Unmanned aerial vehicle monitoring system

Abstract

The invention discloses an unmanned aerial vehicle monitoring system which is designed on an embedded software architecture layer, adopts a hierarchical data processing architecture and comprises a peripheral driving layer, a protocol analysis layer, a data management layer, a tracking and capturing layer and an information reporting layer so as to ensure high efficiency and expandability of the system. After unmanned aerial vehicle identification, the system has an unmanned aerial vehicle capturing and tracking function, and combines GNSS positioning information and a dynamic trajectory prediction algorithm to calculate the real-time position and flight trajectory of the unmanned aerial vehicle, so that effective monitoring of the unmanned aerial vehicle is realized, and the real-time safety of a low-altitude airspace is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle monitoring, and particularly to an unmanned aerial vehicle monitoring system. Background Art

[0002] In recent years, the global civilian unmanned aerial vehicle (commonly known as civilian UAV) industry has developed rapidly. Due to its simple operation, fast speed and flexibility, it has been widely used in agriculture, forestry, electric power, meteorology, ocean monitoring, remote sensing mapping, logistics, emergency rescue and other fields. However, due to its easy modification and difficult prevention, the phenomena of "unauthorized flight" and "random flight" are likely to occur, which has a certain impact on national security and public safety. Therefore, the demand for monitoring and controlling UAVs, maintaining air order and ensuring real-time safety of low-altitude airspace is increasing.

[0003] At present, there are mainly four positioning methods for UAVs, namely active radar positioning, sound-based positioning, optical recognition and tracking, and passive positioning. Among them, active radar positioning uses a radar to actively emit signals and locates the UAV by receiving the signals reflected by the UAV fuselage. The azimuth, speed and other information of the UAV target are mainly obtained by analyzing the echo information. Sound-based positioning analyzes the texture features of the sound of the propeller when the UAV is flying, and then matches with the sound information in the database to identify and locate the UAV. Optical recognition and tracking are divided into two categories: infrared recognition and tracking and visible light recognition and tracking. Among them, infrared recognition and tracking detects the infrared information of the UAV target to identify and track the UAV. Visible light recognition and tracking obtains the target image through a visible light camera and then identifies and tracks the UAV. Passive positioning refers to obtaining the target position by receiving the radiation information emitted by the target itself. During the process of positioning the UAV, the receiver does not need to emit signals, and only needs to analyze the spectral characteristics of the received UAV signals to solve the position information of the UAV.

[0004] To establish a regulatory system for drones, the state has currently introduced relevant policies. The State Administration for Market Regulation (Standardization Administration) has issued the mandatory national standard "Safety Requirements for Civil Unmanned Aerial Vehicle Systems" (GB 42590-2023). This standard is the first mandatory national standard in the field of civil drones in China and applies to micro, light, and small civil drones other than model aircraft. It puts forward mandatory technologies for remote identification of drones and corresponding test methods, stipulating that unmanned aerial vehicles should automatically broadcast identification information through wireless local area network (Wi-Fi) or Bluetooth during flight. The identification information includes the identity information and location information of the flying drone, such as the flight speed, take-off location, and serial number of the drone, which is equivalent to embedding a "digital ID" in the drone. Ground personnel or equipment can view the automatically broadcast identification information of the drone to achieve all-round and all-weather monitoring of drones in the area, so as to ensure the safety of the regional airspace.

[0005] Therefore, it is particularly important to establish a regulatory system for drones, especially in key areas such as military activity areas, national security units, and important gathering places. It is necessary to seek an integrated surveillance system and method for drones to maintain air order and ensure the real-time safety of low-altitude airspace. Summary of the Invention

[0006] The present invention provides a drone monitoring system that can effectively monitor drones, thereby ensuring the real-time safety of low-altitude airspace.

[0007] The present invention provides a drone monitoring system, comprising: a signal recognition module, a signal receiving and processing module, a central information processing module, a network communication module, and a satellite positioning module; the signal recognition module is responsible for the front-end reception and preliminary processing of signals, and sends the processed signals to the signal receiving and processing module; the signal receiving and processing module adopts a multi-channel parallel demodulation architecture, and three radio signal recognition units are respectively used for the reception and analysis of 2.4GHz Wi-Fi, 5.8GHz Wi-Fi, and Bluetooth broadcast signals, and send the analyzed data to the central information processing module; the central information processing module constructs a hierarchical data management architecture, responsible for integrating, caching, and priority management of the received data, and combines GNSS timing information to perform time synchronization and trajectory association calculation on the data of each channel; the network communication module is responsible for remote upload of data and construction of a distributed system; the satellite positioning module integrates a high-precision GNSS receiving unit, and performs timing correction and spatial association analysis on the collected drone data; the embedded software of this system adopts a hierarchical processing architecture, and the embedded software includes a peripheral driver layer, a protocol parsing layer, a data management layer, a tracking and capture layer, and an information reporting layer; among them, the peripheral driver layer is carried out in the signal recognition module to complete the functions of front-end signal acquisition, demodulation, and dynamic gain adjustment; the protocol parsing layer is carried out in the signal receiving and processing module to perform multi-protocol matching, multi-threaded parsing, and multi-signal format compatibility functions of drone signals; the data management layer and the tracking and capture layer are completed in the central information processing module, and the data management layer classifies and organizes data, performs time synchronization, deduplication, and data grouping; the tracking and capture layer corresponds to the functions of target trajectory calculation, target trajectory prediction, multi-station distributed tracking, target relay tracking, blacklist target locking, and trajectory storage in the central information processing module and the satellite positioning module; the information reporting layer corresponds to the functions of TCP / UDP / MQTT real-time reporting, network status switching, and abnormal alarm linkage in the network communication module.

[0008] Specifically, the central information processing module sends initialization configuration instructions to the signal recognition module and the satellite positioning module to enable the peripheral driver layer. The configuration instructions include enabling three radio identification terminals, configuring the working modes of the three radio identification terminals to be 2.4GHz / 5.8GHz WiFi promiscuous listening mode and Bluetooth broadcast frame capture mode respectively, and enabling the satellite positioning module to output site positioning and timing information to the central information processing module; after the initialization configuration instructions are completed, the peripheral driver layer starts to monitor and capture multi-mode UAV signals; among them, UAV signals in 2.4GHz / 5.8GHz Wi-Fi broadcast mode are received through promiscuous listening mode, and the corresponding two radio identification segments are locked on channel 6 of the 2.4GHz band and channel 149 of the 5.8GHz band. In the promiscuous listening mode of this band, all broadcast data frames broadcast by the WiFi protocol, regardless of the mac address, will be received; after receiving the broadcast packet data, OFDM demodulation is performed to extract subcarrier data and restore the complete Wi-Fi data frame. Subsequently, timing synchronization and channel equalization are performed. The demodulated Wi-Fi data frame restores the clock by capturing the frame header, corrects the distortion caused by data displacement and multipath interference, and completes the extraction of the WiFi broadcast data packet; for UAV signals broadcast on the Bluetooth channel, start BLE broadcast channel monitoring, lock on Bluetooth channel broadcast channel 37 for monitoring, and extract all Payload data of the broadcast packet in this band through GFSK demodulation; finally, the data output by the peripheral driver layer includes all WiFi broadcast data packets containing UAV signals and all Bluetooth broadcast data packets containing UAV signals, and stores the data in the corresponding memory address in the signal recognition module for the protocol parsing layer to screen and parse; at the same time, the satellite positioning module outputs site positioning and timing information to the central information processing module for the relevant functions of time synchronization in the data management layer and the tracking and capture layer of the central information processing module.

[0009] Specifically, it further includes:

[0010] Regularly extract the received signal strength indication RSSI of all current WiFi broadcast data packets once for mean estimation, and perform dynamic adjustment of the front-end gain. When the mean value of the received WiFi broadcast data signal strength indication RSSI is lower than the first threshold, enable the front-end low-noise amplifier with a gain of 16dB to amplify the received signal; when the mean value of the signal strength indication RSSI is higher than the second threshold, enable the front-end attenuator with a front-end attenuation gain of -15.5dB to reduce the gain and avoid front-end overload distortion.

[0011] Specifically, the protocol parsing layer first performs pre-screening of data packet protocol identification, starts multi-protocol matching and multi-threaded parsing threads, and the specific processing flow includes: filtering out valid drone broadcast data packets based on the characteristic fields of the target signal broadcast data frame; discarding data packets that are not in the preset protocol or do not meet the format requirements; for qualified drone information packets, extracting Remote ID related fields; after each valid data packet is processed, the protocol parsing layer transmits it to the central information processing module.

[0012] Specifically, the data management layer classifies and organizes the data according to the unique ID of the drone; the timestamp of each data packet is aligned with the timing time provided by the satellite positioning module; after the data management layer completes the information classification and time alignment, the stored, classified and aligned data is stored in the corresponding memory address for the tracking and capture layer application to read and process, and the online drone identification information is transmitted to the network communication module for use in network communication.

[0013] Specifically, the information reporting layer is responsible for the communication between the system and the external interface, including reporting of drone information, network status detection and switching, abnormal drone target detection and alarm; the workflow includes: retrieving and reading the drone information stored in the corresponding memory address by the data management layer; at the same time, querying the drone blacklist pre-stored in the corresponding memory address for comparison. If it is not a blacklisted drone, it is transmitted to the network communication module; if it is a blacklisted drone, an alarm mark is added to the output data format.

[0014] Specifically, the information reporting layer also includes:

[0015] Read the RSRP signal strength of the 4G network regularly. When the RSRP signal strength is less than the set threshold, report the poor signal quality status information in the standardized JSON structure format data, stop reporting the drone Remote ID information, and switch to delayed upload mode. In this mode, the data is cached in the local memory until the 4G network is restored to normal and then the drone Remote ID information reporting is started.

[0016] Specifically, the tracking and capturing layer retrieves and reads the UAV information stored at the corresponding memory address by the data management layer, and compares it with the positioning information of the ground station itself. At this time, the ground station calculates the distance based on its current position information and the position information of the UAV, and preliminarily determines the spatial position of the UAV. The movement trajectory of the UAV is calculated using the real-time position information and speed information in the UAV information. Each time a new frame of UAV information is received, the trajectory points of the target are recorded and updated and smoothed. Through multiple updates, the movement path of the UAV is initially obtained. Subsequently, the UAV tracking mode is enabled, and the trajectory of the UAV is predicted through the Kalman filter algorithm, and the position of the UAV is updated when new UAV information is received.

[0017] Specifically, predicting the trajectory of the UAV through the Kalman filter algorithm and updating the position of the UAV when new UAV information is received includes: using the Kalman filter to predict the position of the UAV at the next moment by inputting the current position and speed data of the UAV; based on the historical data of the UAV, assuming that the UAV continues to move along the current trajectory, and adjusting the state estimation of the UAV through the update step, comparing the predicted position with the actual observation value, and correcting the trajectory of the UAV; finally outputting the updated position and speed of the UAV.

[0018] Specifically, the tracking and capturing layer further includes: if multiple ground stations are working in the same coverage area, each ground station in the area subscribes to and publishes information under the same topic through the MQTT protocol to share the working state and UAV information with other stations. The data format is the same as the UAV information and the station status information, which is used to form a distributed tracking network. In the tracking network, each station can obtain the information of the same UAV from other stations in real time, merge the UAV trajectory information from different sources, and predict the trajectory of the UAV through the merged trajectory information to achieve cross-station UAV relay tracking.

[0019] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0020] (1) The present invention is designed at the embedded software architecture layer, adopting a hierarchical data processing architecture, including a peripheral driver layer, a protocol parsing layer, a data management layer, a tracking and capturing layer, and an information reporting layer, to ensure the high efficiency and scalability of the system, and support remote firmware upgrade, adaptive interference detection, and heartbeat packet monitoring. After the UAV is identified, the system has the UAV capture and tracking function. Combining GNSS positioning information with a dynamic trajectory prediction algorithm, the real-time position and flight trajectory of the UAV are calculated, realizing effective monitoring of the UAV, thereby ensuring the real-time safety of the low-altitude airspace.

[0021] (2) The present invention also supports a multi-station collaborative tracking mode. When a station identifies a drone, it can share the target data with other stations through the MQTT protocol to form a distributed tracking network, improving the drone tracking ability in low-signal areas. In addition, the present invention can also perform blacklist tracking on specific drones. When a drone on the blacklist is identified, an alarm is automatically triggered, and the target data is shared with other stations for enhanced tracking to ensure the full monitoring of the drone. This optimization scheme enables the Remote ID identification system to be not only an information collection terminal but also capable of accurately capturing drones, predicting their trajectories, and performing multi-station collaborative tracking, thereby improving the airspace safety management ability and meeting more complex drone monitoring requirements.

[0022] (3) The present invention is also connected to other parts of the system through the provided SMA interface, J30J interface, and network cable interface, realizing the miniaturization and modularization of the Remote ID identification system. As a result, the Remote ID identification system can be more easily integrated with other systems, and the modular processing also increases the stability and replaceability of the system.

[0023] (4) The present invention provides 1 antenna interface for remote identification signals of drones in the 5.8GHz WiFi band, 1 antenna interface for remote identification signals of drones in the 2.4GHz WiFi band, and 1 antenna interface for remote identification of drones in the 2.4GHz Bluetooth 4, 5 bands, covering all types of remote identification signals of drones required by current national standard documents. Thus, the function of the Remote ID identification system is more comprehensive, and while reducing the space occupied by the system, the function of identifying and receiving drone signals is fully retained.

[0024] (5) The present invention has reserved a network port behind the embedded processor, which can establish a connection with the MQTT platform through a subsequent connected networking module, enabling the data processed by the embedded processor to be transmitted to the MQTT platform through the networking module, thereby establishing a connection between multiple identification system stations and constructing an integrated monitoring network. At the same time, the working status of the Remote ID identification system is reported to the MQTT platform regularly through heartbeat packets, and then remote maintenance is performed according to the station status. Thus, the Remote ID identification system constructs a regional Remote ID identification network through network communication protocols, realizes multi-aircraft interconnection, and can also perform simpler remote maintenance through the network.

[0025] (6) The present invention realizes the reception of all message types in the national standard file in the embedded software. At the same time, the received signals are classified and processed in the subsequent embedded processor, and the Remote ID broadcast signal is converted into a more intuitive text message. Meanwhile, when there are multiple drones broadcasting Remote ID in the area, the Remote ID designed by the present invention batches and packages each type of message by the drone serial number and uploads it to the MQTT platform for the monitoring personnel to view, so that the Remote ID recognition system can receive all the information sent by the drones without omission, avoiding the missed reporting and misreporting of information.

[0026] (7) The present invention also adds a filter, an attenuator, and a low-noise amplifier in the signal recognition module at the back end of the antenna to further amplify the received signals, thereby increasing the signal reception and recognition range of the recognition system, while increasing the stability of the system, further increasing the signal recognition range of the Remote ID recognition system, and thus reducing the cost of laying ground stations in the same area. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the overall structure of the drone monitoring system provided by the embodiment of the present invention;

[0028] Figure 2 It is a schematic diagram of the overall structure of the embedded software in the drone monitoring system provided by the embodiment of the present invention;

[0029] Figure 3 It is a flowchart of the peripheral driver layer of the drone monitoring system provided by the embodiment of the present invention;

[0030] Figure 4 It is a flowchart of the protocol parsing layer of the drone monitoring system provided by the embodiment of the present invention;

[0031] Figure 5 It is a flowchart of the data management layer of the drone monitoring system provided by the embodiment of the present invention;

[0032] Figure 6 It is a flowchart of the information reporting layer of the drone monitoring system provided by the embodiment of the present invention;

[0033] Figure 7 It is a flowchart of the tracking and capturing layer of the drone monitoring system provided by the embodiment of the present invention;

[0034] Figure 8 It is a first perspective three-dimensional view of the drone monitoring system provided by the embodiment of the present invention;

[0035] Figure 9 It is a second perspective three-dimensional view of the drone monitoring system provided by the embodiment of the present invention;

[0036] Figure 10 It is the first physical diagram of the UAV monitoring system provided by the embodiment of the present invention;

[0037] Figure 11 It is the second physical diagram of the UAV monitoring system provided by the embodiment of the present invention. Detailed implementation manners

[0038] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0039] Such as Figure 1 and Figure 2As shown in the figure, the drone monitoring system provided by the embodiments of the present invention includes: a signal recognition module, a signal receiving and processing module, a central information processing module, a network communication module, and a satellite positioning module. The overall system adopts a distributed parallel signal acquisition architecture, and through the methods of multi-channel independent reception, timing synchronization control, and hierarchical data processing, it ensures the efficient and accurate acquisition and parsing of Remote ID broadcast signals. The signal recognition module is responsible for the front-end reception and preliminary processing of signals, and consists of radio frequency devices such as an antenna interface, an active low-noise amplifier (LNA), an attenuator, and a filter, to improve the signal reception sensitivity, enhance the anti-interference ability, and reduce the possibility of false alarms and missed detections, and send the processed signals to the signal receiving and processing module; the signal receiving and processing module adopts a multi-channel parallel demodulation architecture, and three radio signal recognition units are respectively used for the reception and parsing of 2.4GHz Wi-Fi, 5.8GHz Wi-Fi, and Bluetooth broadcast signals. The system has a dynamic signal allocation mechanism, which can intelligently allocate acquisition resources according to the intensity and bandwidth requirements of the signal source, improve the multi-target parallel parsing ability, and at the same time has a built-in data integrity detection and error correction mechanism to reduce data loss or misjudgment and improve the recognition accuracy. The parsed data is formatted and sent to the central information processing module through the serial port; the central information processing module constructs a hierarchical data management architecture, which is responsible for integrating, caching, and priority management of the received data, and combines GNSS timing information to perform time synchronization and trajectory association calculation on the data of each channel to ensure the timing consistency and spatial accuracy of the data; the network communication module is responsible for the remote upload of data and the construction of a distributed system, supports multiple protocols such as TCP, UDP, MQTT, etc., realizes the interconnection of multiple regional sites, and shares data through the MQTT protocol to form a distributed drone monitoring network, improving the monitoring coverage and multi-station collaboration ability, and at the same time has the functions of remote firmware upgrade (OTA) and network status adaptive adjustment to ensure the stability of communication. The satellite positioning module integrates a high-precision GNSS receiving unit, provides accurate timing and spatial positioning capabilities, and performs timing correction and spatial correlation analysis on the collected drone data to improve the spatio-temporal consistency of the data, thereby ensuring the accuracy of remote monitoring; in addition, this module can perform data fusion with external radars, vision sensors, or ADS-B systems to achieve multi-source collaboration and improve the reliability of drone identification and tracking. Overall, through the architecture design of multi-channel parallel acquisition, high-precision timing synchronization, intelligent signal processing, and distributed data management, this system significantly improves the reception ability of Remote ID signals, data processing efficiency, and the accuracy and stability of drone monitoring, has good scalability, can be applied to complex airspace environments, and meets various application requirements such as remote drone monitoring, regional network identification, and precise trajectory tracking.The embedded software of this system adopts a hierarchical processing architecture. The embedded software includes a peripheral driver layer, a protocol parsing layer, a data management layer, a tracking and capture layer, and an information reporting layer. Among them, the peripheral driver layer is implemented in the signal recognition module to complete the functions of front-end signal acquisition, demodulation, and dynamic gain adjustment. The protocol parsing layer is implemented in the signal reception and processing module to perform multi-protocol matching, multi-threaded parsing, and multi-signal format compatibility functions for UAV signals. The data management layer and the tracking and capture layer are completed in the central information processing module. The data management layer classifies and organizes data, synchronizes time, removes duplicates, and groups data. The tracking and capture layer corresponds to the functions of target trajectory calculation, target trajectory prediction, multi-station distributed tracking, target relay tracking, blacklist target locking, and trajectory storage in the central information processing module and the satellite positioning module, realizing the efficient target recognition and continuous tracking capabilities of the system. The information reporting layer corresponds to the functions of TCP / UDP / MQTT real-time reporting, network status switching, abnormal alarm linkage, OTA upgrade, and log management in the network communication module.

[0040] Specifically, as Figure 3As shown in the figure, the central information processing module sends initialization configuration instructions to the signal recognition module and the satellite positioning module through the serial port to enable the peripheral driver layer. The configuration instructions include enabling three-way radio identification terminals, configuring the working modes of the three-way radio identification terminals as 2.4GHz / 5.8GHz WiFi promiscuous listening mode and Bluetooth broadcast frame capture mode respectively, and enabling the satellite positioning module to output site positioning and timing information to the central information processing module. After the initialization configuration instructions are completed, the peripheral driver layer starts to monitor and capture multi-mode UAV signals. Among them, the UAV signals in the 2.4GHz / 5.8GHz Wi-Fi broadcast mode are received through the promiscuous listening mode. The corresponding two-way radio identification segments are locked on channel 6 of the 2.4GHz band (center frequency band 2437MHz) and channel 149 of the 5.8GHz band (center frequency band 5745MHz). In the promiscuous listening mode of this frequency band, all broadcast data frames broadcast by the WiFi protocol, regardless of the mac address, will be received. After receiving the broadcast packet data, OFDM demodulation is performed to extract the subcarrier data and restore the complete Wi-Fi data frame. Subsequently, timing synchronization and channel equalization are performed. The demodulated Wi-Fi data frame restores the clock by capturing the frame header, corrects the distortion caused by data displacement and multipath interference, and completes the extraction of the WiFi broadcast data packet. Subsequently, to ensure the integrity of the received data packet and avoid error codes, the CRC field of the data packet is also extracted for verification. If there is an error code, the packet is discarded. For the UAV signals broadcast on the Bluetooth channel, start BLE broadcast channel monitoring, lock on Bluetooth channel broadcast channel 37 for monitoring, and extract all Payload data of the broadcast packet in this frequency band through GFSK demodulation. Finally, the data output by the peripheral driver layer includes all WiFi broadcast data packets containing UAV signals and all Bluetooth broadcast data packets containing UAV signals. By storing the data in the corresponding memory address in the signal recognition module, it is used for the protocol parsing layer to perform screening and parsing. At the same time, the satellite positioning module outputs site positioning and timing information to the central information processing module for the relevant functions of time synchronization in the data management layer and the tracking and capture layer of the central information processing module.

[0041] The embodiment of the present invention can also dynamically adjust the gain of the receiving front end, and further includes:

[0042] Regularly extract the signal strength indicator RSSI of all currently received WiFi broadcast data packets once for mean estimation, and perform dynamic adjustment of the receiving front-end gain. When the mean value of the signal strength indicator RSSI of the received WiFi broadcast data is lower than the first threshold, enable the front-end low-noise amplifier with a gain of 16dB to amplify the received signal. When the mean value of the signal strength indicator RSSI is higher than the second threshold, enable the front-end attenuator with a front-end attenuation gain of -15.5dB to reduce the gain and avoid front-end overload distortion.

[0043] Specifically, asFigure 4 As shown, the protocol parsing layer first performs pre-screening of data packet protocol recognition, starts multi-protocol matching and multi-threaded parsing threads. The specific processing flow includes: screening out valid UAV broadcast data packets according to the characteristic fields of the target signal broadcast data frame; discarding data packets with non-preset protocols or not meeting the format requirements; for UAV information packets that meet the conditions, extracting relevant Remote ID fields, including: UAV unique ID (UAS ID), real-time position information of the UAV and the pilot (longitude, latitude, altitude), flight speed, flight status code, etc. fields. At the same time, the system compares the repeated content broadcast by the same UAV within 1s, performs data deduplication processing to avoid repeated parsing and save the computing power of the protocol parsing software layer; specifically, for the UAV Remote ID signal broadcast using the WiFi Beacon frame, the vendor code (VendorOUI) in the extended field is 221 (0xFA, 0x0B, 0xBC), and the frame control field (Frame Type) is 0x08 (Type = 0, Subtype = 8). For the UAV Remote ID signal broadcast using Bluetooth, in the Bluetooth broadcast data frame format, the preamble is 0xAA, the access address is 0x8E89BED6, and in the Payload content, the third and fourth bytes of the ServiceUUID are 0xFFFA, followed by the standard flag 0x0D (open drone id) of the Remote ID broadcast data packet. The data packet protocol recognition and screening pass by identifying whether the data packet matches the above fields. After each valid data packet is processed, the protocol parsing layer transmits it to the central information processing module. The protocol parsing layer outputs standard structured data in JSON format, and the fields include but are not limited to: drone_id (UAV unique identification code), protocol_type (WiFi / BLE channel identifier), uav_position (UAV longitude, latitude, altitude), pilot_position (pilot longitude, latitude, altitude), velocity (UAV flight speed), track_angle (UAV track angle), timestamp (UAV broadcast timestamp), rssi (signal strength), etc.

[0044] Specifically, such as Figure 5As shown, the data management layer classifies and organizes data according to the unique ID of the UAV; ensures that the data of each UAV is stored independently to avoid data confusion for subsequent interface monitoring, analysis, and uploading. The timestamp of each data packet is aligned with the timing provided by the satellite positioning module; after the data management layer completes information classification and time alignment, it stores the stored, classified, and aligned data into the corresponding memory address for the tracking and capture layer application to read and process. At the same time, it transmits the identification information of the online UAV to the network communication module for network communication use. After the data management layer finally stores and outputs, it outputs data in the format of a standardized JSON structure. The specific fields include but are not limited to: sn (site serial number), station_location (site longitude, latitude, altitude), station_timestamp (site timing timestamp), drone_id (unique UAV identification code), protocol_type (WiFi / BLE channel identifier), uav_position (UAV longitude, latitude, altitude), pilot_position (pilot longitude, latitude, altitude), velocity (UAV speed), track_angle (UAV track angle), timestamp (UAV broadcast timestamp), rssi (signal strength), etc.

[0045] Specifically, as Figure 6As shown in the figure, the information reporting layer is responsible for the communication between the system and external interfaces, including the reporting of drone information, network status detection and switching, and the detection and alarm of abnormal drone targets. The workflow includes: retrieving and reading the drone information stored in the corresponding memory address by the data management layer; at the same time, querying the drone blacklist pre-stored in the corresponding memory address for comparison. If it is not a blacklisted drone, it will be transmitted to the network communication module through the Ethernet interface via TCP, UDP, and MQTT network protocols in the format of standardized JSON structured data. The specific fields include but are not limited to: sn (site serial number), station_location (site longitude, latitude, altitude), station_timestamp (site timestamp), drone_id (unique drone identification code), protocol_type (WiFi / BLE channel identifier), uav_position (drone longitude, latitude, altitude), pilot_position (pilot longitude, latitude, altitude), velocity (drone speed), track_angle (drone track angle), timestamp (drone broadcast timestamp), rssi (signal strength), etc. If it is a blacklisted drone, an alarm flag will be added to the output data format, including "target flying in", "target intercepted", etc. If external auxiliary detection devices are used in combination, such as cameras, optoelectronic radars, etc., the system will report the information to the external auxiliary detection devices through the Ethernet interface for external joint auxiliary tracking of blacklisted drones.

[0046] For further explanation of the information reporting layer, the information reporting layer also includes:

[0047] Read the RSRP signal strength of the 4G network once at regular intervals. When the RSRP signal strength < the set threshold, report the poor signal quality status information in the format of standardized JSON structure data. The specific fields include but are not limited to: sn (site serial number), station_location (site longitude, latitude, altitude), station_timestamp (site timestamp), RSRP (4G network signal strength), etc. At this time, stop reporting the drone Remote ID information and switch to the delayed upload mode. In this mode, the data is cached in the local memory until it is queried that the 4G network returns to normal and then start reporting the drone Remote ID information. In addition, this system also supports the OTA remote upgrade function, and the system firmware can be remotely updated through the online network to ensure the system functions and security. After each upgrade is completed, the system will automatically verify the integrity and security of the upgrade package to prevent malicious software or incomplete upgrade packages from being installed. The system also supports the log management function. The system will continuously record all key events and operations (such as network status switching, data upload, abnormal target detection, etc.). These logs will be regularly uploaded to the background server and can be viewed and analyzed by the operation and maintenance personnel. The system logs are in the same data output format as the above system data. In addition, this system also supports the heartbeat packet function. The system reports the working status of the device to the background server regularly through the heartbeat packet mechanism to ensure that the online status and health status of the device can be monitored at any time. The system reports the working status information once every 30s through the network in the format of standardized JSON structure data. The specific fields include but are not limited to: sn (site serial number), station_location (site longitude, latitude, altitude), station_timestamp (site timestamp), channel_status (working status of the signal recognition end), RSRP (4G network signal strength), etc.

[0048] Specifically, as Figure 7 shown, the tracking and capture layer retrieves and reads the drone information stored at the corresponding memory address in the data management layer, including longitude, latitude, and altitude, and compares it with the positioning information of the ground station itself (longitude, latitude, altitude of the site); at this time, the ground station will calculate the distance based on its current position information and the position information of the drone and initially determine the spatial position of the drone; use the real-time position information (including longitude, latitude, and altitude) and speed information in the drone information to calculate the movement trajectory of the drone; each time a new frame of drone information is received, record and update the trajectory points of the target and perform smoothing processing. Through multiple updates, initially obtain the movement path of the drone; then start the drone tracking mode, predict the trajectory of the drone through the Kalman filter algorithm, and update the position of the drone when new drone information is received, so as to ensure the stability and accuracy of the tracking.

[0049] Specifically, the trajectory of the UAV is predicted by the Kalman filtering algorithm, and the position of the UAV is updated when new UAV information is received, including: using the Kalman filter to predict the position of the UAV at the next moment by inputting the current position and speed data of the UAV; based on the historical data of the UAV, by assuming that the UAV continues to move along the current trajectory and adjusting the state estimation of the UAV through the update step, comparing the predicted position with the actual observation value, and correcting the trajectory of the UAV; finally, outputting the updated position and speed of the UAV. After the tracking and capture function is started, the format of the UAV Remote ID data output by the system is data in the format of a standardized JSON structure. The specific fields include but are not limited to: sn (site serial number), station_location (site longitude, latitude, altitude), station_timestamp (site time stamp), drone_id (unique UAV identification code), protocol_type (WiFi / BLE channel identification), uav_position (UAV longitude, latitude, altitude), pilot_position (pilot longitude, latitude, altitude), velocity (UAV speed), track_angle (UAV track angle), predicted target position (predicted_position), predicted speed (predicted_velocity), timestamp (UAV broadcast time stamp), rssi (signal strength). When the normal RemoteID information is lost during reception, only the position and information predicted according to the previous trajectory are included in the reported information. In this way, the Kalman filter can effectively smooth the trajectory, compensate for the position error caused by the loss or interference of the UAV Remote ID signal, and ensure the accuracy and stability of target tracking.

[0050] In addition, the embodiment of the present invention also has a multi-site collaborative tracking function. Specifically, the tracking and capture layer further includes: if multiple ground stations work in the same coverage area, each ground station in the area subscribes to and publishes information under the same topic through the MQTT protocol to share the working status and UAV information with other stations. The data format is consistent with the UAV information and the site status information, and is used to form a distributed tracking network; in the tracking network, each site can obtain the information about the same UAV from other sites in real time, merge the UAV trajectory information from different sources, and predict the trajectory of the UAV through the merged trajectory information to achieve cross-site UAV relay tracking.

[0051] The mechanical part of the UAV monitoring system provided by the embodiment of the present invention will be specifically described below:

[0052] Such as Figure 8 、Figure 9 , Figure 10 , Figure 11 As shown in Figure 11 , the mechanical part consists of a housing structure, an external interface structure, and a circuit board mounting structure. The housing structure is made of high-strength metal material and is designed as a cuboid shape overall, with good electromagnetic shielding performance and environmental adaptability to ensure the stable operation of the system under complex climate conditions. Four SMA-type connectors are arranged side by side on one side, which can be connected to an external antenna through a standard SMA interface to achieve efficient reception of multi-band signals. A network port, a J30J-9-pin interface, and a power connection hole are arranged side by side on the other side. The network port is used to connect to the networking module to support the system to access the network. The J30J-9-pin interface includes two groups of serial ports and device configuration ports, which can be used for system data transmission, local device communication, and firmware update. The power port adopts a stable protection design and supports wide-voltage input to ensure reliable operation of the device under different power supply environments. The housing structure has an optimized heat dissipation design, with ventilation and heat dissipation channels provided on the side and bottom to improve the stability of the device during long-term operation. The external housing adopts a modular assembly method, and the outer shell is closed and fixed through ten screw posts to enhance the structural strength and facilitate maintenance. The internal mounting structure also adopts a modular design. Four screw posts are provided inside the housing for fixing the circuit board, and additional mounting space is reserved to facilitate future function expansion or component upgrade. The overall design not only improves the structural strength and heat dissipation performance of the device but also optimizes the interface layout, making the system more durable and reliable while being convenient for installation. All structural parts are processed by precision machining, and the screw holes are processed by drilling and tapping processes and deburred to ensure the high precision and reliability of the mechanical structure.

[0053] The working process of the UAV monitoring system provided by the embodiments of the present invention will be specifically described below:

[0054] First, connect the UAV remote identification system provided by the embodiments of the present invention to the external antenna, power supply interface, network interface, and serial port of the ground station system to ensure the correct docking of all external devices. After the device is powered on, the system performs initialization configuration through the network interface, including setting network communication parameters, GNSS time calibration, and loading of the data parsing module. After the initialization is completed, the ground station system continuously scans the UAV remote identification information in the target frequency band, identifies and parses the RemoteID data, and performs time synchronization and data integration in combination with the GNSS positioning information. After ensuring the integrity of the data through the intelligent duplicate removal and data optimization algorithm, the system uploads it to the remote monitoring platform using TCP, UDP, or MQTT protocols to achieve UAV monitoring and data sharing within the area. The overall process ensures efficient operation through an automated management method and supports remote firmware upgrade and device status monitoring to improve the maintainability and long-term stability of the system.

[0055] In summary, the embodiments of the present invention provide a drone monitoring system, which can effectively monitor drones, thereby ensuring the real-time safety of the low-altitude airspace.

[0056] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0057] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0058] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in Figure 1 one or more of the flows Figure 1 or multiple flows and / or blocks

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide means for realizing the functions specified in Figure 1 one or more of the flows Figure 1Steps of the functions specified in one or more boxes. Where the embodiments of the present invention are not described in detail, they are all well-known technologies in the technical field to which the present invention pertains. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An unmanned aerial vehicle monitoring system, characterized in that, Including: A signal recognition module, a signal receiving and processing module, a central information processing module, a network communication module, and a satellite positioning module; the signal recognition module is responsible for the front-end reception and preliminary processing of signals, and sends the processed signals to the signal receiving and processing module; the signal receiving and processing module adopts a multi-channel parallel demodulation architecture, and three radio signal recognition units are respectively used for the reception and analysis of 2.4GHz Wi-Fi, 5.8GHz Wi-Fi, and Bluetooth broadcast signals, and sends the parsed data to the central information processing module; the central information processing module constructs a hierarchical data management architecture, which is responsible for integrating, caching, and priority management of the received data, and combines GNSS timing information to perform time synchronization and trajectory association calculation on the data of each channel; the network communication module is responsible for remote upload of data and construction of a distributed system; the satellite positioning module integrates a high-precision GNSS receiving unit, and performs timing correction and spatial correlation analysis on the collected UAV data; the embedded software of this system adopts a hierarchical processing architecture, and the embedded software includes a peripheral driver layer, a protocol parsing layer, a data management layer, a tracking and capture layer, and an information reporting layer; among them, the peripheral driver layer is carried out in the signal recognition module to complete the functions of front-end signal acquisition, demodulation, and dynamic gain adjustment; the protocol parsing layer is carried out in the signal receiving and processing module to perform multi-protocol matching, multi-threaded parsing, and multi-signal format compatibility functions of UAV signals; the data management layer and the tracking and capture layer are completed in the central information processing module, and the data management layer classifies and organizes data, performs time synchronization, deduplication, and data grouping; the tracking and capture layer corresponds to the functions of target trajectory calculation, target trajectory prediction, multi-station distributed tracking, target relay tracking, blacklist target locking, and trajectory storage in the central information processing module and the satellite positioning module; the information reporting layer corresponds to the TCP / UDP / MQTT real-time reporting, network status switching, and abnormal alarm linkage functions of the network communication module.

2. The drone monitoring system according to claim 1, characterized in that, The central information processing module sends initialization configuration instructions to the signal recognition module and the satellite positioning module to enable the peripheral driver layer. The configuration instructions include enabling three radio identification terminals, configuring the working modes of the three radio identification terminals to be 2.4GHz / 5.8GHz WiFi promiscuous listening mode and Bluetooth broadcast frame capture mode respectively, and enabling the satellite positioning module to output site positioning and timing information to the central information processing module. After the initialization configuration instructions are completed, the peripheral driver layer starts to monitor and capture multi-mode UAV signals. Among them, the UAV signals in the 2.4GHz / 5.8GHz Wi-Fi broadcast mode are received through the promiscuous listening mode. The corresponding two radio identification segments are locked on channel 6 of the 2.4GHz band and channel 149 of the 5.8GHz band. In the promiscuous listening mode of this band, all broadcast data frames broadcast by the WiFi protocol, regardless of the mac address, will be received. After receiving the broadcast packet data, OFDM demodulation is performed to extract subcarrier data and restore the complete Wi-Fi data frame. Subsequently, timing synchronization and channel equalization are performed. The demodulated Wi-Fi data frame restores the clock by capturing the frame header, corrects the distortion caused by data displacement and multipath interference, and completes the extraction of the WiFi broadcast data packet. For the UAV signals broadcast on the Bluetooth channel, start BLE broadcast channel monitoring, lock on Bluetooth channel broadcast channel 37 for monitoring, and extract all Payload data of the broadcast packet in this band through GFSK demodulation. Finally, the data output by the peripheral driver layer includes all WiFi broadcast data packets containing UAV signals and all Bluetooth broadcast data packets containing UAV signals. By storing the data in the corresponding memory address in the signal recognition module, it is used for the protocol parsing layer to screen and parse. At the same time, the satellite positioning module outputs site positioning and timing information to the central information processing module for the relevant functions of time synchronization in the data management layer and the tracking and capture layer of the central information processing module.

3. The drone monitoring system according to claim 2, characterized in that, It further includes: Regularly extract the received signal strength indication RSSI of all current WiFi broadcast data packets once for mean estimation, and perform dynamic adjustment of the receive front-end gain. When the mean value of the received WiFi broadcast data signal strength indication RSSI is lower than the first threshold, enable the front-end low-noise amplifier with a gain of 16dB to amplify the received signal. When the mean value of the signal strength indication RSSI is higher than the second threshold, enable the front-end attenuator with a front-end attenuation gain of -15.5dB to reduce the gain and avoid front-end overload distortion.

4. The drone monitoring system according to claim 1, wherein The protocol parsing layer first performs pre-screening of data packet protocol recognition, starts multi-protocol matching and multi-threaded parsing threads. The specific processing flow includes: screening out valid UAV broadcast data packets according to the characteristic fields of the target signal broadcast data frame; discarding data packets with non-preset protocols or those that do not meet the format requirements; for UAV information packets that meet the conditions, extracting relevant Remote ID fields; after each valid data packet is processed, the protocol parsing layer transmits it to the central information processing module.

5. The drone monitoring system according to claim 1, wherein, The data management layer classifies and organizes the data according to the unique UAV ID; aligns the timestamp of each data packet with the timing time provided by the satellite positioning module; after the data management layer completes information classification and time alignment, it stores the stored, classified, and aligned data in the corresponding memory address for the tracking and capture layer to read and process, and at the same time transmits the online UAV identification information to the network communication module for network communication use.

6. The drone monitoring system according to claim 1, characterized in that, The information reporting layer is responsible for the work of the system's communication with external interfaces, including reporting of UAV information, network status detection and switching, abnormal UAV target detection and alarm; the work flow includes: retrieving and reading the UAV information stored in the corresponding memory address by the data management layer; at the same time, querying and comparing with the UAV blacklist pre-stored in the corresponding memory address. If it is not a blacklist UAV, it is transmitted to the network communication module; if it is a blacklist UAV, an alarm flag is added to the output data format.

7. The drone monitoring system according to claim 6, characterized in that, The information reporting layer also includes: Regularly reading the RSRP signal strength of the 4G network once. When the RSRP signal strength < the set threshold, reporting the poor signal quality status information in the format of a standardized JSON structure format data, stopping reporting the UAV Remote ID information, and switching to the delayed upload mode. In this mode, the data is cached in the local memory until it is queried that the 4G network returns to normal and then the UAV Remote ID information reporting is restarted.

8. The drone monitoring system according to claim 1, characterized in that, The tracking and capture layer retrieves and reads the UAV information stored in the corresponding memory address by the data management layer and compares it with the positioning information of the ground station itself; at this time, the ground station calculates the distance based on its current position information and the position information of the UAV, and preliminarily determines the spatial position of the UAV; uses the real-time position information and speed information in the UAV information to calculate the movement trajectory of the UAV; each time a new frame of UAV information is received, it records and updates the trajectory points of the target and performs smoothing processing. Through multiple updates, the movement path of the UAV is initially obtained; then the UAV tracking mode is enabled, and the trajectory of the UAV is predicted through the Kalman filter algorithm, and the position of the UAV is updated when new UAV information is received.

9. The drone monitoring system according to claim 8, characterized in that, Predicting the trajectory of the UAV through the Kalman filtering algorithm and updating the position of the UAV when new UAV information is received, including: using Kalman filtering to predict the position of the UAV at the next moment by inputting the current position and speed data of the UAV; based on the historical data of the UAV, by assuming that the UAV continues to move along the current trajectory and adjusting the state estimation of the UAV through the update step, comparing the predicted position with the actual observation value, and correcting the trajectory of the UAV; finally outputting the updated position and speed of the UAV.

10. The drone monitoring system according to claim 8, wherein, The tracking and capturing layer further includes: if multiple ground stations work in the same coverage area, each ground station in the area subscribes to and publishes information under the same topic through the MQTT protocol to share the working status and UAV information with other stations, and the data format is consistent with the UAV information and the station status information, so as to form a distributed tracking network; in the tracking network, each station can obtain the information about the same UAV from other stations in real time, merge the UAV trajectory information from different sources, and predict the trajectory of the UAV through the merged trajectory information to achieve cross-station UAV relay tracking.

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