A small suicide drone defense system

Abstract

A defense system for small suicide drones is disclosed. The small suicide drone defense system according to an embodiment of the present invention comprises: a defense target detection unit that detects enemy long-range artillery, self-propelled artillery, coastal artillery, naval guns, low-altitude infiltration aircraft, ballistic missiles, and missiles, and transmits real-time detection data to an active response central control unit; a swarm drone carrying platform that carries a plurality of small suicide drones and operates by a control signal transmitted from the active response central control unit to scatter the swarm drones at a preset scattering location; and an active response central control unit that controls the operation of the swarm drone carrying platform by utilizing an AI-based path prediction method and a multi-interception algorithm based on data acquired in real-time from the defense target detection unit.

Description

Technology Field

[0001] The present invention relates to a defense system for small suicide drones, and more specifically, to a system capable of effectively countering enemy long-range artillery, self-propelled artillery, towed artillery, fixed artillery, multiple rocket launchers, coastal artillery, naval guns, low-altitude penetrating aircraft, and ballistic missiles by utilizing the swarm flight of small suicide drones. Background Technology

[0002] Most air defense weapon systems currently in operation consist of missile interception systems and anti-aircraft gun systems. These defense systems are highly effective against large targets that are relatively easy to detect, such as large aircraft or high-speed missiles. However, due to the recent proliferation of asymmetric warfare capabilities, miniaturized attack means—such as drones, long-range artillery, and low-speed, low-altitude aircraft—are rapidly increasing, and there is a problem in that the ability to respond to these targets is significantly lacking.

[0003] Although Israel's Iron Dome, the representative missile interception system currently in existence, possesses a certain level of performance, it has structural limitations that result in massive costs when responding to mass attacks, such as those involving inexpensive rockets. While the unit cost of the Qassam rockets used by the Palestinian militant group Hamas is approximately 800,000 won, the interceptor missiles fired from the Iron Dome cost about 80 million won each. This cost imbalance reveals an inherent limitation: it is difficult to economically maintain a system like the Iron Dome in situations of prolonged conflict rather than short-term clashes.

[0004] Considering South Korea's security situation, this issue becomes even more serious. North Korea is capable of firing over 16,000 rounds of long-range artillery within the first hour of a conflict, which could directly threaten the capital region and major military bases. Existing air defense missile or gun systems cannot block all of such massive shelling. In particular, because artillery shells have short trajectories and relatively low speeds after launch, there are clear technical limitations to missile interception systems detecting and intercepting them.

[0005] Low-altitude penetrating aircraft are also exploiting vulnerabilities in existing defense systems. Small, low-speed drones, cruise missiles, and glider-type unmanned aerial vehicles utilize terrain features to evade radar detection or delay detection as much as possible. These aircraft are difficult to intercept even with air defense artillery systems, and responding with missile interception systems leads to serious issues regarding cost and ammunition supply.

[0006] Existing ballistic missile defense systems are based on a multi-layered defense system. However, ballistic missiles travel at very high speeds, and the probability of interception decreases sharply when launched in large numbers simultaneously. Furthermore, since North Korea's Scud and Nodong series ballistic missiles are operated from mobile launchers, they are difficult to detect in advance. Consequently, it is virtually impossible to effectively respond to a mass ballistic missile attack using only existing interceptor systems.

[0007] Missile detection and interception are also not free from cost issues. The radars and interceptor missiles used to detect and intercept guided missiles have very high manufacturing costs, and a single launch incurs enormous expenses. If the enemy operates a large number of inexpensive missiles or drones, existing high-cost defense systems are structurally very difficult to operate continuously.

[0008] Furthermore, existing defense systems are often installed in fixed locations, making it difficult to respond immediately to mobile enemy attacks or rapid battlefield changes. While such fixed defense systems are effective for protecting specific areas, they have limitations in defending large areas, moving troops, or key facilities.

[0009] Attacks by swarms of small drones are also a blind spot for existing defense systems. Drones have a small radar cross-section (RCS), and existing radar systems find it difficult to clearly identify multiple drones approaching simultaneously. Even if detection is successful, intercepting a large number of drones at once presents challenges regarding cost and quantity that missile systems cannot handle.

[0010] Currently operational defense systems separate the stages of detection, tracking, and interception, and lack AI-based automated threat analysis and multi-target interception capabilities. Furthermore, existing interceptor systems operate effectively only when threat objects are above a certain altitude, and their response capability deteriorates sharply against low-altitude and terrain-evading flights. These structural limitations act as a fundamental problem preventing effective responses to the diversified attack patterns of modern warfare.

[0011] Ultimately, air defense systems based on conventional technology exhibit economic, technical, and strategic limitations against large quantities of low-cost ammunition, low-altitude aircraft, small drone swarms, and mobile ballistic missiles, and this invention aims to fundamentally resolve these problems of conventional technology. Prior art literature

[0012] Korean Patent Publication No. 10-1924863 (Registration Date: November 28, 2018) The problem to be solved

[0013] The present invention aims to overcome the structural limitations of conventional air defense weapon systems. In particular, existing missile and air defense gun systems face the problem of being unable to effectively respond to large numbers of incoming long-range artillery, small drones, low-altitude infiltration aircraft, and ballistic missiles, and incur excessive response costs. Accordingly, the present invention aims to provide a defense system capable of efficiently and economically intercepting and neutralizing multiple threat objects through real-time detection and AI-based path prediction by utilizing the swarm flight of small suicide drones. means of solving the problem

[0014] A small self-destructing drone defense system according to one aspect of the present invention for achieving such objectives may comprise: a defense target detection unit that detects enemy long-range artillery, low-altitude infiltration aircraft, ballistic missiles, and missiles, and transmits real-time detection data to an active response central control unit; a swarm drone carrying platform that carries a plurality of small self-destructing drones and operates by means of a control signal transmitted from the active response central control unit to scatter the swarm drones at a preset scattering location; and an active response central control unit that controls the operation of the swarm drone carrying platform by utilizing an AI-based path prediction method and a multi-interception algorithm based on data acquired in real-time from the defense target detection unit.

[0015] In one embodiment of the present invention, the defense target detection unit comprises: a long-range artillery detection module that detects the firing of an enemy long-range artillery shell, analyzes its trajectory to predict the landing point, and detects the physical characteristics of the shelling from multiple angles by simultaneously utilizing a low-frequency ballistic detection radar and an ultra-precision acoustic sensor array; a low-altitude aircraft detection module that detects low-altitude penetrating aircraft including drones, low-speed cruise missiles, and gliders, and utilizes a high-performance low-frequency radar and AI-based image and acoustic analysis technology; and a ballistic missile detection module that tracks and predicts the entire flight segment of a ballistic missile in real time from immediately after launch to the ascent, peak, and descent phases, and identifies the ballistic missile body, multiple independently targetable reentry vehicle (MIRV) separator, and decoy by simultaneously operating a high-frequency band radar and a low-frequency band radar. The configuration may include a missile detection module equipped with a complex detection system that detects and tracks missiles including cruise missiles, guided missiles, air-to-ground missiles, and stealth missiles, and integrates a low-frequency band radar, a high-frequency band radar, an infrared (IR) sensor, an electro-optical (EO) sensor, and an RF signal detection device.

[0016] In one embodiment of the present invention, the swarm drone carrying platform may be configured to include: a swarm drone carrying module having a structure that stores a plurality of suicide drones, is launched or transported to a pre-set scattering position by a ground platform, an aerial platform, and a sea platform, and releases the stored plurality of suicide drones at the pre-set scattering position; a ground platform that launches or scatters the swarm drone carrying module as a multi-launcher; an aerial platform that loads the swarm drone carrying module onto a helicopter, a fighter jet, or a transport aircraft to launch or scatter it; and a sea platform that loads the swarm drone carrying module onto a ship or vessel to launch or scatter it.

[0017] In one embodiment of the present invention, the self-destructing drone housed in the swarm drone mounting module may be structured to hover or fly at a predetermined scattering position, fly in a swarm or hover according to a predetermined net arrangement pattern together with a plurality of self-destructing drones, and may be structured to self-destruct when approaching a defense target within a predetermined range, emit jamming electromagnetic waves toward the defense target, or perform collision interception with the defense target based on the predicted path of the defense target.

[0018] In one embodiment of the present invention, the active response central control unit may comprise: a defense target location determination module that calculates the current location of a detected threat object, time-synchronizes asynchronous data collected from multiple sensors, and utilizes an advanced location estimation algorithm to prevent spatial errors; a defense target path prediction module that predicts the future movement trajectory of a threat object in real time based on current location data input from the location determination module and information such as speed, direction, and altitude; a multi-interception algorithm module that establishes and executes a simultaneous interception strategy for multiple threat targets; an active response control module that comprehensively controls the operation of all components that actually perform defense operations in real time, automates the entire process from detection to analysis, path prediction, interception, and recovery, and actively changes the strategy in accordance with changes in the situation; and a swarm drone control module that controls the entire drone swarm to operate like a single organism by networking it, and monitors and manages the drone's location, speed, status, remaining energy, and sensor data in real time based on a bidirectional data link between the central control unit and the drone. Effects of the invention

[0019] Based on the swarm flight of small self-destructing drones and an active central control system, this invention effectively overcomes the cost issues and limitations of responding to mass attacks inherent in conventional defense systems. It enables rapid response to complex threats, such as long-range artillery, low-altitude aircraft, ballistic missiles, and missiles, through real-time detection and AI path prediction, and allows for the simultaneous interception of multiple targets by deploying multiple drones. Furthermore, it can be freely deployed on land, sea, and air platforms, enabling flexible operation in various battlefield environments. The drone swarm allows for complex defense capabilities that disrupt enemy sensors and communications through electronic warfare functions, in addition to physical collision interception. Above all, it enables mass defense at a lower cost compared to conventional missiles, resulting in significant defense spending savings. It also possesses high scalability and economic efficiency, making it applicable for protecting key facilities and responding to civilian disasters. Brief explanation of the drawing

[0020] FIG. 1 is a block diagram showing a small self-destructing drone defense system according to one embodiment of the present invention. FIG. 2 is a schematic diagram showing a ground platform and an aerial platform of a small suicide drone defense system according to one embodiment of the present invention. FIG. 3 is a schematic diagram showing the terminal phase interception of a low-altitude penetrating aircraft and an enemy ballistic missile by a small suicide drone defense system according to one embodiment of the present invention. FIG. 4 is a schematic diagram illustrating the concept of ballistic missile and long-range artillery detection in a small self-destructing drone defense system according to one embodiment of the present invention. FIG. 5 is a conceptual diagram of the integrated operation of a small self-destructing drone defense system according to one embodiment of the present invention. Specific details for implementing the invention

[0021] Preferred embodiments of the present invention will be described in detail below with reference to the drawings. Prior to this, terms and words used in this specification and claims should not be interpreted as being limited to their ordinary or dictionary meanings, but should be interpreted in a meaning and concept consistent with the technical spirit of the present invention.

[0022] Throughout this specification, when it is stated that one component is located "on" another component, this includes not only cases where one component is in contact with another component, but also cases where another component exists between the two components. Throughout this specification, when it is stated that a part "includes" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0023] FIG. 1 shows a block diagram illustrating a small suicide drone defense system according to one embodiment of the present invention, and FIG. 2 shows a schematic diagram illustrating a ground-type platform and an air-type platform of a small suicide drone defense system according to one embodiment of the present invention.

[0024] Referring to these drawings, according to the small self-destructing drone defense system (100) of the present embodiment, by providing a defense target detection unit (110) that performs a specific role, a swarm drone carrying platform (120), and an active response central control unit (130), a system can be provided that can effectively respond to enemy long-range artillery, low-altitude infiltration aircraft, and ballistic missiles by utilizing the swarm flight of small self-destructing drones. Furthermore, it can be implemented at a lower operating cost compared to defense systems according to the prior art, thereby having the effect of significantly reducing defense costs. It can be operated through various platforms such as loading vehicles, ships, fighter jets, and helicopters, allowing for effective operation by adapting to various situations. It can respond realistically and cost-effectively to a large number of inexpensive weapon systems possessed by the enemy, and furthermore, it can respond to civilian disasters and be utilized for monitoring and defense of key facilities.

[0025] Hereinafter, each component constituting the small self-destructing drone defense system (100) according to the present embodiment will be described in detail with reference to FIGS. 1 to 5.

[0026] Specific configuration and role of the defense target detection unit (110)

[0027] The defense target detection unit (110) is a core module responsible for overall initial detection and information collection of the small self-destructing drone defense system according to the present invention. The defense target detection unit (110) includes a plurality of sub-detection modules to perform multi-layered detection functions, and each module has the function of precisely detecting a specific type of threat element. The detection unit is designed with a multi-sensor structure in a radial array and is equipped with a composite detection system capable of capturing artillery, low-altitude flying vehicles, ballistic missiles, missiles, etc., in real time. This structure enables all-around detection and simultaneous detection of multiple targets.

[0028] The defense target detection unit (110) is not merely a collection of physical sensors, but includes an artificial intelligence (AI)-based data analysis and judgment function. Various physical signals collected from the sensors are immediately preprocessed by an AI processor within the detection unit. This AI processor includes a deep learning-based image recognition model, an acoustic pattern recognition model, and a time series prediction model. This reduces the possibility of false positives from a single sensor and dramatically improves detection accuracy through complex signal analysis.

[0029] The detection unit is designed with a structure capable of responding highly sensitively to environmental changes. While conventional radar systems may experience performance degradation due to weather conditions, terrain, and radio wave reflection, this detection unit overcomes these limitations through multi-sensor fusion technology. For example, electro-optical (EO) and infrared (IR) sensors are primarily utilized for detecting low-altitude aircraft, maintaining a high detection rate even at night or in foggy or rainy conditions. Furthermore, the high-power low-frequency radar is highly effective for detecting stealth targets.

[0030] The detection unit's AI operates based on reinforcement and unsupervised learning. In other words, it continuously learns data from the real-time changing battlefield environment, independently recognizing threat types, firing patterns, and flight paths that repeatedly appear in specific areas, and predicting future attack patterns. For example, if repeated shelling occurs from the same battery at a specific time, the detection unit independently detects this and automatically increases the surveillance sensitivity of that area.

[0031] Detected threat information is immediately transmitted to the active response central control unit (130). This data includes not only current location information but also the future trajectory, speed change, and estimated arrival time predicted by AI. Additionally, the detection reliability index and threat priority are also transmitted, and the central control unit determines the interception priority and drone deployment strategy in real time based on this. In this way, the defense target detection unit (110) performs a function of providing tactical information beyond simple detection in the defense system of the present invention.

[0032] Detailed description of the standard range gun detection module (111)

[0033] The long-range artillery detection module (111) is responsible for the function of rapidly detecting the firing of long-range artillery, self-propelled artillery, coastal artillery, and naval artillery shells, and accurately analyzing their trajectories to predict the landing point. Since the shell flies in a parabolic shape immediately after firing, early detection is very important. This module utilizes a low-frequency ballistic detection radar and an ultra-precision acoustic sensor array simultaneously to detect the physical characteristics of the shelling from various angles.

[0034] In particular, pressure waves and low-frequency acoustic signals generated during shell firing are the primary detection targets of this module. An ultra-precision microphone array analyzes acoustic fingerprints in real time to rapidly calculate the shell's launch time, launch location, and initial velocity. This data is converted into a ballistic trajectory through a composite trajectory tracking algorithm that combines Kalman filters and Particle Filters. During this process, the AI ​​utilizes a Spectrogram-based CNN model to remove noise and filter out only valid acoustic signals.

[0035] The standard-range gun detection module (111) also has very powerful radio wave-based detection capabilities. The low-frequency radar detects minute radio wave reflection signals generated as a metallic projectile flies through the air, and uses Doppler Shift to measure the speed and direction of the projectile in real time. The radar signal processor is equipped with an AI-based Signal Denoising Autoencoder, so it can detect precisely without signal distortion even in complex radio wave environments.

[0036] This module also features time-series forecasting capabilities. The Long Short-Term Memory (LSTM) network receives data on the shell's current location, velocity, and acceleration, and calculates the estimated arrival time and landing point with high precision. Additionally, a reinforcement learning-based prediction model is added to learn the shell's firing patterns or periodic firing behaviors, and evaluates the threat intensity and behavioral patterns of specific artillery batteries in real time.

[0037] Detected data is immediately transmitted to the central control unit, and the drone swarm automatically deploys near the predicted impact point. This enables the drones to effectively intercept artillery shells through physical collision interception, proximity self-destruction, or electronic warfare jamming. While existing missile-based interception systems struggle to counter artillery shells, this module overcomes the limitations of conventional defense systems by enabling real-time detection and drone swarm response.

[0038] Detailed description of the low-altitude flight object detection module (112)

[0039] The low-altitude aircraft detection module (112) performs the role of effectively detecting low-altitude penetrating aircraft such as drones, low-speed cruise missiles, and gliders. These low-altitude aircraft have the characteristic of passing through radar blind spots or flying outside the radar detection range using mountainous, urban, or coastal terrain, so high-performance low-frequency radar and AI-based image and acoustic analysis technology are combined.

[0040] This module consists of a low-frequency search radar, an electro-optical (EO) sensor, an infrared (IR) sensor, and an ultra-precision acoustic sensor. In particular, drones have unique frequency patterns caused by propeller rotation. AI learns these acoustic patterns to detect the approach of drones from a distance of several kilometers, and can even detect drones using stealth paint or radar-absorbing materials.

[0041] Image-based detection utilizes the latest models of the Transformer family (ViT, Swin Transformer, etc.). EO and IR imaging capture the thermal signals and shapes of low-altitude aircraft, while AI precisely separates the aircraft from the surrounding background. For example, even a drone moving rapidly through trees can be effectively tracked through the analysis of thermal signals and reflectance.

[0042] The AI ​​utilizes Bayesian Networks and the Markov Decision Process (MDP) to determine the threat level of currently detected aircraft, identifying whether they are for simple reconnaissance, carrying explosives, or intended for electronic warfare. This determination is directly reflected in the drone swarm deployment strategy. If an aircraft poses a high threat, more drones are deployed to perform multi-interception and electronic warfare countermeasures.

[0043] The future movement path of a detected low-altitude aircraft is predicted through LSTM-based time series analysis. In particular, even if the aircraft performs detection evasion maneuvers or suddenly changes its speed or altitude, a reinforcement learning-based prediction engine learns this in real time and readjusts the drone's interception position. As such, the low-altitude aircraft detection module (112) performs intelligent detection functions that include situation awareness, threat assessment, and path prediction, going beyond simple detection.

[0044] Detailed description of the ballistic missile detection module (113)

[0045] The ballistic missile detection module (113) is responsible for tracking and predicting the entire flight path of a ballistic missile in real time, from the moment of launch to the ascent, peak, and descent phases. Ballistic missiles typically fly at speeds of Mach 5 or higher and have the characteristic of exiting and re-entering the atmosphere, so their altitude and speed are very high. Considering these characteristics, this module is configured by combining an ultra-long range phased array radar and a deep learning-based image and data analysis system. In particular, by operating a high-frequency band radar and a low-frequency band radar simultaneously, it effectively identifies the ballistic missile body, multiple independently targetable reentry vehicles (MIRV) separators, and decoys.

[0046] The ballistic missile detection module (113) includes a flash detection function based on infrared thermal signals. The rocket flame generated during the launch of a ballistic missile generates a strong thermal signal even as it penetrates the atmosphere, and the Deep Infrared Sensor Array captures this signal in real time. The detected thermal signal is processed by the AI-based image recognition models UNet and Mask R-CNN to recognize the launch of a ballistic missile with high reliability even in a multi-noise environment. This initial detection information is immediately input into a trajectory calculation algorithm.

[0047] Ballistic trajectory calculation simultaneously applies probability-based and physics-law-based models. The Extended Kalman Filter, Multiple Hypothesis Tracking (MHT), and Particle Filter dynamically calculate ascent and descent trajectories based on currently detected velocity, altitude, and launch angle data. Monte Carlo simulation is applied to calculate the landing point, impact point, and predicted collision probability for multiple scenarios. In particular, when MIRV warheads separate in mid-air, a built-in function is included to track the individual trajectory of each warhead in real-time.

[0048] Based on reinforcement learning and unsupervised learning, the AI ​​continuously learns detection patterns, ballistic missile evasion maneuvers, and decoy deployment patterns. It automatically recognizes characteristics by specific country or projectile type based on past launch data; for instance, it identifies differences in ascent speed, altitude, and rotation patterns between North Korean Scud and Nodong series ballistic missiles in real time. Based on this, the AI ​​operates a binary classification model to distinguish between actual warheads and decoys, and determines interception priorities.

[0049] The detected information is transmitted in real time to the active response central control unit (130), and the swarm of drones is automatically deployed near the expected landing point. The drones can respond not only by simply intercepting the collision, but also by emitting proximity jamming electromagnetic waves against the ballistic missile warhead or by disrupting the heat flux upon the warhead's re-entry. This complex response strategy is much lower cost than conventional missile defense systems and can effectively respond even in situations where multiple warheads and decoys are deployed simultaneously.

[0050] Detailed description of the missile detection module (114)

[0051] The missile detection module (114) is responsible for detecting and tracking various types of missiles, such as cruise missiles, guided missiles, air-to-ground missiles, and stealth missiles. In particular, cruise missiles and low-altitude stealth missiles fly along the terrain and have a very small radar cross-section (RCS), making them difficult to detect with conventional fixed radars. This module integrates a low-frequency band radar, a high-frequency band radar, an infrared (IR) sensor, an electro-optical (EO) sensor, and an RF signal detection device to form a complex detection system.

[0052] The core of detection lies in AI-based signal and image analysis. Upon missile detection, Doppler shift, Radar Cross Section (RCS) change patterns, and velocity-altitude profiles are analyzed in real time. CNN-based signal pattern recognition corrects even minute signal distortions of stealth missiles. In particular, the latest AI models, such as the Vision Transformer (ViT) and Swin Transformer, effectively separate missiles from complex backgrounds in EO and IR image data. Compared to conventional CNNs, they are robust against long-range dependency, maintaining high detection performance even in low brightness or incomplete thermal signals.

[0053] In particular, this module supports Federated Learning. Data models are shared in real-time between each detection module, rather than through a central server. For example, data detected by maritime radar and EO data from aerial platforms are integrated in real-time to analyze the same target from multiple angles. This significantly reduces false positives and misses from a single sensor, while maximizing the accuracy and reliability of threat detection.

[0054] Model-based RL based on reinforcement learning is applied to missile trajectory prediction. It learns data on missile flight patterns, evasive maneuvers, altitude changes, and velocity changes in real time to derive the optimal interception point and timing. In particular, based on large-scale simulation data, the AI ​​predicts even when a missile attempts to evade detection by utilizing specific terrain and preemptively designates the drone interception location.

[0055] Detected missile information is immediately transmitted to the active response central control unit (130). The central control unit establishes a deployment strategy for the swarm of drones based on the missile's expected flight path, possible evasion paths, and speed fluctuations. The drones form a defense network along the missile's expected path and, if necessary, use electronic warfare equipment to disrupt the missile's guidance system or perform close-range collision interception. This response method is much more economical and flexible than conventional high-cost missile interception systems and enables simultaneous response to multiple missiles.

[0056] Detailed description of the swarm drone-mounted platform (120)

[0057] The swarm drone carrying platform (120) plays a key role in the small self-destructing drone defense system of the present invention by rapidly transporting multiple self-destructing drones and deploying them in a swarm flight state by immediately scattering or launching them when necessary. This platform is designed not as a single form, but as a composite structure of land, sea, and air types to adapt to various battlefield environments. Each platform is operated in a multi-launch form by combining a swarm drone carrying module (121).

[0058] The payload platform adopts a highly modular design, allowing the number of drones to be flexibly adjusted according to combat situations. For example, dozens of drone payload modules can be loaded during large-scale artillery defense scenarios, while only a small number of modules are installed to maximize maneuverability when responding to small-scale threats. This modularity provides high operational flexibility not only in drone payload capacity but also in launch methods, dispersion density, and deployment time.

[0059] The swarm drone-mounted platform (120) includes an AI-based drone deployment algorithm. When threat information is input, it calculates the optimal deployment point based on the speed, location, trajectory, and type of the detected threat. In particular, a reinforcement learning-based drone distribution optimization model determines a swarm deployment pattern so that the drone swarm can effectively surround or block the target.

[0060] The platform is equipped with an automated dispersal mechanism that selectively operates using pneumatic compressed air, electromagnetic launch, or sliding rail-based transport methods depending on the situation. This launch mechanism precisely controls the drone's initial velocity, launch angle, and deployment altitude, enabling the drones to rapidly form a swarm in the air.

[0061] The onboard platform is not limited to defense-only operations. It can be immediately switched to non-military purposes, such as surveillance and reconnaissance, disaster relief, and communications relay. The interior features a shock-absorbing structure and an automatic temperature control system to protect the drone from damage even in extreme environments, while an intelligent management system is applied to automatically maintain battery charging, inspections, and operational readiness.

[0062] Detailed description of the swarm drone mounting module (121)

[0063] The swarm drone carrying module (121) is a core sub-component designed to safely store multiple small self-destructing drones and to quickly release them when necessary. This module is manufactured in the form of a container, and its interior is precisely designed with a structure for shock absorption, power maintenance, and communication connection of the drones. The exterior is made of bulletproof and heat-resistant materials, maintaining high durability even in a battlefield environment.

[0064] The interior of the onboard module is structured with cell-unit storage slots. Each slot is designed with a 3-axis shock absorption structure to fit the shape of the drone, and the drone is kept in an automatic charging state. Although the interior of the module is sealed, automatic ventilation and temperature control functions are used when necessary to prevent the drone's electronic components from overheating.

[0065] The onboard module is connected to the upper platform in real time via an electrical interface and a data link port. The module is equipped with a drone status monitoring system that checks each drone's battery level, communication status, and sensor malfunctions in real time. Drones with issues are automatically excluded from use or switched to a priority inspection mode.

[0066] It is equipped with an AI-based dispersal control system that does not simply release drones in bulk, but individually adjusts the dispersal timing, direction, and initial speed of each drone according to the type and location of the threat. For example, when intercepting ballistic missiles, it is launched at a high-speed ascent angle, and when defending against low-altitude aircraft, it is released in a parabolic shape and hovers over the target area.

[0067] The payload module also incorporates a recovery function. After an operation, surviving drones return to a designated location via a return command, and upon return, they are safely stored back in the module using an automatic recovery lift located on top. This enables continuous operational use and allows for the repeated recharging, status checks, and redeployment of the drones.

[0068] Detailed explanation of suicide drones

[0069] The self-destructing drone housed in the swarm drone-mounted module (121) serves as a core interception means of the present invention and is a composite drone equipped with miniaturization, lightweight design, high maneuverability, and intelligent control functions. Unlike conventional UAVs, the self-destructing drone is a special-purpose drone designed not for simple reconnaissance or surveillance purposes, but for direct collision with a specific target, near-field explosion, or electronic warfare jamming. The body of the drone is composed of carbon fiber composite material and impact-resistant aramid fiber, maintaining structural stability during high-speed collisions while remaining lightweight. The outer shell of the body features a low-observability design that minimizes the radar cross-section (RCS).

[0070] The suicide drone is equipped with a high-performance optical sensor, infrared (IR) detector, radar receiver, and RF detector at the front. This composite sensor package performs target detection, tracking, identification, and distance measurement in real time. An AI control module capable of high-speed computation is mounted in the center, enabling the drone to individually recognize changes in target speed, direction, and evasive maneuvers, and to modify its flight path in real time in response. In particular, the AI ​​utilizes a YOLOv8-based image recognition algorithm and a Transformer-based time-series path prediction model to deliver high-precision target tracking performance.

[0071] The power system is based on a high-output brushless electric motor and a high-density lithium polymer battery, and a hybrid fuel cell can be installed if necessary. The maximum flight speed is over 250 km / h, and the maximum flight time is approximately 45 to 60 minutes. The drone is capable of stable navigation even in electronic warfare environments through GPS, an Inertial Navigation System (INS), and an AI-based non-GPS navigation system. This means that it can approach targets without deviating from its trajectory, even in situations involving GPS jamming or spoofing.

[0072] The weapon systems of suicide drones are selectively configured based on target characteristics. By default, they are equipped with High Explosive Defense Propellant (HEDP) or fragmentation warheads at the front, and kinetic energy collision interception is also applied against high-velocity targets such as ballistic missile warheads or missiles. Built-in RF fuses for proximity detonation cause them to explode automatically upon approaching a target within a set radius, or AI directly controls the timing of impact to induce optimal strike effects. Additionally, some drones are equipped with an Electronic Warfare (EW) package, allowing them to operate in a "soft kill" mode by jamming enemy radar, communications, and GPS signals.

[0073] The drones are equipped with swarm network communication capabilities. An internal mesh network is formed among the drones, allowing information detected by a single drone to be shared in real-time with the entire swarm. Through this, the drone swarm responds immediately to changing situations and performs collaborative deployment, tracking, and interception against targets. Furthermore, the structure is designed so that surviving drones perform an automatic return function after the mission ends or maintain a close standby state when necessary to respond immediately to subsequent threats. Equipped with such autonomy, intelligence, and both offensive and defensive capabilities, these self-destructing drones represent an innovative system that is fundamentally differentiated from existing missile systems or single-drone-based defense systems.

[0074] Detailed description of the land-type platform (122)

[0075] The ground-type platform (122) is a specialized structure for operating the swarm drone-carrying platform (120) on the ground and is operated in the form of a vehicle-based mobile launch system or a fixed defensive base. The vehicle-type platform is mounted on a 6-wheel, 8-wheel, or tracked armored vehicle and has high mobility and battlefield adaptability. The fixed type is installed for air defense positions or for defending key facilities.

[0076] A swarm drone mounting module (121) is mounted in a multi-layered arrangement on the top of the platform. The multi-layered structure can be expanded from a minimum of 2 layers to a maximum of 6 layers, and each layer is equipped with an independent launch rail or spraying device. This allows tens to hundreds of drones to be sprayed simultaneously or sequentially in a short period of time.

[0077] The vehicle platform includes autonomous driving capabilities. In addition to fixed routes, AI analyzes real-time battlefield information to perform tasks such as avoiding hazardous areas, selecting concealment positions, and determining optimal launch locations. It is equipped with an AI path optimization algorithm combined with LiDAR, GPS, satellite data, and battlefield terrain data.

[0078] The land-based platform is designed with bulletproof, explosion-proof, and electronic warfare countermeasures, providing high survivability against EMP (electromagnetic pulse) attacks and precision-guided missile threats. Additionally, equipped with a self-contained generator and a solar auxiliary power system, it can operate independently for extended periods without external power supply.

[0079] In addition to combat, the platform can be utilized for civilian missions such as disaster response, border surveillance, and security for major events. For example, it can perform aerial surveillance and real-time data collection using drones during wildfires, and detect and track foot intrusions during border breaches. These multi-purpose operational capabilities significantly enhance the strategic value of the platform.

[0080] Detailed description of the aerial platform (123)

[0081] The aerial platform (123) is configured for aerial operation of the swarm drone-carrying platform (120) and is mounted on various aircraft such as helicopters, transport planes, and fighter jets to perform the role of directly scattering or launching drones in the air. Based on aerial maneuverability, this platform can rapidly approach the airspace above a threat area and efficiently deploy drones. In particular, compared to ground or sea platforms, it has the advantage of being easy to secure visibility and significantly expanding the deployment range of drones.

[0082] The platform features a modular design that allows it to be installed either inside or outside an aircraft. The internal version operates while loaded in the cargo bay of transport aircraft (such as the C-130 and KC-390), while the external version is mounted on the underside hardpoints of fighter jets or helicopters. The external version is designed with a streamlined ballistic capsule structure to minimize aerodynamic drag. The internal version can carry a large number of drones and operates by deploying multiple drones simultaneously through the opening of the cargo door.

[0083] The aerial platform is equipped with an AI-based algorithm for optimizing deployment locations. By receiving inputs such as enemy detection evasion paths, predicted ballistic missile impact points, and expected collision points along the missile's trajectory, it automatically calculates the optimal drone release altitude, speed, and direction. As a result, the drones can rapidly form a swarm in the air and immediately enter designated defense zones to carry out operations.

[0084] The platform features a highly sophisticated communication interface with aircraft. It receives real-time sensor data from aircraft (radar, EO / IR, signal intelligence) and utilizes it for drone deployment. Furthermore, through a proprietary data link with the drone, it enables real-time drone status monitoring, route correction, and operational changes even while airborne. This system maximizes tactical flexibility and provides high responsiveness in a battlefield environment where threats are rapidly changing.

[0085] The aerial platform is designed to enable stable drone deployment even in extreme conditions, such as storms, strong winds, and electronic warfare environments. In particular, it features a built-in automatic attitude control system that allows the drone to launch stably even with changes in the aircraft's pitch, yaw, and roll. Furthermore, the aerial platform compensates for the limitations of ground platforms, which cannot deploy drones in specific areas for extended periods, and serves as a highly powerful tactical asset capable of rapidly establishing a drone defense network in strategic regions through high-speed maneuvers.

[0086] Detailed description of the offshore platform (124)

[0087] The maritime platform (124) is configured for the maritime operation of the swarm drone-carrying platform (120) and is mounted on various marine carriers such as warships, naval vessels, high-speed boats, and large merchant ships to perform the role of operating drones. The platform is designed with a structure optimized for the marine operational environment and can rapidly respond to complex threats (cruise missiles, ballistic missiles, drone swarms, etc.) that may occur at sea.

[0088] The platform can be installed on the ship's upper deck, helipad, or internal hangar. In particular, the ship-mounted platform is installed in a multi-launch configuration aft of the bridge or on the stern deck; it features highly flame-resistant materials and waterproof and dustproof designs, ensuring high durability even in maritime environments. At the same time, an automatic leveling system is applied to ensure stable drone launching even when the ship rolls or pitches due to waves.

[0089] The maritime platform links detection information with drone deployment strategies in real time. For example, the platform's AI immediately analyzes threat information detected by the ship's radar, sonar, and EO / IR sensors to automatically calculate the deployment location, time, and direction of the drones. In particular, the maritime platform can deploy drones to intercept and defend against predicted ballistic missile impact points or the approach paths of low-altitude drone swarms.

[0090] The platform is integrated with the ship's power system and is equipped with its own power generation system. It also includes an EMP protection design and integration capabilities with the ship's electronic warfare system. This ensures resistance to enemy electronic warfare attacks or GPS jamming during drone launch, while guaranteeing secure communication and data exchange between the drone and the platform.

[0091] The maritime platform can also be utilized for civilian disaster response. It can be deployed for various missions during peacetime, such as maritime distress rescue, marine pollution monitoring, and search and reconnaissance. Furthermore, it performs wide-area sea surveillance and defense missions by continuously operating drones along the vessel's movement path. These multi-purpose capabilities and specialized functions for maritime operations establish the maritime platform as one of the core components of this invention.

[0092] Detailed explanation of the active response central control unit (130)

[0093] The active response central control unit (130) serves as the brain responsible for overall operational control and command of the small self-destructing drone defense system of the present invention. This central control unit is not a simple data relay device, but is composed of a highly intelligent command system that includes AI-based real-time judgment, tactical decision-making, establishment of multiple interception strategies, and control of swarm drones.

[0094] The central control unit collects all sensor data input from the defense target detection unit (110) in real time, refines and integrates it, and comprehensively recognizes the battlefield situation. Various inputs such as radar signals, infrared images, acoustic data, and RF signals are processed by a multi-sensor fusion algorithm, and a deep learning-based object detection model and a time-series data analysis model are applied simultaneously. Through this, false positives and missed detections are minimized, and the accuracy of identifying threat elements is maximized.

[0095] The Active Response Central Control Unit includes a reinforcement learning-based tactical optimization engine. This engine calculates the most efficient response strategy based on data regarding the types (long-range artillery, ballistic missiles, missiles, drones, etc.), quantities, speeds, and trajectories of currently detected threats. The strategy includes drone deployment density, interception priorities, interception methods (collision, close-range self-destruction, electronic warfare, etc.), and deployment paths. The AI ​​learns from historical battlefield data and real-time situational data simultaneously, continuously improving operational effectiveness.

[0096] The Central Control Center also manages communication and collaboration among multiple platforms. It transmits and receives data in real time not only with ground, maritime, and aerial platforms but also with the drones' own autonomous networks. This enables a transition from a centralized command mode to a distributed command mode depending on the situation. Even if network failures occur or some communications are disrupted due to electronic warfare attacks, the drones can perform autonomous operations based on prior strategic instructions from the Central Control Center.

[0097] The Active Response Central Control Unit is equipped with a battlefield awareness display and interface system. The display visualizes the real-time location, predicted trajectory, drop point, and drone deployment status of threat objects, allowing operators to control operations by switching between automatic, semi-automatic, and manual modes as needed. In addition to recommendations provided by AI, this system supports the intuitive judgment of human commanders, maximizing the reliability of the entire system and the success rate of operations.

[0098] Detailed description of the defense target location determination module (131)

[0099] The defense target location determination module (131) performs the function of calculating the current location of the detected threat object with great precision. It is not merely a matter of displaying the detected coordinate data; rather, a highly advanced location estimation algorithm is applied to time-synchronize asynchronous data collected from multiple sensors and minimize spatial errors.

[0100] This module is structured around an AI-based sensor fusion algorithm. Radar, EO / IR, acoustic, and RF detection signals each have different characteristics and provide slightly different coordinate values ​​for the same target. Accordingly, the position determination module dynamically adjusts the reliability weights of each sensor using probabilistic filtering techniques such as the Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter.

[0101] Even when detection signals are partially missing or distorted, this module corrects the missing information through a deep learning-based recovery model. For example, if a drone swarm moves out of the detection range of some sensors using terrain features, a time-series pattern recognition algorithm predicts and interpolates the current location based on previous trajectory data. This feature enables highly accurate location determination even in radar blind spots or situations involving communication failures.

[0102] The defense target location determination module also includes an object identification function. When multiple threat objects are detected simultaneously, the AI ​​uses an RNN-based multi-object tracking algorithm to uniquely identify the location of each object. This enables the individual location estimation of objects that intersect or approach in clusters, preventing duplicate detection or tracking errors.

[0103] The accurately calculated location data is immediately transmitted to the path prediction module (132). At the same time, it is also shared with the active response central control unit (130) and utilized in the initial deployment strategy of the drone swarm. In addition, the location determination module outputs the location accuracy index, detection reliability, and estimated error range together, thereby supporting the central control unit to perform tactical judgments more precisely.

[0104] Detailed description of the defense target path prediction module (132)

[0105] The defense target path prediction module (132) is responsible for predicting the future movement trajectory of a threat object in real time based on current location data input from the location determination module (131) and information such as speed, direction, and altitude. This module performs advanced prediction functions that go beyond simple straight-line trajectory prediction and include ballistic curves, evasive maneuvers, and changes in speed.

[0106] Path prediction algorithms combine physics-based simulations with AI-based learning models. For ballistic missiles, trajectory calculations are performed by reflecting air resistance, gravity, rotational force, and velocity changes with altitude. For drones or cruise missiles, the AI ​​learns past evasive maneuver patterns and current velocity changes to calculate the path with the highest probability. In particular, Transformer-based time-series prediction models provide high accuracy even in multi-variable environments.

[0107] The prediction module includes a reinforcement learning-based behavior prediction engine. It derives from training data the likelihood that an enemy aircraft will ascend at a specific altitude or abruptly change speed to evade defense networks. This enables path prediction that considers evasion patterns occurring with high probability in actual battlefields, rather than assuming only a typical straight path.

[0108] This module is capable of simultaneously predicting multiple threat objects. By applying the Multiple Hypothesis Tracking (MHT) algorithm, it calculates possible path branches that may occur as each object moves in real time and continuously updates the path with the highest probability. For example, if a ballistic missile separates into multiple warheads in the form of a Multi-Independent Re-entry Vehicle (MIRV), the individual trajectory of each warhead is automatically predicted.

[0109] The results of the path prediction are transmitted in real time to the active response central control unit (130), and based on this, the central control unit immediately determines the drone's deployment, approach path, interception point, and electronic warfare application location. In addition, this data is also transmitted to the drone's onboard AI, enabling the drone to autonomously perform operations by readjusting its path in real time according to changes in the situation.

[0110] Detailed description of the multi-interception algorithm module (133)

[0111] The multi-interception algorithm module (133) is responsible for establishing and executing a simultaneous interception strategy against multiple threat targets. In a modern battlefield environment where multiple attack targets appear simultaneously—such as dozens of long-range artillery shells, multiple independently targetable reentry vehicles (MIRVs), and swarms of low-altitude drones—as well as a single threat, the role of this module is very important. An AI-based optimization algorithm is applied as the core, and a multi-layered interception plan is automatically established based on the speed, direction, expected time of arrival, size, and threat level of each threat object.

[0112] This module applies a combination of methods—including physical collision interception, close-range suicide interception, and electronic warfare-based soft kill strategies—depending on the situation. For instance, collision interception is prioritized against extremely fast and destructive targets, such as ballistic missiles, while low-speed, low-altitude threats like drone swarms are countered using close-range suicide or electronic warfare. This multi-interception selection operates based on reinforcement learning and continuously optimizes interception strategies using real-time battlefield data.

[0113] The multi-interception algorithm features a network-centric, collaborative structure. It enables mutual cooperation among drone swarms deployed on land, sea, and air platforms, with each platform performing interceptions independently or jointly. For example, drones are deployed to an initial interception point against a cruise missile first detected by an air platform, while drones on the ground platform stand by at a second line of defense to form a multi-layered defense.

[0114] This module applies probabilistic path analysis and multi-objective optimization algorithms. Metaheuristic techniques such as the Genetic Algorithm and Particle Swarm Optimization are utilized to derive optimal solutions that simultaneously satisfy minimum drone consumption, maximum interception success rates, and shortest response times against multiple threats. This approach generates deployment strategies of high tactical value, going beyond simple distance-based shortest path calculations.

[0115] The multi-interception algorithm module also features enhanced situational awareness capabilities. If an enemy utilizes decoys or attempts electronic warfare jamming, it detects this and separates actual threat objects from false signals. The AI ​​determines the presence of decoys based on patterns, velocity changes, and trajectory inconsistencies in the detected data, and dynamically adjusts drone deployment priorities accordingly. This function plays a crucial role in minimizing unnecessary drone losses due to false positives in situations where multiple threats coexist.

[0116] Detailed explanation of the active response control module (134)

[0117] The active response control module (134) performs the role of comprehensively controlling the operation of all components that actually carry out defensive operations in real time. It is not limited to simply launching drones, but has a command and control function that automates the entire process from detection, analysis, path prediction, interception, and recovery, and actively changes the strategy in accordance with changes in the situation.

[0118] This module includes an AI-based situational awareness engine. It comprehensively analyzes data input from the defense target detection unit, location determination module, path prediction module, and multi-interception algorithm module to perceive the current battlefield situation in a multidimensional manner. For example, it modifies response strategies by considering the current threat density, remaining number of drones, weather conditions, and platform status in real time.

[0119] The active response control module enables action control based on event triggers. When a specific threat is detected, it automates a series of operations—such as 'detection → alert → prepare for interception → drone deployment → execute interception → identify remaining threats → recovery or additional deployment'—using a trigger-based approach. Furthermore, the AI ​​continuously learns during this process and automatically generates optimized response sequences for recurring situations.

[0120] Furthermore, this module supports collaborative operations between humans and AI. It allows switching between manual, semi-automatic, and automatic modes; operators can issue manual interception orders when tactical judgment is required, or select from optimal response scenarios suggested by the AI. Particularly in critical situations, it switches to automatic mode to enable the AI ​​to perform an immediate response, thereby minimizing operational delays.

[0121] The active response control module also supports synchronization control functions between platforms. It adjusts drone deployments in real-time to prevent overlap or blind spots among land-based, maritime, and air-based platforms. For example, maritime platforms automatically respond to maritime threats that are difficult for land-based platforms to access, ensuring that the entire operation functions as a single, integrated defense system.

[0122] Detailed description of the swarm drone control module (135)

[0123] The swarm drone control module (135) performs the role of controlling the entire drone swarm to operate as a single organism by networking the drones, rather than controlling individual drones. Based on a bidirectional data link between the central control unit and the drones, this module monitors and manages the drones' location, speed, status, remaining energy, sensor data, etc., in real time.

[0124] This module applies an autonomous collaboration algorithm among drones. Based on the relative position and status information of each drone, the AI ​​maintains the optimal formation within the swarm and dynamically distributes roles among the drones according to threat situations. For example, the lead drone performs reconnaissance and detection missions, while follow-up drones maintain a state of readiness for interception, with their positions changing immediately if necessary.

[0125] The swarm drone control module is based on the Swarm Intelligence Algorithm. A combination of algorithms, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Boid, is applied, enabling the drone swarm to autonomously perform tasks such as obstacle avoidance, optimal path generation, collision avoidance, and dynamic formation maintenance.

[0126] In addition, this module features tactical pattern recognition and automatic formation generation capabilities. It automatically forms a linear blocking formation when the threat is a ballistic missile, and a three-dimensional spherical defensive formation when the threat is a drone swarm. This process is accomplished by AI analyzing the type, speed, and approach direction of the threat to calculate and determine the optimal formation in real time.

[0127] The swarm drone control module manages the automatic recovery and return of drones after the operation is completed. Surviving drones either return to the platform or move to a temporary safe zone to prepare for future reuse. Furthermore, based on remaining energy and status information among the drones, it automatically plans the immediate recovery, charging, and redeployment following the execution of interception missions. This function significantly improves the sustainability and cost-efficiency of drone operations.

[0128] As explained above, the present invention fundamentally resolves the economic limitations of existing air defense weapon systems. Conventional missile interception systems are highly inefficient in responding to attacks by inexpensive ammunition or drones because they use expensive interceptor missiles. On the other hand, the present invention is designed to drastically reduce the unit cost by utilizing small suicide drones and to enable continuous and repetitive defense against multiple threats. As a result, cost-effective defense is possible against long-range artillery shells, low-cost rockets, and drone swarm attacks used in large quantities by the enemy.

[0129] Furthermore, the present invention significantly enhances the physical response capability against mass attacks. Conventional defense systems provide only limited interception means against large volumes of threats, such as artillery shells or ballistic missiles, that fly in concentratedly over a short period. In contrast, the drone swarm of the present invention deploys multiple self-destructing drones in real time to simultaneously intercept targets or block their paths, thereby dramatically improving the defense capability against mass attacks that existing systems could not handle.

[0130] Existing defense systems have very low responsiveness against low-altitude infiltrating aircraft, whereas the present invention enables defense specialized against low-altitude aircraft and small drones. Small drones and cruise missiles are difficult to detect and intercept with conventional air defense missiles because they utilize terrain features to evade radar detection or fly at low speeds. However, the swarm drones of the present invention possess overwhelming responsiveness against such low-altitude threats by freely deploying at low altitudes, changing positions in real time, and simultaneously performing physical interception and electronic warfare jamming.

[0131] This invention significantly expands operational range and flexibility through platform versatility. While existing air defense systems are fixed or limited to specific locations, the drone-mounted platform of this invention can be mounted on various carriers, such as land vehicles, warships, naval vessels, helicopters, fighter jets, and transport aircraft. This enables the immediate deployment to locations where threats are detected, without restrictions on the operational area, to carry out defensive operations.

[0132] By utilizing AI-based path prediction and multi-interception algorithms, the present invention implements precise defense. Existing systems are capable only of simple tracking-based interception, making it difficult to respond rapidly to changes in the speed or path of changing threat objects. The central control unit of the present invention analyzes the speed, trajectory, and predicted collision point of threat objects in real time and calculates the optimal drone deployment to maximize interception efficiency. This intelligent defense provides a significantly higher success rate compared to conventional technology.

[0133] Electronic warfare countermeasures are also one of the key effects of this invention. Existing defense systems focus on physical interception, and thus lack the capability to neutralize enemy communications, GPS, and sensor systems. The self-destructing drone of this invention can perform electromagnetic jamming, GPS disruption, and communication blocking functions when necessary, thereby simultaneously neutralizing not only physical interception but also the enemy's electronic operational capabilities. This provides a complex defensive effect that is crucial in modern warfare.

[0134] Consequently, the present invention is an innovative defense platform that resolves all the problems of conventional defense systems, such as detection limitations, cost issues, limitations in responding to mass attacks, and the lack of response to low-altitude intrusions. This low-cost, high-efficiency drone swarm defense method can replace or complement existing missile-centric defense systems and is applicable not only to military purposes but also to various fields such as the protection of key facilities, border surveillance, and disaster response; therefore, it is expected to change the paradigm of future defense systems.

[0135] The above detailed description of the present invention describes only specific embodiments thereof. However, it should be understood that the present invention is not limited to the specific forms mentioned in the detailed description, but rather should be understood to include all variations, equivalents, and substitutions within the spirit and scope of the invention as defined by the appended claims.

[0136] In other words, the present invention is not limited to the specific embodiments and descriptions described above, and any person skilled in the art to which the present invention pertains can make various modifications without departing from the essence of the invention as claimed in the claims, and such modifications fall within the scope of protection of the present invention. Explanation of the symbols

[0137] 100: Small suicide drone defense system 110: Defense Target Detection Unit 111: Orbital Artillery Detection Module 112: Low-altitude aircraft detection module 113: Ballistic Missile Detection Module 114: Missile detection module 120: Swarm Drone Carrier Platform 121: Swarm Drone Payload Module 122: Land-based platform 123: Aerial Platform 124: Offshore Platform 130: Active Response Central Control Center 131: Defense Target Location Determination Module 132: Defense Target Path Prediction Module 133: Multi-interception algorithm module 134: Active Response Control Module 135: Swarm Drone Control Module

Claims

Claim 1 A defense target detection unit (110) that detects enemy long-range artillery, self-propelled artillery, coastal artillery, naval artillery, low-altitude infiltration aircraft, ballistic missiles, and missiles, and transmits real-time detection data to an active response central control unit (130); a swarm drone carrying platform (120) that carries multiple small self-destructing drones and operates by a control signal transmitted from the active response central control unit (130) to scatter swarm drones at preset scattering positions; The system includes an active response central control unit (130) that controls the operation of a swarm drone-carrying platform (120) by utilizing an AI-based path prediction method and a multi-interception algorithm based on data acquired in real time from the defense target detection unit (110); wherein the swarm drone-carrying platform (120) comprises: a swarm drone-carrying module (121) having a structure that stores a number of suicide drones, is launched or transported to a pre-set scattering position by a ground-type platform (122), an aerial-type platform (123), and a sea-type platform (124), and releases the stored number of suicide drones at the pre-set scattering position; a ground-type platform (122) that launches or scatters the swarm drone-carrying module (121) as a multi-launcher; and an aerial-type platform (123) that loads the swarm drone-carrying module (121) onto a helicopter, fighter jet, or transport aircraft to launch or scatter it. A small self-destructing drone defense system characterized by including: a maritime platform (124) that loads the above-mentioned swarm drone mounting module (121) onto a ship or vessel and launches or disperses it. Claim 2 In claim 1, the defense target detection unit (110) comprises: a long-range artillery detection module (111) that detects the firing of enemy long-range artillery, self-propelled artillery, coastal artillery, and naval gun shells, analyzes the trajectory to predict the landing point, and detects the physical characteristics of the shelling from multiple angles by simultaneously utilizing a low-frequency ballistic detection radar and an ultra-precision acoustic sensor array; a low-altitude aircraft detection module (112) that detects low-altitude penetrating aircraft including drones, low-speed cruise missiles, and gliders, and utilizes a high-performance low-frequency radar and AI-based image and acoustic analysis technology; and a ballistic missile detection module (113) that tracks and predicts the entire flight segment of a ballistic missile in real time from immediately after launch to the ascent, peak, and descent phases, and identifies the ballistic missile body, multiple independently targetable reentry vehicle (MIRV) separator, and decoy by simultaneously operating a high-frequency band radar and a low-frequency band radar. A small suicide drone defense system characterized by including a missile detection module (114) equipped with a complex detection system that detects and tracks missiles including cruise missiles, guided missiles, air-to-ground missiles, and stealth missiles, and integrates a low-frequency band radar, a high-frequency band radar, an infrared (IR) sensor, an electro-optical (EO) sensor, and an RF signal detection device. Claim 3 delete Claim 4 A small self-destructing drone defense system according to claim 1, wherein the self-destructing drone housed in the swarm drone mounting module (121) is structured to hover or fly at a predetermined scattering position, flies in a swarm or hovers according to a predetermined net arrangement pattern together with a plurality of self-destructing drones, and when approaching a defense target within a predetermined range, self-destructs, emits jamming electromagnetic waves toward the defense target, or performs collision interception with the defense target based on the predicted path of the defense target. Claim 5 In paragraph 4, the active response central control unit (130) comprises: a defense target location determination module (131) that calculates the current location of a detected threat object, time-synchronizes asynchronous data collected from multiple sensors, and utilizes an advanced location estimation algorithm to prevent spatial errors; a defense target path prediction module (132) that predicts the future movement trajectory of a threat object in real time based on current location data input from the location determination module (131) and information such as speed, direction, and altitude; a multi-interception algorithm module (133) that establishes and executes a simultaneous interception strategy against multiple threat targets; and an active response control module (134) that comprehensively controls the operation of all components that actually perform defense operations in real time, automates the entire process from detection-analysis-path prediction-interception-recovery, and actively changes the strategy in accordance with changes in the situation. A small self-destructing drone defense system characterized by including a swarm drone control module (135) that networks the entire drone swarm to control it to operate as a single organism, and monitors and manages the drone's position, speed, status, remaining energy, and sensor data in real time based on a bidirectional data link between the central control unit and the drone.

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