Distributed unmanned aerial vehicle active defense system

Abstract

The invention belongs to the technical field of radio countermeasure, and particularly relates to a distributed unmanned aerial vehicle active defense system which comprises an intelligent stationing dynamic optimization module, a multi-mode cognitive interference module, a high-precision three-level clock synchronization module and an elastic ad hoc network communication module which are used for supporting the overall function of the system. The distributed hardware equipment and the edge intelligent decision-making terminal are used for implementing a detection countering function at distributed layout positions; during specific operation and use, a distributed unmanned aerial vehicle active defense system can be provided for an important target with a large area and multiple important parts; the application problems that the arrangement position cannot be accurately selected, a large coverage blind area exists, the synchronous time service precision is low, communication networking is unreliable, serious self-interference is generated on electromagnetic and time service equipment of a partner, and the unmanned aerial vehicle countering effect is poor can be solved. And rapid and automatic passive detection and accurate interference of important targets with a large area and multiple important parts on the low-slow-small unmanned aerial vehicle are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of radio countermeasures, and in particular relates to a distributed unmanned aerial vehicle active defense system. Background Art

[0002] The current drone countermeasure systems used in key target areas primarily rely on long-term monitoring and detection using optoelectronics, radar, and radio to detect drones and counter them through navigation decoys, omnidirectional electromagnetic interference, and high-energy lasers. These devices comprise a drone countermeasure system, which is typically deployed in a centralized manner within key target areas. When the target area is far beyond the range of a single device, multiple sets of drone countermeasure equipment are required. While this centralized deployment approach and the more extensive multi-device stacking method have addressed some of the challenges of drone countermeasures, they also present the following issues: 1. Centrally deployed drone countermeasure systems have a single point of failure risk, and fixed-site detection and countermeasure equipment have coverage blind spots. After discovering a "low, slow, and small" drone, countermeasures are generally carried out using navigation decoys or omnidirectional electromagnetic interference. Navigation decoys can affect other timing and positioning systems in important target areas. Omnidirectional electromagnetic interference is generally achieved by human-operated jammers, which have high power and cause serious self-disturbance to the partner's electromagnetic equipment. There are also spatial coverage blind spots and it is easy for the drone's AI obstacle avoidance system to identify and avoid it. 2. Multiple countermeasure devices are mostly synchronized through satellite synchronization methods such as GPS, and a local area network is established through a fixed Ethernet IP. The clock synchronization accuracy is low and it is easily affected by navigation decoy devices. The network reliability is also not high. CN110161533A discloses a drone detection solution based on a phased array, but does not solve the problem of multi-node coordination. At the same time, the multi-point deployment of the drone countermeasure system has no optimized deployment basis, and the deployment based on experience has resulted in a waste of countermeasure effects and economic investment. Summary of the Invention

[0003] The purpose of the present invention is to provide a distributed UAV active defense system, which can provide a distributed UAV active defense system for important targets in a large area and multiple important parts, effectively avoiding application difficulties such as the inability to accurately select the deployment location, the existence of large coverage blind spots, low synchronization timing accuracy, unreliable communication networking, serious self-disturbance to the electromagnetic and timing equipment of the partner, and poor UAV counter-effect, and realizes rapid and automatic passive detection and precise interference of "low, slow and small" UAVs by important targets in a large area and multiple important parts.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A distributed UAV active defense system includes an intelligent deployment dynamic optimization module, a multimodal cognitive jamming module, a high-precision three-level clock synchronization module, and a flexible ad hoc network communication module to support the overall system function; distributed hardware equipment and edge intelligent decision-making terminals for distributed deployment and implementation of detection and countermeasure functions; The intelligent point placement dynamic optimization module combines the value of important target assets V, airspace openness α, historical attack frequency β, and the effectiveness E of the detection and identification system performance to construct a four-dimensional weight. Based on the spatial Durbin model, it calculates the economic-military-functional composite value index ρ at different locations of each important target, and deploys four types of heterogeneous distributed equipment according to the distribution of ρ values. The multimodal cognitive jamming module can achieve real-time fusion processing of radio signals, radar signals and optoelectronic signals. When it identifies a drone target, it provides high-precision drone positioning information, combines historical track data to output the drone's motion state estimation result, and then implements precise jamming. The high-precision three-level clock synchronization module can achieve the unified time base of distributed equipment, laying the foundation for direction finding and positioning and data fusion, and is completed using a three-level local clock synchronization mechanism that does not rely on the satellite navigation system; The elastic self-organizing network communication module is used to realize the networking and data sharing of distributed devices.

[0005] Furthermore, important nodes are deployed in the top 10% areas with ρ values, that is, detection and identification + electromagnetic interference + hard damage functions are deployed; general nodes are deployed in the areas with ρ values ​​of 10%-40%, that is, detection and identification + electromagnetic interference are deployed; slave nodes are deployed in the areas with ρ values ​​of 40%-70%, that is, radio spectrum monitoring + electromagnetic interference are deployed; portable electromagnetic interference equipment is deployed in the areas with the last 30% of ρ values. Through the optimized distributed deployment algorithm, it is possible to reduce the overall equipment cost of large and important target areas while reducing coverage blind spots.

[0006] Furthermore, the frequency domain of the interference electromagnetic signal of the multimodal cognitive interference module adopts the same frequency band as the current UAV, that is, it is achieved by using a broadband coverage signal method for the frequency hopping signal. In the airspace, a directional narrow beam is formed for the movement direction of the UAV through adaptive digital beamforming. In the time domain, it is basically simultaneous with the detection and identification of UAV parameters, and a suitable low transmission power is designed in combination with the UAV distance to complete the electromagnetic interference of the time-frequency-space three-dimensional precise suppression of the UAV target.

[0007] Furthermore, in the high-precision three-level clock synchronization module, the reference layer deploys a rubidium atomic clock with an accuracy of 1e-12 as the main clock source, and distributes IRIG-B code time signals via optical fiber. The relay layer directly shares the clock with nearby devices through the edge intelligent decision-making terminal with a built-in TCXO oscillator with a temperature drift of ±0.1ppm and an improved IEEE 1588v2 protocol. The terminal layer solves the clock synchronization problem of the terminal device. For terminal distributed devices within a distance of 2km and without wired network access conditions, 1PPS signals are sent through wireless LoRa modulation, with a signal rising edge of <2ns, as a correction signal for clock alignment.

[0008] Furthermore, in addition to the Ethernet and fiber optic links of each device, additional communication modules and LORA communication modules that support wireless MESH networks are added to ensure redundant backup of physical connections. When the delay and bandwidth indicators of a physical link are reduced, it can automatically switch to other communication links; different distributed devices in the network use an adaptive TDMA networking method to maintain and update routes, and divide the time axis into a superframe structure. Each superframe contains a 20ms beacon period for broadcasting network topology information, a 30ms contention period for CSMA / CA access, and a 250ms guarantee period. For movable distributed devices, Kalman filtering is used to estimate the position of the next cycle and update the routing table.

[0009] Furthermore, distributed hardware equipment used for distributed deployment to implement detection and countermeasure functions, including radio, optoelectronic and radar detection equipment, navigation deception, electromagnetic interference, high-energy lasers and high-power microwaves, and devices of different types and interfaces can all be distributedly networked and deployed and synchronized with the clock under the support of the hardware protocol middleware of the edge intelligent decision-making terminal. At the same time, the intelligent recognition and decision-making algorithms are all implemented in the processor of the edge intelligent decision-making terminal through embedded firmware programs, which serve as the execution unit of the system decision to drive and control the distributed drone countermeasure equipment.

[0010] The working process of a distributed UAV active defense system is as follows: S1. The intelligent point placement dynamic optimization module first calculates the composite value index ρ of each location using the spatial Durbin model based on the four dimensions of the value V of important target assets in the protection area, airspace openness α, historical attack frequency β, and system performance effectiveness E. Based on the ρ value distribution, the system automatically plans the deployment of four types of nodes: deploying important nodes with detection and identification, electromagnetic interference, and hard damage capabilities in the high-value areas with the top 10% ρ value; deploying general nodes with detection and identification + electromagnetic interference in areas with ρ values ​​of 10%-40%; deploying slave nodes with radio monitoring + electromagnetic interference in areas with ρ values ​​of 40%-70%; and deploying portable jamming equipment in the remaining areas. This optimized deployment strategy ensures coverage while reducing equipment costs. S2. Distributed radio, optoelectronic, and radar detection equipment continuously monitors the protected airspace. When a suspicious target is detected, the multimodal cognitive jamming module integrates multi-source sensor data in real time and determines whether it is a drone target through signal feature analysis and pattern recognition. Once the target is confirmed, the module combines real-time measurement data with historical flight paths to calculate the target's precise distance, azimuth, and pitch angle coordinates, as well as its speed and heading motion, to form a complete drone situational awareness. The S3 high-precision three-level clock synchronization module maintains the system time base: the reference layer transmits the IRIG-B code signal of the rubidium atomic clock via optical fiber; the relay layer uses the improved IEEE 1588v2 protocol to achieve microsecond-level synchronization between devices; the terminal layer uses LoRa wireless transmission of 1PPS signals to achieve nanosecond-level time alignment of terminal devices; the elastic self-organizing network communication module builds a redundant network through wired Ethernet / optical fiber and wireless Mesh / LoRa multi-links, uses an adaptive TDMA mechanism to dynamically allocate communication resources, and uses Kalman filtering to predict the location of mobile nodes and update routing tables in real time to ensure communication continuity when the network topology changes. S4. After confirming the threat target, the system automatically selects the optimal jamming node and generates a spatially directional jamming beam with a beamwidth of approximately 3° based on the drone's operating frequency band, real-time location, and motion trajectory through adaptive digital beamforming technology. The jamming signal is synchronized with the detection cycle in the time domain, matches the target signal in the frequency domain, and accurately points to the target in the airspace. At the same time, the transmit power is dynamically adjusted according to the distance, achieving precise suppression in the three dimensions of time, frequency, and space. The entire process is completed within 200ms, effectively avoiding the collateral effects of traditional omnidirectional jamming. S5. The management software monitors the jamming effect in real time. If the target persists, it automatically upgrades the countermeasure strategy, such as coordinating multi-node joint jamming or activating hard-destruction equipment. The system continuously learns the signal characteristics and avoidance strategies of new drones, updates the jamming parameter library and deployment plan, and forms a closed-loop defense system of "detection-identification-jamming-evaluation-optimization."

[0011] The beneficial effects of the present invention are as follows: the distributed UAV active defense system provided by the present invention can provide a distributed UAV active defense system for important targets in multiple important locations over a large area of ​​more than 50 square kilometers during specific operational use. It can avoid application difficulties such as the inability to accurately select deployment locations, the existence of large coverage blind spots, low synchronization timing accuracy, unreliable communication networking, serious self-disturbance to the electromagnetic and timing equipment of the partner, and poor UAV counter-measures. It achieves rapid and automatic passive detection and precise interference of "low, slow, and small" UAVs for important targets in a large area and multiple important locations. Specifically, when the present invention is applied, the signal blind spot can be reduced by 80%, while the anti-interference accuracy can be improved and the electromagnetic side effects can be reduced by 90%. The system adopts a high-reliability architecture and has extremely strong anti-destruction capabilities. Single-point failures will not affect the overall operation. Its excellent adaptability to complex electromagnetic environments is particularly suitable for combating modern threats such as intelligent UAVs, and can demonstrate comprehensive battlefield communication guarantee capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the structure of the distributed UAV active defense system of the present invention; Figure 2 This is a schematic diagram of the intelligent point distribution dynamic optimization module in the present invention; Figure 3 Schematic diagram of the multimodal cognitive interference module in the present invention; Figure 4 Schematic diagram of the high-precision three-level clock synchronization module in the present invention. DETAILED DESCRIPTION

[0013] Specific embodiment 1: The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that: in the present invention, unless otherwise specified, all the implementation methods and preferred implementation methods mentioned herein can be combined with each other to form a new technical solution. In the present invention, unless otherwise specified, all the technical features and preferred features mentioned herein can be combined with each other to form a new technical solution.

[0014] As the specification of the present invention Figure 1 To the instruction manual Figure 4As shown, in order to solve the application problems of existing drone countermeasure systems, such as the inability to accurately select deployment locations, large coverage blind spots, low synchronization timing accuracy, unreliable communication networking, serious self-interference to the partner's electromagnetic and timing equipment, and poor drone countermeasure effectiveness, the embodiment of the present invention is designed to provide a distributed drone active defense system. The distributed drone active defense system adopts a distributed networking architecture. Its core modules include: distributed hardware devices for distributed deployment to implement detection and countermeasure functions. The hardware devices integrate radio, optoelectronic, radar detection and electromagnetic interference, high-energy laser, high-power microwave and other countermeasures; edge intelligent decision terminals that perform data processing, interference decision-making and equipment control; dynamic optimization modules for intelligent point placement in real time to optimize equipment deployment; multi-modal cognitive interference modules for generating precise interference strategies; high-precision three-level clock synchronization modules for unified system time bases; flexible self-organizing network communication modules to ensure reliable network transmission; and management software for system monitoring, threat analysis and countermeasure execution. Through the collaboration of multiple modules, this architecture builds an intelligent protection system that integrates detection, decision-making, interference and optimization.

[0015] The present invention achieves precise defense deployment through an intelligent point distribution dynamic optimization module: first, based on the composite value index ρ, the spatial Durbin model is used to calculate the protection level of each area and generate a distribution map; then, defense nodes are deployed in layers according to the ρ value range, forming a multi-level protection system of core area-key area-conventional area-auxiliary area; finally, dynamic optimization is achieved by regularly updating the ρ value and temporarily adding mobile devices.

[0016]

[0017] Intelligent countermeasures are achieved through multimodal cognitive jamming module technology: first, target detection and identification are carried out, integrating radio monitoring to capture 2.4GHz / 5.8GHz remote control signals, radar detection to obtain distance / speed, and AI image analysis to confirm the optoelectronic identification of target types and other multi-source data; then, precise jamming strategies are generated, including frequency domain matching, which tracks frequency hopping signals and adopts broadband coverage, spatial focusing, which adaptively forms 3° narrow beam directional jamming, time domain synchronization, which aligns the jamming signal with the detection period, and power regulation, which dynamically adjusts the transmission power; finally, the target status is monitored in real time through jamming effect evaluation. If it has not failed, it is upgraded to enhanced modes such as multi-node coordinated suppression, forming a closed-loop countermeasure process of "detection-jam-evaluation".

[0018] The system utilizes a high-precision, three-tier clock synchronization module architecture, achieving a unified global time base through a layered design. The reference layer utilizes a rubidium atomic clock and fiber-optic IRIG-B to provide ultra-high 1e-12 synchronization accuracy, ensuring timing for core nodes. The relay layer utilizes a TCXO and PTP protocol to achieve ±0.1ppm accuracy, meeting the requirements of general nodes. The terminal layer utilizes LoRa wireless 1PPS technology to achieve low-cost synchronization with <2ns jitter, suitable for edge nodes. The master clock source distributes time signals via optical fiber, while wireless LoRa is used to synchronize end devices within a 2km radius, forming a hybrid "wired + wireless" synchronization network.

[0019] It employs a flexible ad hoc communication module architecture, achieving highly reliable transmission through a hybrid wired and wireless network. The wired side uses Ethernet and fiber optics to provide a high-bandwidth, low-latency backbone channel; the wireless side utilizes mesh networking and LoRa technology to enhance survivability. The communication protocol utilizes a 300ms periodic adaptive TDMA superframe structure, divided into a 20ms beacon period for topology broadcast, a 30ms contention period for CSMA / CA random access, and a 250ms fixed time slot for data transmission. For mobile nodes, dynamic routing optimization is achieved through Kalman filtering to predict location, ensuring the network maintains optimal connectivity even in complex environments. This design combines deterministic transmission with dynamic adaptability to meet the high real-time requirements of mission-critical services.

[0020] Efficient local processing is achieved through edge intelligent decision-making terminals, and hardware protocol middleware is used to be compatible with devices from multiple manufacturers, enabling plug-and-play rapid deployment. The embedded AI algorithm can complete target identification and jamming strategy generation within 200ms, ensuring real-time response. At the same time, it supports remote firmware upgrade capabilities, and continuously improves the response efficiency to new drone threats through dynamic updates of algorithms and strategies, forming an edge computing node with autonomous evolution capabilities.

[0021] The specific working process of the distributed UAV active defense system of the present invention is as follows: S1. The intelligent point placement dynamic optimization module first calculates the composite value index ρ of each location using the spatial Durbin model based on the four dimensions of the value V of important target assets in the protection area, airspace openness α, historical attack frequency β, and system performance effectiveness E. Based on the ρ value distribution, the system automatically plans the deployment of four types of nodes: deploying important nodes with detection and identification, electromagnetic interference, and hard damage capabilities in the high-value areas with the top 10% ρ value; deploying general nodes with detection and identification + electromagnetic interference in areas with ρ values ​​of 10%-40%; deploying slave nodes with radio monitoring + electromagnetic interference in areas with ρ values ​​of 40%-70%; and deploying portable jamming equipment in the remaining areas. This optimized deployment strategy reduces equipment costs while ensuring coverage. S2. Distributed radio, optoelectronic, and radar detection equipment continuously monitors the protected airspace. When a suspicious target is detected, the multimodal cognitive jamming module integrates multi-source sensor data in real time and determines whether it is a drone target through signal feature analysis and pattern recognition. Once the target is confirmed, the module combines real-time measurement data with historical flight paths to calculate the target's precise three-dimensional coordinates of distance, azimuth, and pitch angle, as well as its speed and heading motion, forming a complete drone situational awareness. The S3 high-precision three-level clock synchronization module maintains the system time base: the reference layer transmits the IRIG-B code signal of the rubidium atomic clock via optical fiber; the relay layer uses the improved IEEE 1588v2 protocol to achieve microsecond-level synchronization between devices; the terminal layer uses LoRa wireless transmission of 1PPS signals to achieve nanosecond-level time alignment of terminal devices; the elastic self-organizing network communication module builds a redundant network through wired Ethernet / optical fiber and wireless Mesh / LoRa multi-links, uses an adaptive TDMA mechanism to dynamically allocate communication resources, and uses Kalman filtering to predict the location of mobile nodes and update routing tables in real time to ensure communication continuity when the network topology changes. S4. After confirming the threat target, the system automatically selects the optimal jamming node and generates a spatially directional jamming beam with a beamwidth of approximately 3° based on the drone's operating frequency band, real-time location, and motion trajectory through adaptive digital beamforming technology. The jamming signal is synchronized with the detection cycle in the time domain, matches the target signal in the frequency domain, and accurately points to the target in the airspace. At the same time, the transmit power is dynamically adjusted according to the distance, achieving precise suppression in the three dimensions of time, frequency, and space. The entire process is completed within 200ms, effectively avoiding the collateral effects of traditional omnidirectional jamming. S5. The management software monitors the jamming effect in real time. If the target persists, it automatically upgrades the countermeasure strategy, such as coordinating multi-node joint jamming or activating hard-destruction equipment. The system continuously learns the signal characteristics and avoidance strategies of new drones, updates the jamming parameter library and deployment plan, and forms a closed-loop defense system of "detection-identification-jamming-evaluation-optimization."

[0022] The distributed UAV active defense system of the present invention has the following features when in use: First, the system uses the spatial Durbin model to construct a four-dimensional evaluation system: asset value V, airspace openness α, historical attack frequency β, and system effectiveness E. The composite value index ρ is used to dynamically optimize the allocation of defense resources, forming a four-level defense circle of "core-key-conventional-auxiliary". Compared with traditional uniform deployment solutions, it can reduce equipment investment by 30% while also reducing coverage blind spots. The innovative integration of radio spectrum monitoring, radar detection, and optoelectronic recognition technologies improves the detection probability of "low, slow, and small" targets through multi-source data fusion, with a measured probability of 98. 7%, and adopts a time-frequency-space three-dimensional joint analysis algorithm to increase the accuracy of drone model identification to more than 95%; breaking through the traditional omnidirectional interference mode, adopting an adaptive digital beam with a beam width of 3° and dynamic power adjustment technology, the interference energy concentration is increased by 20dB, and the incidental electromagnetic interference is reduced by 90% while ensuring the suppression effect, effectively solving the problem of timing system pollution caused by navigation deception; using an original three-level clock synchronization architecture to build a high-reliability synchronization system: the reference layer provides an ultra-high-precision time reference through a 1e-12 precision rubidium atomic clock and an optical fiber IRIG-B code; the relay layer uses a ±0.1ppm TCXO oscillator combined with an improved PTP protocol to achieve precise relay synchronization; the terminal layer uses LoRa wireless 1PPS technology to complete terminal device synchronization. The above architecture has achieved a breakthrough in achieving 100ns-level time synchronization accuracy for the entire system, completely getting rid of dependence on external signals such as GPS, and providing autonomous and controllable time base protection for collaborative operations in complex electromagnetic environments; building a "wired Ethernet / fiber + wireless Mesh / LoRa" multi-physical layer redundant architecture, using a 300ms period adaptive TDMA superframe structure and Kalman filter predictive routing algorithm, making the network reconstruction time less than 50ms and the communication availability reach 99.999%, meeting the needs of strong confrontation environment in wartime; achieving efficient and autonomous response through edge intelligent decision-making terminals, integrating 12 protocol intermediate The system enables plug-and-play integration with multi-vendor devices, leverages embedded computing power to achieve localized rapid decision-making within 200ms, and is equipped with a jamming strategy library with online learning capabilities, which dynamically updates countermeasures, significantly improving real-time adaptability and countermeasure effectiveness against emerging threats such as intelligent drones. This system achieves comprehensive performance optimization through a closed-loop "detection-identification-jam-assessment" control system, achieving full coverage of a 50km² area and possessing strong survivability with automatic reconstruction from single-point failures. While ensuring protection effectiveness, it significantly reduces countermeasure energy consumption by 40% and operation and maintenance costs by 35%, forming an efficient, reliable, and energy-saving integrated defense system.

[0023] In general, the management software of the distributed UAV active defense system of the present invention integrates four core functions: real-time monitoring of the node status of the entire network and fault warning through device management; situational awareness based on multi-source data fusion can dynamically display the UAV track and assess the threat level; the counter-execution module automatically matches the optimal interference strategy and triggers it quickly; at the same time, it provides effect evaluation support, including statistical analysis of the interference success rate and system parameter optimization suggestions, forming a closed-loop management from monitoring to counter-measures, and comprehensively improving the intelligent operation and maintenance level of the protection system. It should be noted that in the present invention, for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. In addition, it should be understood that although this specification is described in accordance with the implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A distributed UAV active defense system, characterized in that: It includes an intelligent deployment dynamic optimization module, a multimodal cognitive interference module, a high-precision three-level clock synchronization module, and a flexible ad hoc network communication module for supporting the overall system functions; distributed hardware equipment and edge intelligent decision-making terminals for implementing detection and countermeasure functions in distributed deployment locations; The intelligent point placement dynamic optimization module combines the value of important target assets V, airspace openness α, historical attack frequency β, and the effectiveness E of the detection and identification system performance to construct a four-dimensional weight. Based on the spatial Durbin model, it calculates the economic-military-functional composite value index ρ at different locations of each important target, and deploys four types of heterogeneous distributed equipment according to the distribution of ρ values. The multimodal cognitive jamming module can achieve real-time fusion processing of radio signals, radar signals and optoelectronic signals. When it identifies a drone target, it provides high-precision drone positioning information, combines historical track data to output the drone's motion state estimation result, and then implements precise jamming. The high-precision three-level clock synchronization module can unify the time base of distributed equipment, laying the foundation for direction finding and data fusion. It adopts a three-layer local clock synchronization mechanism that does not rely on the satellite navigation system. The specific architecture is as follows: the reference layer provides an ultra-high-precision time base through rubidium atomic clocks and optical fiber IRIG-B codes; the relay layer uses TCXO oscillators combined with an improved PTP protocol to achieve precise relay synchronization; the terminal layer uses LoRa wireless 1PPS technology to complete terminal device synchronization. This architecture achieves a breakthrough 100ns time synchronization accuracy for the entire system, completely eliminating the dependence on external GPS signals, and providing an autonomous and controllable time base guarantee for collaborative operations in complex electromagnetic environments. The elastic self-organizing network communication module is used to realize the networking and data sharing of distributed devices.

2. A distributed UAV active defense system according to claim 1, characterized in that: Important nodes are deployed in the first 10% of the ρ value area, general nodes are deployed in the 10%-40% area, slave nodes are deployed in the 40%-70% area, and portable electromagnetic interference equipment is deployed in the last 30% area. Through the above-mentioned optimized distributed deployment algorithm, it is possible to reduce the overall equipment cost of large and important target areas while reducing coverage blind spots.

3. A distributed UAV active defense system according to claim 2, characterized in that: The frequency domain of the interference electromagnetic signal of the multimodal cognitive interference module adopts the same frequency band as the current drone, and the airspace forms a directional narrow beam for the drone's movement direction through adaptive digital beamforming. In the time domain, while detecting and identifying drone parameters, the appropriate low transmission power is designed in combination with the drone distance to complete the electromagnetic interference of the time-frequency-space three-dimensional precise suppression of the drone target.

4. A distributed UAV active defense system according to claim 3, characterized in that: In the high-precision three-level clock synchronization module, the reference layer deploys a rubidium atomic clock with an accuracy of 1e-12 as the master clock source, distributing IRIG-B code time signals via optical fiber. The relay layer directly shares clocks with nearby devices through the built-in TCXO oscillator and improved IEEE 1588v2 protocol of the edge intelligent decision terminal. The terminal layer solves the clock synchronization problem of the terminal devices. For terminal distributed devices within a distance of 2km and without wired network access conditions, 1PPS signals are sent through wireless LoRa modulation as correction signals for clock alignment.

5. A distributed UAV active defense system according to claim 4, characterized in that: In addition to the Ethernet and fiber optic links of each device, additional communication modules and LORA communication modules that support wireless MESH networks are added to ensure redundant backup of physical connections. When the delay and bandwidth indicators of a physical link are reduced, it can automatically switch to other communication links. Different distributed devices in the network use adaptive TDMA networking methods to maintain and update routes, and divide the time axis into a superframe structure. Each superframe includes a beacon period, a contention period, and a guarantee period. Kalman filtering is used for mobile distributed devices to estimate the position of the next cycle and update the routing table.

6. A distributed UAV active defense system according to claim 1, characterized in that: Distributed hardware equipment used for distributed deployment to implement detection and countermeasure functions includes radio, optoelectronic and radar detection equipment, navigation deception, electromagnetic interference, high-energy lasers and high-power microwaves. Equipment of different types and different interfaces are all supported by the hardware protocol middleware of the edge intelligent decision-making terminal to achieve distributed networking and clock synchronization. At the same time, the intelligent recognition and decision-making algorithms are all implemented in the processor of the edge intelligent decision-making terminal through embedded firmware programs, which serve as the execution unit of the system decision to drive and control the distributed drone countermeasure equipment.

7. The distributed UAV active defense system according to any one of claims 1 to 6, characterized in that: The system works as follows: S1. The intelligent point placement dynamic optimization module first calculates the composite value index ρ of each location using the spatial Durbin model based on the four dimensions of the value V of important target assets in the protection area, airspace openness α, historical attack frequency β, and system performance effectiveness E. Based on the ρ value distribution, the system automatically plans the deployment of four types of nodes: deploying important nodes with detection and identification, electromagnetic interference, and hard damage capabilities in the high-value areas with the top 10% ρ value; deploying general nodes with detection and identification + electromagnetic interference in areas with ρ values ​​of 10%-40%; deploying slave nodes with radio monitoring + electromagnetic interference in areas with ρ values ​​of 40%-70%; and deploying portable jamming equipment in the remaining areas. The above optimized deployment strategy reduces equipment costs while ensuring coverage; S2. Distributed radio, optoelectronic, and radar detection equipment continuously monitors the protected airspace. When a suspicious target is detected, the multimodal cognitive jamming module integrates multi-source sensor data in real time and determines whether it is a drone target through signal feature analysis and pattern recognition. Once the target is confirmed, the multimodal cognitive jamming module combines real-time measurement data and historical flight paths to calculate the target's precise three-dimensional coordinates and motion state, forming a complete drone situational awareness. S3, a high-precision three-level clock synchronization module maintains the system time base: the reference layer transmits the IRIG-B code signal of the rubidium atomic clock via optical fiber; the relay layer uses the improved IEEE 1588v2 protocol to achieve microsecond-level synchronization between devices; The terminal layer uses LoRa wireless transmission of 1PPS signals to achieve nanosecond-level time alignment of terminal devices. The elastic ad hoc network communication module builds a redundant network through wired and wireless multi-links, uses an adaptive TDMA mechanism to dynamically allocate communication resources, and uses Kalman filtering to predict the location of mobile nodes and update the routing table in real time to ensure communication continuity when the network topology changes. S4. After confirming the threat target, the system automatically selects the optimal jamming node and generates a spatially directional jamming beam based on the drone's operating frequency band, real-time location, and motion trajectory through adaptive digital beamforming technology. The jamming signal is synchronized with the detection cycle in the time domain, matches the target signal in the frequency domain, and accurately points to the target in the airspace. At the same time, the transmission power is dynamically adjusted according to the distance, achieving precise suppression in the three dimensions of time, frequency, and space. The entire process is completed within 200ms, effectively avoiding the side effects of traditional omnidirectional jamming. S5. The management software monitors the jamming effect in real time. If the target persists, the countermeasure strategy is automatically upgraded. The system continuously learns the signal characteristics and avoidance strategies of new drones, updates the jamming parameter library and deployment plan, and forms a closed-loop defense system of "detection-identification-jamming-evaluation-optimization".

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