithm model for target recognition and train the target recognition;

[0062] The image data is divided into dynamic feature vectors and static feature vectors, and MPCA attention processing is performed on the dynamic feature vectors and static feature vectors. The MPCA (Multi-Linear Principal Component Analysis) attention mechanism is a technology used to enhance model performance in the field of deep learning. It improves the expressiveness of features by modeling attention in different dimensions (such as channels, space, etc.);

[0063] The processed dynamic feature vector and the static feature vector are concatenated to obtain a first feature vector;

[0064] The first eigenvector is processed by the output result of the loss function in the training process to obtain a second eigenvector associated with the target. In AI algorithms, the LOSS result usually refers to the output of the loss function, which is used to measure the difference between the predicted value and the true value of the model. The first eigenvector is processed by the output result of the loss function to obtain a second eigenvector associated with the target that is closer to the true value.

[0065] The second eigenvector is input into the GeLU and fully connected layers of the AI ​​recognition algorithm model to determine whether the suspected target is a real target. GeLU (Gaussian Error Linear Units) is an activation function that can be used in conjunction with the fully connected layer to improve model performance and training efficiency.

[0066] In one embodiment, the reference specification Figures 2 to 4The present application provides an identification and tracking system based on the identification and tracking method of the above-mentioned embodiment, including: a smart helmet, a sight and a computing terminal 30. The smart helmet includes a helmet body, on which a millimeter-wave radar detection module 11 is movably mounted, the millimeter-wave radar detection module 11 includes a first attitude sensor, and the millimeter-wave radar detection module 11 can be mounted on a base 12 arranged on the top of the helmet body, and is movably connected to the base 12 through a rotating shaft 13, so that the detection direction of the millimeter-wave radar detection module 11 can be adjusted.

[0067] An infrared camera module 21 is installed at one end of the sight, and a second attitude sensor 22 is installed on the sight, and the other end of the sight is an automatic observation eyepiece 23. The sight is a handheld sight or the sight can be installed on the strike equipment, so that when the system accurately detects the drone target, it can perform a precise strike.

[0068] The computing terminal 30 is connected to the millimeter wave radar detection module 11, the infrared camera module 21, the first posture sensor and the second posture sensor through the terahertz wireless close communication module. For easy carrying, the computing terminal 30 can be a shoulder-type terminal or a backpack terminal.

[0069] The helmet body is also provided with AR glasses 14 and a projection device arranged toward the AR glasses 14. The projection device is communicatively connected with the computing terminal 30 and is used to project the projection picture sent by the computing terminal to the AR glasses 14 so as to be used as the display device described in the aforementioned embodiment.

[0070] The specific working process of the identification and tracking system of this scheme is as follows:

[0071] S1. The millimeter-wave radar detection module (which can be fixed on the helmet) is responsible for all-weather monitoring of the surrounding environment, especially the low-altitude environment, and detecting and tracking targets such as low-altitude drones. The detection data is continuously transmitted to the computing terminal, such as the edge computing unit, in real time through the terahertz wireless close-range communication component (which can also be fixed on the helmet) connected to the millimeter-wave radar detection module. At the same time, the attitude sensor built into the millimeter-wave radar detection module also sends the attitude (pitch, azimuth, etc.) data of the current millimeter-wave radar module to the edge computing unit through the wireless close-range communication component.

[0072] S2. The infrared high-definition camera on the sighting device (generally installed on the strike equipment) can capture the image information in the direction of the sight, and the image information is sent to the edge computing unit through the terahertz wireless close-range communication component on the sight. The attitude sensor built into the sight also sends the attitude (pitch, azimuth, etc.) data of the current millimeter-wave radar module to the edge computing unit through the wireless close-range communication component. However, at the beginning, only the millimeter-wave radar detection module works. When a suspected target is determined to appear, the sighting device is used for accurate detection and identification.

[0073] S3. When the edge computing unit detects a suspected target based on the millimeter-wave radar data, the edge computing unit will send a warning message (warning icon, character, etc.) to the glasses (display device) through the wireless close-range communication component to remind the user. At this time, if the posture sensor on the sight senses that the user has raised the sight device, it is considered that the user is looking for the target intentionally. The edge computing unit will project the image of the sight onto the glasses (display device), and calculate the direction and distance of the target through the posture data and detection data of the radar module, and then compare the posture data of the sight to obtain the direction difference between the target direction and the current aiming direction of the sight, and then make a guidance prompt on the glasses to help the user aim the sight at the direction of the target as soon as possible so as to obtain the target image.

[0074] S4. The edge computing unit compares the target image data sent back by the aiming device with the data captured by the millimeter-wave radar, identifies and tracks the target in the image through the AI ​​algorithm network, and compares and verifies it with the distance, speed and direction measured by the radar module (including the attitude sensor), thereby improving the observation and recognition accuracy of targets such as low-altitude drones.

[0075] S5. After confirming that the target is a low-altitude drone or other target rather than interference such as birds, the system will frame the target on the image (the image source is the sight) sent to the glasses (display device), mark its distance and speed, and even provide intuitive aiming reticles to assist in aiming based on the model and performance of the strike equipment. The user can move the sight as needed to align the aiming line with the target as quickly as possible to carry out the strike.

[0076] It should be noted that the above embodiments can be freely combined as needed. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention.

Claims

1. A method for rapid identification and tracking of low-altitude targets, used for identification and tracking of unmanned aerial vehicles, characterized in that: Includes steps: Acquire radar detection data within a detection range collected by a millimeter-wave radar detection module and first posture data of the millimeter-wave radar detection module in real time through a terahertz wireless close-range communication module; When it is determined according to the radar detection data that a suspected target appears within the detection range, the direction and distance of the suspected target are obtained by real-time calculation according to the first posture data and the radar detection data; Calculating in real time according to the orientation and distance of the suspected target to obtain second posture data required for detection by the infrared camera module, and adjusting the posture of the infrared camera module in real time according to the second posture data; The image data collected by the infrared camera module is acquired in real time through the terahertz wireless close-range communication module, and the AI ​​recognition algorithm is used to identify whether the suspected target in the image data is a real target.

2. The identification and tracking method according to claim 1, characterized in that: When judging that a suspected target appears within the detection range according to the radar detection data, the method further includes: The warning information is sent to the display device through the terahertz wireless close-range communication module.

3. The identification and tracking method according to claim 2, characterized in that: After the calculation obtains the direction and distance of the suspected target, the method further includes: Acquiring current posture data of the infrared camera module through the terahertz wireless close-range communication module; When the current posture data changes, it is determined that the user has an intention to find a target, and the second posture data required for detection by the infrared camera module is obtained by calculation according to the orientation and distance of the suspected target.

4. The identification and tracking method according to claim 3, characterized in that: Also includes: The image data collected by the infrared camera module is sent to the display device for display through the terahertz wireless close-range communication module, and the position of the suspected target is marked in the display screen; The azimuth difference between the azimuth of the suspected target and the current aiming direction of the infrared camera module is calculated based on the current posture data and the second posture data, and a guidance prompt for adjusting the aiming direction of the infrared camera module is set and displayed based on the azimuth difference and the position of the suspected target marked in the display screen.

5. The identification and tracking method according to claim 1, characterized in that: The step of identifying whether the suspected target in the image data is a real target by using an AI recognition algorithm specifically includes: Build an AI recognition algorithm model for target recognition and train the target recognition; Dividing the image data into dynamic feature vectors and static feature vectors, and performing MPCA attention processing on the dynamic feature vectors and the static feature vectors; Concatenate the processed dynamic feature vector and the static feature vector to obtain a first feature vector; Processing the first feature vector by using the loss function output result in the training process to obtain a second feature vector associated with the target; The second feature vector is input into the AI ​​recognition algorithm model to determine whether the suspected target is a real target.

6. The identification and tracking method according to claim 2, characterized in that: After the AI ​​recognition algorithm is used to identify whether the suspected target in the image data is a real target, the method further includes: When the suspected target is determined to be a real target, the distance and speed of the real target are actually calculated based on the radar detection data and the image data, and are displayed and marked through the display device.

7. An identification and tracking system based on the identification and tracking method according to any one of claims 1 to 6, characterized in that: include: A smart helmet, the smart helmet comprising a helmet body, the millimeter-wave radar detection module being movably mounted on the helmet body, the millimeter-wave radar detection module comprising a first posture sensor; A sight, one end of which is equipped with the infrared camera module, and a second attitude sensor is installed on the sight; A computing terminal is communicatively connected with the millimeter wave radar detection module, the infrared camera module, the first posture sensor and the second posture sensor through a terahertz wireless close-range communication module.

8. The identification and tracking system according to claim 7, characterized in that: The helmet body is provided with AR glasses and a projection device arranged toward the AR glasses. The projection device is communicatively connected with the computing terminal and is used for projecting a projection picture sent by the computing terminal to the AR glasses.

9. The identification and tracking system according to claim 7, characterized in that: The computing terminal is a shoulder-type terminal or a backpack-type terminal.

10. The identification and tracking system according to claim 7, characterized in that: The sight is a handheld sight or is installed on a striking device.

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