High-speed unmanned aerial vehicle photoelectric tracking locking system and precise countering method

Abstract

The invention discloses a high-speed unmanned aerial vehicle photoelectric tracking locking system and a precise countering method, relates to the technical field of unmanned aerial vehicle control and photoelectric detection, and aims to solve the technical problems of poor tracking stability of a high-speed unmanned aerial vehicle and lack of pertinence in countering when the unmanned aerial vehicle is countered in the prior art. The system comprises a hyperspectral imaging and locking module, a trajectory prediction and miss distance calculation module, a laser ranging and dynamic focusing module, a magnetofluid countering ammunition presetting module and a cooperative control unit.

Description

Technical Field

[0001] This application relates to the fields of drone control and photoelectric detection technology, specifically to a high-speed drone photoelectric tracking and locking system and a precise countermeasure method. Background Technology

[0002] With the rapid popularization of drone technology, unauthorized drone activities pose an increasingly serious security threat to critical areas such as airports, military facilities, and large public event venues. Historical records show that malicious drone incidents targeting critical infrastructure have been on the rise in recent years, putting significant pressure on both civilian and military security environments. Existing anti-drone systems mainly consist of two parts: detection and countermeasures. The detection component encompasses technologies such as radar scanning, radio spectrum monitoring, photoelectric imaging identification, and acoustic positioning. However, single detection methods generally have inherent defects: radar systems are susceptible to ground clutter interference, leading to missed detections of low-altitude targets; radio spectrum detection cannot identify autonomous aircraft not controlled by communication; photoelectric detection is unstable due to weather conditions and changes in lighting; and acoustic monitoring has limited propagation distance and cannot cover a wide area. In the countermeasures component, mainstream solutions include full-band electromagnetic interference, navigation signal deception, physical capture devices, and kinetic energy impact interception. However, traditional photoelectric tracking systems exhibit serious shortcomings when facing high-speed, maneuvering small drones exceeding 15 meters per second: significant delays in image acquisition and processing lead to sluggish target lock response; the mechanical gimbal structure, limited by physical inertia, cannot promptly match the target's highly maneuverable trajectory changes, resulting in frequent misses during tracking; simultaneously, the system lacks the ability to accurately identify the target's material properties and key structures, leading to insufficient basis for countermeasure decisions. Regarding countermeasures, while electromagnetic interference technology can disrupt communication links, it indiscriminately affects surrounding electronic devices and generates electromagnetic pollution, and is ineffective against autonomously navigational drones operating on pre-set routes; net-catching technology is constrained by factors such as wind speed and target size, resulting in extremely low actual interception success rates, especially under complex weather conditions; kinetic interception systems rely on dedicated interceptor drones for physical collisions, which are not only costly but also unable to meet the real-time response requirements of multi-target swarm attacks. These deficiencies make it difficult for existing technologies to achieve a closed-loop operation from target lock to precise countermeasures, especially in high-speed scenarios where tracking stability is poor, countermeasures lack specificity, and secondary hazards are easily triggered. Summary of the Invention

[0003] The main purpose of this application is to provide a high-speed unmanned aerial vehicle (UAV) optoelectronic tracking and locking system and a precise countermeasure method, aiming to solve the technical problems of poor tracking stability and lack of targeted countermeasures when countering UAVs in existing technologies.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0005] In a first aspect, embodiments of this application provide a high-speed unmanned aerial vehicle (UAV) photoelectric tracking and locking system, comprising:

[0006] The hyperspectral imaging and locking module includes a short-wave infrared camera and a visible light camera, used to acquire information about the target UAV, its material type, and key parts.

[0007] The trajectory prediction and miss calculation module, based on the Kalman filtering algorithm and deep learning target detection network, calculates the motion trajectory and miss amount of the target UAV in three-dimensional space in real time.

[0008] The laser ranging and dynamic focusing module adjusts the focal length of the laser emitter in real time based on the target UAV's motion trajectory and miss distance in three-dimensional space, ensuring that the laser spot always locks onto the target's vital parts;

[0009] The pre-positioning module for magnetohydrodynamic countermeasure munitions includes an electromagnetic acceleration chamber and several micro-projectiles filled with conductive and magnetic powder. The electromagnetic acceleration chamber dynamically adjusts the powder loading amount of the projectiles according to the target classification results.

[0010] The collaborative control unit is connected to the above modules and is used to receive data from the trajectory prediction module. When it is determined that the target has entered the countermeasure range, it calculates the minimum magnetic particle interference threshold according to the target material type and motion parameters, and triggers the magnetohydrodynamic countermeasure ammunition pre-set module to adjust the loading amount before launching based on the threshold.

[0011] As some optional embodiments of this application, the hyperspectral imaging and locking module further includes:

[0012] The feature band selection unit is used to select the corresponding identification band to distinguish the target material based on the spectral reflectance characteristics corresponding to the material type of the target UAV.

[0013] As some optional embodiments of this application, the magnetohydrodynamic countermeasure ammunition pre-positioning module further includes:

[0014] A powder loading dynamic adjustment mechanism controls the electromagnetic acceleration chamber to inject a predetermined amount of conductive and magnetic powder into the projectile based on the minimum magnetic powder interference threshold calculated by the collaborative control unit.

[0015] As some optional embodiments of this application, the trajectory prediction and miss distance calculation module further includes:

[0016] An interactive multi-model filter is used to switch between different motion models to improve prediction accuracy when the target UAV performs high-maneuver evasion.

[0017] Secondly, this application also provides a high-speed UAV precision countermeasure method based on the above system, including the following steps:

[0018] Identify the target drone's data and motion parameters to pinpoint its vital parts; the data includes material data, volume data, motor power, rotation speed, and protection level;

[0019] Calculate the velocity vector of the target UAV based on its motion parameters, and simultaneously calculate the minimum magnetic particle interference threshold required to cause its motor to fail based on the data of the target UAV.

[0020] According to the minimum magnetic powder interference threshold, the amount of conductive powder loaded in the projectile is adjusted, and the projectile is accelerated to a predetermined trajectory where it meets the target UAV by electromagnetic acceleration, so that the projectile disintegrates after reaching the predetermined interception point, releasing the conductive and magnetic powder. The powder is then forcibly sucked into the motor gap by the downwash airflow generated by the rotor of the target UAV.

[0021] The real-time motion parameters of the target drone are obtained to determine whether it is out of control. If it is not out of control, the countermeasures are recalculated and executed based on the real-time motion parameters.

[0022] As some optional embodiments of this application, the minimum magnetic particle interference threshold is obtained based on the following steps:

[0023] Based on the motor power, speed, and protection level of the target UAV, a model is established to establish the correspondence between the amount of magnetic powder absorbed and the probability of motor failure. The minimum amount of magnetic powder used to achieve a motor failure probability of over 95% is selected as the threshold.

[0024] As some optional embodiments of this application, the amount of conductive powder loaded in the projectile is obtained by adjusting the acceleration voltage of the electromagnetic acceleration chamber according to the target's movement speed, so that the relative speed of the projectile when it meets the target at the predetermined interception point is within a preset range.

[0025] As some optional embodiments of this application, the projectile disintegration is controlled by a time-delay fuse or a laser proximity fuse. The delay time of the fuse is dynamically adjusted according to the motion parameters of the target UAV, which include the miss distance and relative velocity.

[0026] As some optional embodiments of this application, the critical part refers to the connection between the UAV motor and the battery compartment, which exhibits specific thermal radiation characteristics and structural features in hyperspectral images.

[0027] As some optional embodiments of this application, obtaining the real-time motion parameters of the target UAV refers to monitoring the flight attitude changes, altitude descent rate, or infrared feature changes of the target UAV through a hyperspectral imaging module to determine whether it has lost power or gone out of control.

[0028] Compared with existing technologies, the high-speed UAV photoelectric tracking and locking system and high-speed UAV precision countermeasure method provided in this application acquire target information through a hyperspectral imaging and locking module, calculate the motion trajectory in real time through a trajectory prediction and miss distance calculation module, adjust the focal length and lock the vital parts through a laser ranging and dynamic focusing module, dynamically adjust the loading amount through a magnetohydrodynamic countermeasure ammunition pre-positioning module, and calculate the threshold and trigger countermeasures through a collaborative control unit. This solves the problems of poor tracking stability and lack of specificity in countermeasures in high-speed scenarios, and has the advantages of achieving precise locking and effective countermeasures. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application;

[0030] Figure 2 A flowchart illustrating the high-speed unmanned aerial vehicle (UAV) precision countermeasure method provided in this application embodiment;

[0031] Figure 3 A schematic diagram of the modules of the high-speed UAV photoelectric tracking and locking system provided in the embodiments of this application;

[0032] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0033] It should be made clear that the specific implementation examples described herein are for illustrative purposes only and are not intended to limit this application.

[0034] See attached document Figure 1 , attached Figure 1This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 105 may be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk storage device. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component.

[0035] Those skilled in the art will understand that the appendix Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0036] As attached Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a high-speed UAV photoelectric tracking and locking system.

[0037] In the appendix Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device. The electronic device calls the high-speed UAV photoelectric tracking and locking system stored in the memory 105 through the processor 101 and executes the high-speed UAV precision countermeasure method provided in the embodiment of this application.

[0038] See attached document Figure 2 Based on the hardware device described in the foregoing embodiments, embodiments of this application provide a method for precise countermeasures against high-speed unmanned aerial vehicles (UAVs), comprising the following steps:

[0039] Step S10: Identify the target drone's data and motion parameters, and pinpoint its vital parts; the data includes material data, volume data, motor power, rotation speed, and protection level;

[0040] Step S20: Calculate the velocity vector of the target UAV based on its motion parameters, and simultaneously calculate the minimum magnetic particle interference threshold required to cause its motor to fail based on the data of the target UAV.

[0041] Step S30: According to the minimum magnetic powder interference threshold, adjust the amount of conductive powder in the projectile, and use electromagnetic acceleration to accelerate the projectile to a predetermined trajectory where it will meet the target UAV, so that the projectile will disintegrate after reaching the predetermined interception point, release the conductive and magnetic powder, and use the downwash airflow generated by the rotor of the target UAV to force the powder into the motor gap.

[0042] Step S40: Obtain the real-time motion parameters of the target drone to determine whether it is out of control. If it is not out of control, recalculate and execute countermeasures based on the real-time motion parameters.

[0043] And, such as Figure 3 As shown, this application proposes a high-speed unmanned aerial vehicle (UAV) photoelectric tracking and locking system, comprising:

[0044] The hyperspectral imaging and locking module includes a short-wave infrared camera and a visible light camera, used to acquire information about the target UAV, its material type, and key parts.

[0045] The trajectory prediction and miss calculation module, based on the Kalman filtering algorithm and deep learning target detection network, calculates the motion trajectory and miss amount of the target UAV in three-dimensional space in real time.

[0046] The laser ranging and dynamic focusing module adjusts the focal length of the laser emitter in real time based on the target UAV's trajectory and miss distance in three-dimensional space, ensuring that the laser spot is always locked onto the target's vital parts.

[0047] The pre-positioning module for magnetohydrodynamic countermeasure munitions includes an electromagnetic acceleration chamber and several micro-projectiles filled with conductive and magnetic powder. The electromagnetic acceleration chamber dynamically adjusts the powder loading amount of the projectiles according to the target classification results.

[0048] The collaborative control unit is connected to the above modules and is used to receive data from the trajectory prediction module. When it is determined that the target has entered the countermeasure range, it calculates the minimum magnetic particle interference threshold according to the target material type and motion parameters, and triggers the magnetohydrodynamic countermeasure ammunition pre-set module to adjust the loading amount before launching based on the threshold.

[0049] For ease of understanding, the following explains some key terms in this embodiment:

[0050] A hyperspectral imaging and target locking module is a unit that integrates hyperspectral imaging technology and target locking functionality. This module acquires spectral information of the target object across multiple narrow bands, enabling it to identify the target's material type and physical characteristics of specific parts, thus achieving precise target identification and locking.

[0051] A shortwave infrared camera is an imaging device that operates in the shortwave infrared (SWIR) band. This camera can penetrate atmospheric conditions such as smoke and fog, and exhibits unique spectral reflectance characteristics of objects of different materials, making it suitable for identifying target materials and thermal radiation features.

[0052] A visible light camera is an imaging device that operates in the visible light band. This camera is used to acquire general image information of a target drone, providing high-resolution visual details and assisting in the identification of the target's shape, structure, and motion.

[0053] The trajectory prediction and miss distance calculation module is a computing unit responsible for analyzing target motion data in real time and predicting its future position. Through complex algorithms, this module can estimate the target's trajectory in three-dimensional space and calculate the deviation between the counter-missile and the target's predetermined interception point, i.e., the miss distance.

[0054] The Kalman filter algorithm is a recursive algorithm used to estimate the state of a dynamic system. By fusing observational data and a system model, this algorithm can optimize noisy measurement data, thereby improving the prediction accuracy of the target's trajectory.

[0055] Deep learning object detection networks refer to neural network models built based on deep learning technology. By training on large amounts of image data, these networks can automatically identify and locate specific targets in images and extract their feature information for real-time detection and classification of targets, such as drones.

[0056] A laser ranging and dynamic focusing module is a unit that integrates laser ranging and focus adjustment functions. This module determines the target distance by emitting a laser and measuring its round-trip time. It then adjusts the laser emitter's focus in real time based on the target distance and its movement to ensure the laser spot remains focused at different distances, achieving precise illumination of the target's vital areas.

[0057] A magnetohydrodynamic (MHD) countermeasure munition pre-loading module is a unit used for preparing and launching MHD countermeasure munitions. This module is responsible for storing micro-projectiles and loading conductive and magnetic powder into the projectiles according to countermeasure requirements, preparing for subsequent electromagnetic acceleration launch.

[0058] The electromagnetic acceleration chamber is a core component of the pre-positioning module for magnetohydrodynamic (MHD) countermeasures munitions. This chamber uses electromagnetic force to accelerate miniature projectiles filled with conductive and magnetically conductive powder to a predetermined launch velocity, thereby enabling rapid interception of target drones.

[0059] Micro-projectiles containing conductive and magnetic powder are micro-projectiles filled with conductive and magnetic materials. Upon reaching the vicinity of a target, the projectile disintegrates, releasing conductive and magnetic powder that can be sucked into the motors of the target drone, thereby interfering with or disrupting its normal operation.

[0060] The collaborative control unit is the brain of the entire system, responsible for coordinating and managing the work of various modules. This unit receives data from different modules, performs comprehensive analysis and decision-making, and sends control commands to other modules to achieve the overall function of the system.

[0061] The minimum magnetic particle interference threshold refers to the minimum amount of conductive and magnetic powder required to disable the motor of a target UAV. This threshold is calculated based on the specific parameters of the target UAV (such as material type and motion parameters) and is used to guide the precise loading of magnetohydrodynamic countermeasures munitions to ensure countermeasure effectiveness and reduce unnecessary resource consumption.

[0062] This embodiment provides a high-speed unmanned aerial vehicle (UAV) photoelectric tracking and locking system.

[0063] The system first detects and identifies the target drone using a hyperspectral imaging and locking module. This module includes a short-wave infrared camera and a visible light camera. The short-wave infrared camera acquires spectral information of the target drone in the short-wave infrared band, used to identify the drone's material type and thermal radiation characteristics. The visible light camera acquires visible light images of the target drone, providing high-resolution visual details. By fusing the data from these two cameras, the material type of the target drone can be identified, and its vulnerable parts, such as the motor or battery compartment, can be located. As one implementation, the hyperspectral imaging and locking module can simply overlay the short-wave infrared and visible light images, allowing operators to manually analyze the image information to identify target features.

[0064] After acquiring target UAV information, the trajectory prediction and miss distance calculation module is used to calculate the target UAV's trajectory and miss distance in three-dimensional space in real time. This module processes data based on the Kalman filtering algorithm and a deep learning target detection network. The Kalman filtering algorithm can estimate and predict the target UAV's motion state, providing relatively accurate trajectory information even in the presence of measurement noise. The deep learning target detection network is used to identify and track the target UAV from image data in real time and extract its position and velocity information. By combining these two algorithms, high-speed maneuvering targets can be continuously tracked, their future movement paths predicted, and the miss distance between the countermeasure munition and the target's predetermined interception point calculated. For example, this module can use a single Kalman filter to track the target and predict its trajectory based on its linear motion model.

[0065] Subsequently, the laser ranging and dynamic focusing module adjusts the focal length of the laser emitter in real time based on data provided by the trajectory prediction and miss distance calculation module. This module acquires precise distance information of the target UAV through laser ranging and dynamically adjusts the laser emitter's optical system according to the target distance and predicted trajectory, ensuring the laser spot remains focused at different distances. Thus, the laser spot can consistently and accurately lock onto the target UAV's vital parts, providing precise aiming for subsequent countermeasures. As one implementation method, the laser ranging and dynamic focusing module can use a fixed-focal-length laser emitter, attempting to maintain focus by adjusting the distance between the laser emitter and the target, or it can use manual focal length adjustment.

[0066] Furthermore, a magnetohydrodynamic (MHD) countermeasures munition pre-loading module is used to prepare and launch countermeasures munitions. This module includes an electromagnetic acceleration chamber and several micro-projectiles filled with conductive and magnetically conductive powder. The electromagnetic acceleration chamber is responsible for accelerating the micro-projectiles. Based on the classification results of the target UAV, such as its material type, size, or threat level, the electromagnetic acceleration chamber can dynamically adjust the powder loading amount of the micro-projectiles. For example, different powder loading amounts can be preset for different types of UAVs, and the corresponding projectiles can be selected and loaded based on the identification results before countermeasures.

[0067] Finally, the cooperative control unit, as the core of the entire system, is connected to the aforementioned hyperspectral imaging and locking module, trajectory prediction and miss distance calculation module, laser ranging and dynamic focusing module, and magnetohydrodynamic countermeasure munition pre-setting module. This unit receives real-time data from the trajectory prediction and miss distance calculation module. When it determines that the target UAV has entered the preset countermeasure range, the cooperative control unit calculates the minimum magnetic powder interference threshold required to disable its motors based on the target UAV's material type and motion parameters. Based on this calculated threshold, the cooperative control unit triggers the magnetohydrodynamic countermeasure munition pre-setting module, instructing it to adjust the powder loading amount of the micro-projectiles and launch them after loading is complete. For example, the cooperative control unit can simply determine whether the target has entered the countermeasure range based on the target distance and trigger countermeasures based on a preset fixed threshold without performing dynamic calculations.

[0068] This system accurately identifies the critical parts of high-speed UAVs through hyperspectral imaging, combines Kalman filtering and deep learning to achieve high-precision trajectory prediction and miss distance calculation, and locks onto the target using dynamically focused lasers. Furthermore, the system calculates the minimum magnetic powder interference threshold based on the target's material and motion parameters, driving the pre-loaded module of the magnetohydrodynamic countermeasure munition to dynamically adjust the powder loading and launch it with electromagnetic acceleration. This effectively solves the technical challenges of unstable tracking, inaccurate countermeasures, long lock-on delays, easy misses, and crude countermeasures with significant collateral damage associated with high-speed UAVs, achieving precise tracking, locking, and efficient countermeasures against them.

[0069] In some embodiments described above, a hyperspectral imaging and locking module is proposed to acquire the target UAV, its material type, and key parts. However, in this process, due to the different spectral reflectance characteristics of different materials, the lack of targeted band selection may lead to inaccurate material identification, affecting the accuracy of subsequent countermeasures. To address this, this application further proposes that the hyperspectral imaging and locking module also include a feature band selection unit, used to select the corresponding identification band to distinguish the target material based on the spectral reflectance characteristics corresponding to the material type of the target UAV. The feature band selection unit refers to a hardware or software module capable of dynamically adjusting or selecting specific spectral bands for data acquisition or analysis based on input information (such as the target material type). Its core function is to optimize the efficiency of spectral data acquisition and identification accuracy. Specifically, this unit can be implemented in hardware, for example, using optical devices such as tunable filter arrays, acousto-optic tunable filters (AOTF), or liquid crystal tunable filters (LCTF). These devices can accurately select or filter spectral information within a specific wavelength range according to control signals, thereby enabling the hyperspectral imaging system to acquire data only in the most relevant bands. In addition, this unit can also be implemented by integrating intelligent algorithms at the data processing front end. For example, after acquiring broadband hyperspectral data, this unit can identify the spectral feature bands most relevant to a specific material type through a preset material spectral database and machine learning model, and perform subsequent processing and analysis only on the data of these bands, thereby realizing the "selection" function at the data level.

[0070] Based on the spectral reflectance characteristics corresponding to the material type of the target UAV, appropriate identification bands are selected to distinguish the target material. This step aims to maximize the accuracy and robustness of material identification by focusing on the unique spectral fingerprint of the target material, while reducing noise and computational burden from irrelevant band data. Specifically, the system can pre-store reflectance characteristic curves of common UAV materials (such as carbon fiber, composite materials, aluminum alloys, and plastics) in different spectral bands. After the hyperspectral imaging and locking module acquires the initial spectral data of the target UAV, the feature band selection unit identifies several key bands that can most effectively distinguish the current target material based on this preset data. For example, a certain composite material may have unique absorption peaks or reflection valleys in specific short-wave infrared bands, while metallic materials have different reflectance characteristics in visible or near-infrared light. Alternatively, machine learning or deep learning models can be used to train on a large amount of spectral data of different materials, allowing the model to learn how to automatically extract the most discriminative band features from the full-spectrum data. In practical applications, when target spectral data is received, the model can dynamically indicate or select the band combination that best characterizes the target material for efficient and accurate material classification.

[0071] In some of the solutions described above in this application, a pre-loading module for magnetohydrodynamic (MHD) counter-munitions is proposed to dynamically adjust the powder loading amount of the projectile based on the target classification results. However, in its implementation, the adjustment may lack real-time performance and precision, and cannot be optimized based on the minimum magnetic powder interference threshold calculated by the collaborative control unit. This results in the counter-munition loading amount not matching the target requirements, low efficiency, or waste of resources. To address this, this application further proposes that the MHD counter-munition pre-loading module also includes a dynamic powder loading amount adjustment mechanism. This adjustment mechanism controls the electromagnetic acceleration chamber to inject a predetermined amount of conductive and magnetically conductive powder into the projectile based on the minimum magnetic powder interference threshold calculated by the collaborative control unit.

[0072] Specifically, the dynamic powder loading adjustment mechanism is a device used to precisely control the injection of conductive and magnetic powder into the projectile. The core function of this mechanism is to ensure that the powder loading amount within the projectile accurately matches the actual needs of countering the target drone. Its implementation can be varied. For example, a system consisting of a precision metering pump, a weighing sensor, and a feedback control unit can be used. The precision metering pump is responsible for accurately delivering the powder, the weighing sensor monitors the powder weight within the projectile in real time, and the feedback control unit dynamically adjusts the delivery speed or time of the metering pump according to a preset minimum magnetic powder interference threshold to ensure the powder loading amount reaches the accurate value. Another implementation method combines a variable-volume storage silo with a high-precision valve. The storage silo can adjust its internal volume according to instructions to pre-store different amounts of powder, or precisely control the powder release amount through a piston mechanism. The high-precision valve, upon receiving instructions from the cooperating control unit, precisely opens and releases a predetermined amount of conductive and magnetic powder into the projectile.

[0073] The minimum magnetic powder interference threshold is calculated by the collaborative control unit based on the target UAV's material type and motion parameters. It represents the minimum amount of conductive and magnetic powder required to disable the target UAV's motor. This threshold is a key basis for the powder loading amount dynamic adjustment mechanism to control the loading, aiming to ensure the countermeasure effect while avoiding unnecessary resource waste.

[0074] The electromagnetic acceleration chamber is an important component of the magnetohydrodynamic countermeasures ammunition pre-positioning module. Its main function is to electromagnetically accelerate a projectile loaded with conductive and magnetic powder to achieve a predetermined launch velocity. In this embodiment, the electromagnetic acceleration chamber works closely with the powder loading dynamic adjustment mechanism. After the projectile has completed powder loading, the mechanism receives and prepares the projectile for accelerated launch.

[0075] The projectile is a miniature carrier that holds conductive and magnetically conductive powder. After being injected with a predetermined amount of the powder, it is accelerated and launched by an electromagnetic acceleration chamber, and finally disintegrates and releases the powder at a predetermined interception point.

[0076] The conductive and magnetic powder consists of fine particles with both electrical and magnetic properties. These powders are released after the projectile disintegrates and are forcibly drawn into the motor gaps of the target drone using the downwash airflow generated by its rotor, causing short circuits, jamming, or performance degradation in the motor, ultimately leading to the drone losing power or control.

[0077] In some embodiments described above, a trajectory prediction and miss calculation module is proposed to calculate the trajectory and miss distance of the target UAV in real time. However, when the target UAV performs high-maneuver evasion, using a single motion model may result in insufficient prediction accuracy, affecting the system's locking performance. Therefore, this application further proposes that the trajectory prediction and miss calculation module also includes an interactive multi-model filter, used to switch between different motion models when the target UAV performs high-maneuver evasion to improve prediction accuracy.

[0078] Interactive Multi-Model Filter (IMF) is an advanced state estimation algorithm. Its core strength lies in its ability to simultaneously process multiple possible motion models and dynamically adjust the weights of each model based on the degree of matching between the model and the target's actual motion. This filter operates multiple sub-filters in parallel, each corresponding to a pre-defined motion model, such as uniform linear motion, uniform acceleration, uniform turning, or constant angular velocity. At each time step, the IMF updates the corresponding model probabilities based on the prediction residuals and covariance of each model, and uses these probabilities to weight and fuse the state estimates from each sub-filter, resulting in a more accurate and robust overall state estimate.

[0079] Specifically, the interactive multi-model filter can employ Kalman-based or extended Kalman filters as its sub-filters. For example, in one implementation, the filter first performs model interaction, that is, it mixes the state estimates and covariance matrices of each model at the previous time step according to the model transition probabilities to provide initial conditions for each sub-filter at the current time step; then, each sub-filter independently performs state prediction and measurement updates; next, it updates its corresponding model probability according to the likelihood function of each sub-filter; finally, it weights and fuses the state estimates and covariance matrices of all sub-filters according to their updated model probabilities to obtain the final system state estimate.

[0080] In another implementation, the Interactive Multi-Model Filter (IMF) can be combined with more complex nonlinear filtering techniques, such as unscented Kalman filters or particle filters, as sub-filters to handle stronger nonlinearity and non-Gaussian noise. In this way, the IMF can effectively address the complex scenarios where target UAVs frequently switch motion modes during high-maneuver evasion, avoiding the limitations of a single model in predicting complex maneuvers and significantly improving trajectory prediction accuracy. Specifically, the IMF can dynamically switch between multiple preset motion models based on the real-time motion state of the target UAV, such as switching from a uniform linear motion model to a uniform acceleration motion model or a uniform turning model. This dynamic switching mechanism ensures that the system always uses the model that best matches the target's current motion mode for trajectory prediction, thereby significantly improving the accuracy and robustness of trajectory prediction. In high-maneuver evasion scenarios, the IMF can quickly identify changes in the target's motion mode and adjust the prediction strategy in a timely manner, avoiding prediction bias caused by model mismatch. This enables the trajectory prediction and miss distance calculation modules to more accurately calculate the target UAV's trajectory and miss distance in three-dimensional space. This provides more reliable data support for the laser ranging and dynamic focusing modules to adjust the laser emitter's focal length in real time, ensuring the laser spot can always accurately lock onto the target's vital parts. Ultimately, by improving the accuracy of trajectory prediction, this application enhances the adaptability and countermeasure effectiveness of the entire high-speed UAV electro-optical tracking and locking system, especially when dealing with highly maneuverable targets, enabling more stable and accurate tracking and locking.

[0081] In some of the solutions described above in this application, a high-speed UAV photoelectric tracking and locking system was proposed for target locking and countermeasures. However, during the countermeasures process, challenges remain in accurately calculating the minimum interference threshold, dynamically adjusting countermeasures parameters, and ensuring effective powder intake into the motor gap to avoid coarse countermeasures and collateral damage. To address this, this application proposes a high-speed UAV precision countermeasures method, comprising the following steps:

[0082] First, the target drone's data and motion parameters are identified, and its critical areas are located. This data includes material data, volume data, motor power, rotational speed, and protection level. Specifically, identifying the target drone's data and motion parameters involves acquiring the drone's static attributes (such as material, volume, motor parameters, and protection level) and dynamic attributes (such as position, velocity, acceleration, and attitude) through sensors. For example, material and volume data can be acquired using a hyperspectral imaging and tracking module, combined with radar or electro-optical tracking systems to obtain motion parameters; alternatively, visible light and short-wave infrared cameras can be used to acquire the drone's external and thermal characteristics, which can be compared with a database to identify the drone model, thereby inferring its internal data such as motor power, rotational speed, and protection level. Motion parameters are acquired in real-time through multi-sensor fusion (such as radar, electro-optical, and inertial navigation). Locating its critical areas means identifying the key areas on the drone that are most vulnerable to attack and could cause it to malfunction. For example, based on image data acquired by the hyperspectral imaging and locking module, image processing and pattern recognition algorithms can be used to identify areas with specific thermal radiation and structural features, such as the connection between the drone's motor and battery compartment, as critical parts; or, a drone model library can be pre-established, containing structural diagrams and critical part information of various drones, and after identifying the target drone model, the corresponding critical part information can be retrieved from the model library for locking.

[0083] Secondly, the velocity vector of the target UAV is calculated based on its motion parameters, and the minimum magnetic particle interference threshold required to disable its motor is calculated based on the data of the target UAV. Calculating the velocity vector aims to provide accurate motion state information for subsequent projectile trajectory prediction and interception. For example, the Kalman filtering algorithm in the trajectory prediction and miss distance calculation module can be used, combined with continuous time-series position data, to estimate the three-dimensional velocity vector of the target UAV in real time; or, the radial velocity of the target UAV can be directly measured by Doppler radar, and combined with the angle information provided by the photoelectric tracking system, its velocity vector in three-dimensional space can be calculated. Calculating the minimum magnetic particle interference threshold aims to determine the minimum amount of magnetic particle required for countermeasures, avoiding excessive countermeasures and collateral damage. For example, based on data such as the target UAV's motor power, speed, and protection level, combined with a pre-established model of the correspondence between magnetic particle intake and motor failure probability, the minimum amount of magnetic particle used to achieve a motor failure probability of over 95% can be selected as the threshold; or, through simulation or experimental data, failure models of different types of UAV motors under different magnetic particle concentrations and intake amounts can be established, and the co-control unit can query this model based on the identified UAV data to determine the minimum magnetic particle interference threshold.

[0084] Next, based on the minimum magnetic particle interference threshold, the amount of conductive powder loaded into the projectile is adjusted. Electromagnetic acceleration is used to propel the projectile to a predetermined trajectory where it will encounter the target UAV. This causes the projectile to disintegrate upon reaching the predetermined interception point, releasing the conductive and magnetic powder. The downwash airflow generated by the UAV's rotor forces the powder into the motor gap. Adjusting the amount of conductive powder loaded into the projectile ensures precise countermeasures and avoids resource waste and collateral damage. For example, the dynamic powder loading adjustment mechanism in the pre-loading module of the magnetohydrodynamic countermeasure munition can inject a predetermined amount of conductive and magnetic powder into the projectile via a precision metering pump or valve, based on the minimum magnetic particle interference threshold calculated by the cooperating control unit. Alternatively, the projectile can be pre-designed as a variable-volume powder chamber, with the effective volume of the powder chamber adjusted according to instructions via a micro-piston or diaphragm mechanism, thereby controlling the loading amount. Using electromagnetic acceleration to propel the projectile to a predetermined trajectory where it will encounter the target UAV aims to provide high initial velocity and precise ballistic control to intercept high-speed maneuvering targets. For example, an electromagnetic acceleration chamber, through a multi-stage coil or railgun structure, can apply pulsed current under the precise control of a coordinating control unit to generate a powerful electromagnetic force, accelerating the projectile to a preset speed and causing it to fly along a calculated predetermined trajectory. Alternatively, the electromagnetic acceleration chamber can adjust the acceleration voltage and current waveform according to the target's speed to ensure that the relative speed of the projectile when it encounters the target at the predetermined interception point is within a preset range, improving the interception success rate. The projectile disintegrates upon reaching the predetermined interception point, releasing conductive and magnetic powder to maximize the countermeasure effect by releasing the powder at the optimal time. For example, a time-delay fuse or laser proximity fuse can be integrated inside the projectile. The coordinating control unit dynamically adjusts the fuse's delay time or trigger distance based on the target UAV's motion parameters (such as miss distance and relative speed) to ensure precise disintegration at the predetermined interception point. Alternatively, the projectile can employ a controllable explosive structure, using a miniature explosive device or thermally sensitive material to rapidly disintegrate in a predetermined airspace upon receiving a disintegration command from the coordinating control unit, uniformly releasing the powder. Utilizing the downwash airflow generated by the target UAV's rotor to forcibly draw the powder into the motor gaps ensures that the powder accurately enters key components inside the UAV, improving countermeasure efficiency. For example, after the projectile disintegrates, the powder forms a high-concentration cloud near the interception point. The powerful downwash airflow generated by the high-speed rotation of the drone's rotors engulfs this powder and forces it into internal structures such as motor cooling holes and bearing gaps. Alternatively, the powder particles are designed to have specific aerodynamic characteristics, giving them better suspension and directionality in the downwash airflow, thus allowing them to be more effectively sucked into motor gaps.

[0085] Finally, the real-time motion parameters of the target UAV are acquired to determine whether it is out of control. If it is not out of control, the countermeasures are recalculated and executed based on the real-time motion parameters. Acquiring the real-time motion parameters of the target UAV to determine whether it is out of control aims to form a closed-loop feedback mechanism, evaluate the countermeasure effect, and decide whether secondary countermeasures are needed. For example, the flight attitude changes, altitude descent rate, or infrared characteristic changes (such as abnormal motor temperature) of the target UAV can be monitored by a hyperspectral imaging module, combined with data from the trajectory prediction and miss distance calculation modules to determine whether it has lost power or is out of control; or, the cooperative control unit continuously receives data from sensors such as radar and photoelectric tracking systems to analyze whether the UAV's speed, acceleration, attitude angle, and other parameters exceed the normal flight range or whether an abnormal trajectory occurs, thereby determining whether it is out of control. If it is not out of control, the countermeasures are recalculated and executed based on the real-time motion parameters to ensure the success rate of the countermeasures and to deal with situations where the target is not completely ineffective. For example, after determining that the target is not out of control, the collaborative control unit can immediately initiate a new round of countermeasures, recalculate the latest trajectory of the target UAV, the miss distance, and the required magnetic particle interference threshold, and instruct the magnetohydrodynamic countermeasure munition pre-positioned module to launch new projectiles; or, the system can preset multiple rounds of countermeasures, and when the first round of countermeasures is ineffective, it can automatically trigger the second or even the third round of countermeasures, and dynamically adjust the countermeasures parameters based on real-time evaluation results until the target is completely out of control.

[0086] By identifying the target UAV's data and motion parameters and pinpointing its critical areas, this application ensures highly targeted countermeasures, avoiding ineffective attacks on non-critical areas. Furthermore, by calculating the target UAV's velocity vector and the minimum magnetic powder interference threshold based on its data, this application dynamically determines the minimum amount of magnetic powder required for countermeasures, effectively preventing over-countermeasures and reducing collateral damage. Based on this, the amount of conductive powder loaded into the projectile is adjusted according to the calculated minimum magnetic powder interference threshold, and electromagnetic acceleration is used to precisely accelerate the projectile to a predetermined trajectory where it will encounter the target UAV. When the projectile disintegrates and releases the conductive and magnetic powder upon reaching the predetermined interception point, the downwash airflow generated by the target UAV's rotor cleverly utilizes the powder to force it into the motor gaps, ensuring the countermeasure material precisely targets the target's critical areas, greatly improving countermeasure efficiency and success rate. Simultaneously, by continuously acquiring the target UAV's real-time motion parameters and determining whether it is out of control, this application establishes an effective closed-loop feedback mechanism. If the target drone is not completely out of control, the system can quickly recalculate and execute countermeasures based on the latest real-time motion parameters, thereby ensuring the continuity and thoroughness of the countermeasure process. This significantly improves the success rate and reliability of precise countermeasures against high-speed drones and effectively solves the problems of crude countermeasures and collateral damage.

[0087] In some of the solutions described above in this application, a minimum magnetic particle interference threshold is proposed to calculate the minimum amount of magnetic particle required to cause the motor of a target UAV to fail. However, in this process, how to accurately determine this threshold to ensure a high failure probability while avoiding excessive use of magnetic particle, thereby reducing resource waste and potential collateral damage, is a technical challenge. To address this, this application further proposes that the minimum magnetic particle interference threshold be obtained based on the following steps: establishing a model relating the amount of magnetic particle absorbed to the probability of motor failure based on the motor power, speed, and protection level of the target UAV; and selecting the minimum amount of magnetic particle required to achieve a motor failure probability of 95% or higher as the threshold.

[0088] Specifically, determining the minimum magnetic particle interference threshold first requires obtaining the target UAV's motor power, speed, and protection level. Motor power and speed are key parameters reflecting the UAV motor's operating status and performance, directly affecting its sensitivity to external interference (such as conductive and magnetic powders). These parameters can be obtained in various ways, such as by using UAV model information obtained through hyperspectral imaging and locking modules, and then querying relevant technical databases to obtain its nominal power and speed; or by performing spectral analysis on the electromagnetic radiation characteristics of the UAV motor to estimate its operating power and speed. The protection level characterizes the UAV motor's ability to resist the intrusion and influence of external environmental factors (such as dust, liquids, and electromagnetic interference). This level can be determined based on the publicly available technical parameters of the UAV model and the manufacturer's specifications, or by analyzing the UAV's structural characteristics, such as using a hyperspectral imaging module to identify the motor housing's sealing and heat dissipation hole design, to assess its anti-interference capability. A higher protection level generally means a stronger resistance to the intrusion and influence of magnetic particles.

[0089] Based on this, a model is established to correlate the amount of magnetic powder absorbed with the probability of motor failure. This model aims to quantify the relationship between the quantity (absorption amount) of conductive and magnetic powder entering the UAV motor and the likelihood (probability of failure) of causing motor malfunction. This is a crucial predictive model used to guide the formulation of countermeasures. The model can be established in two ways: one is driven by extensive experimental data. Experiments are conducted on different types, power levels, and speeds of UAV motors, applying different doses of conductive and magnetic powder under controlled conditions, and recording the amount of magnetic powder absorbed and the corresponding failure condition at the time of motor failure in each experiment. Subsequently, statistical regression analysis, machine learning algorithms (such as support vector machines and neural networks), or fuzzy logic are used to construct an empirical model that reflects the relationship between the amount of magnetic powder absorbed and the probability of motor failure. Another approach combines physical simulation with theoretical calculations. Fluid dynamics principles are used to simulate the trajectory and absorption process of powder in the downwash airflow generated by the UAV rotor, electromagnetic principles are used to analyze the impact of powder on the internal electromagnetic field of the motor, and materials science principles are used to analyze the wear or short-circuit effects of powder on the internal structure of the motor (such as bearings and windings). By using simulation tools such as finite element analysis (FEA) or computational fluid dynamics (CFD), a comprehensive physical model is established to predict the physicochemical changes inside the motor under different magnetic powder intake amounts, and then the motor failure probability is derived.

[0090] Finally, based on the established model, the minimum amount of magnetic powder used to achieve a motor failure probability of 95% or higher is selected as the threshold. The core of this step lies in optimizing the efficiency of conductive and magnetic powder usage while ensuring high countermeasure success rates, avoiding unnecessary resource waste, and reducing potential collateral damage. A failure probability of 95% or higher sets a high-reliability countermeasure standard. In practice, the minimum amount of magnetic powder absorbed when the failure probability reaches or exceeds 95% can be determined by reverse lookup or solution within the established model of the relationship between magnetic powder absorption amount and motor failure probability. If the model is presented in tabular or graphical form, the point with a failure probability of 95% can be directly found in the table or located on the curve, and the corresponding magnetic powder absorption amount can be read, ensuring that the selected amount is the minimum required to meet this high probability.

[0091] In some of the solutions described above in this application, the amount of conductive and magnetic powder loaded into the projectile is adjusted according to the minimum magnetic powder interference threshold. However, in this process, the problem of ensuring that the relative velocity of the projectile when it encounters the target UAV at the predetermined interception point is within a suitable range to effectively release the powder and allow it to be sucked into the gap of the target UAV's motor remains unresolved, which may lead to countermeasure failure or low efficiency. To address this, this application further proposes that the amount of conductive and magnetic powder loaded into the projectile is obtained by adjusting the acceleration voltage of the electromagnetic acceleration chamber according to the target's movement speed, so that the relative velocity of the projectile when it encounters the target at the predetermined interception point is within a preset range.

[0092] The loading amount of conductive and magnetic powder inside the projectile refers to the precise quantity of conductive and magnetic powder carried within the micro-projectile. This loading amount is one of the key factors affecting the countermeasure effectiveness, as it directly determines the total amount of interfering powder that can be released and absorbed into the gaps of the target UAV's motor. This can be achieved, but is not limited to: precisely controlling the injection volume of powder through a volume metering device; or monitoring the powder mass in real time through a weighing sensor to ensure the preset loading amount is reached; or using a screw feeding mechanism to precisely deliver the powder by controlling the screw's rotation speed and time. Adjusting the acceleration voltage of the electromagnetic acceleration chamber according to the target's speed refers to the system dynamically adjusting the power supply voltage of the electromagnetic acceleration chamber in the pre-positioned module of the magnetohydrodynamic countermeasure munition based on the real-time acquired speed information of the target UAV. The acceleration voltage of the electromagnetic acceleration chamber directly determines the magnitude of the electromagnetic force it generates, thus affecting the projectile's launch velocity. The implementation methods may include: a cooperative control unit calculating the required projectile exit velocity based on the target's velocity and instructing a variable voltage power supply module to adjust the output voltage to the electromagnetic acceleration chamber; or employing pulse width modulation (PWM) technology to precisely control the effective value of the acceleration voltage by adjusting the duty cycle of the electromagnetic coil, thereby achieving fine-grained control of the projectile acceleration process. Ensuring that the relative velocity between the projectile and the target at the predetermined interception point is within a preset range is a technical feature designed to guarantee that the velocity difference between the projectile and the target UAV remains within a pre-set range conducive to countermeasures at the precise spatial point of interception. A suitable relative velocity is crucial for effective projectile disintegration, sufficient powder release, and efficient absorption of the powder into the motor gaps by the target UAV's rotor downwash airflow. If the relative velocity is too high, it may lead to excessive powder dispersion or insufficient projectile disintegration; if the relative velocity is too low, it may result in insufficient powder energy release or low absorption efficiency. The implementation methods may include: the system continuously predicts the trajectory and speed of the target UAV, and calculates and adjusts the launch speed of the projectile in real time by combining the launch parameters of the projectile, so as to ensure that the preset relative speed range is reached at the predicted interception point; or, the system presets an optimal relative speed range and uses a closed-loop control algorithm to dynamically adjust the acceleration voltage of the electromagnetic acceleration chamber according to the real-time motion state of the target UAV, so that the projectile can meet this speed requirement during interception.

[0093] In some of the solutions described above in this application, projectile disintegration is proposed to release conductive and magnetic powder. However, during this process, due to the dynamic changes in the target UAV's motion parameters, such as miss distance and relative velocity, a fixed or non-dynamically adjusted fuze delay may cause the disintegration timing to mismatch with the target's motion, affecting the accuracy of powder release and thus reducing the counter-attack success rate. To address this, this application further proposes that the aforementioned projectile disintegration be controlled using a time-delay fuze or a laser proximity fuze. The fuze's delay time is dynamically adjusted according to the target UAV's motion parameters, including miss distance and relative velocity.

[0094] Specifically, the fuse used for projectile disintegration can be a time-delay fuse or a laser proximity fuse. A time-delay fuse is a device that triggers an explosion or disintegration after a preset time. This can be achieved through internal mechanical structures, such as using springs and gear sets in conjunction with the burning speed of the propellant to precisely control the delay, or through electronic delay circuits, such as RC charging / discharging circuits or microcontroller timing, to achieve more flexible and precise delay settings. A laser proximity fuse, on the other hand, is a device that determines the distance to a target by emitting a laser beam and receiving its reflected signal. When the distance reaches a preset value, it triggers disintegration. This can be achieved by using lidar principles to measure the laser's round-trip time for precise distance measurement, or by using a laser light curtain; when the target passes through the light curtain, disintegration is triggered.

[0095] The delay time of the fuze is not fixed, but rather a dynamically adjusted process calculated and set based on real-time acquired external parameters. This can be achieved through a microprocessor built into the fuze or through communication with an external collaborative control unit. For example, after receiving motion parameters, the electronic control unit inside the fuze calculates the optimal delay time using a preset algorithm model and drives internal mechanisms, such as stepper motors, to adjust the mechanical delay mechanism, or changes the parameters of the electronic delay circuit. Alternatively, the collaborative control unit can directly calculate the delay time and send the command to the fuze wirelessly or via wired means, allowing the fuze to set its parameters accordingly.

[0096] The motion parameters of the target UAV include miss distance and relative velocity. Miss distance refers to the spatial distance deviation between the projectile and the target UAV at the predetermined interception point, reflecting the degree of similarity between the projectile's trajectory and the target UAV's trajectory. Miss distance can be measured in real time by radar or electro-optical tracking systems, or estimated by predictive algorithms (such as Kalman filtering) combined with historical data. Relative velocity refers to the velocity vector difference between the projectile and the target UAV, reflecting their approach or departure rates. Relative velocity can be measured by Doppler radar or calculated from the respective velocity vectors obtained by the tracking system. The acquisition of these parameters typically relies on a trajectory prediction and miss distance calculation module within the system, which provides accurate motion data in real time.

[0097] In some of the solutions described above in this application, a critical part is proposed for locking onto the key parts of the target UAV. However, in this process, the definition of the critical part is unclear, which may lead to inaccurate locking, poor countermeasure effect, and inability to reliably identify the target's weaknesses. In this regard, this application further clarifies the specific definition of the critical part in the above methods, namely, the critical part refers to the connection between the UAV motor and the battery compartment, which exhibits specific thermal radiation characteristics and structural features in hyperspectral images.

[0098] Specifically, this critical area, the connection between the drone's motor and battery compartment, is a key intersection of the drone's power and energy systems. This connection typically contains power lines, signal lines, and structural components used to secure the motor and battery compartment, making it a weak point in the drone's functional integrity. Effective interference with this area can directly lead to a power outage or control failure, rendering the drone incapable of flight. In practical applications, this can be precisely located by analyzing the drone's structure, such as through physical structural analysis to determine if the connection is a point of mechanical stress concentration or electrical vulnerability, or by examining drone design drawings or performing a physical dissection to identify the critical node for functional failure.

[0099] This area exhibits specific thermal radiation characteristics in hyperspectral images. Thermal radiation characteristics refer to the infrared radiation of different wavelengths emitted by an object's surface due to temperature differences. Drone motors generate heat during operation, and batteries generate heat during charging and discharging. This heat accumulates or is conducted at the connection points, creating a temperature distribution different from the surrounding environment or other parts of the drone. Hyperspectral imaging technology can capture these subtle temperature differences and convert them into identifiable spectral data. For example, short-wave infrared (SWIR) or mid-wave infrared (MWIR) hyperspectral cameras can be used to capture the unique thermal radiation spectral curves generated by the heat generated at the motor and battery compartment connection points. Furthermore, by analyzing the thermal radiation intensity and distribution patterns of this area under different operating conditions (such as takeoff, cruise, and high load), a unique thermal characteristic model can be established to improve the accuracy of identification.

[0100] Simultaneously, this area also exhibits specific structural features in hyperspectral images. Structural features refer to the unique characteristics of the connection point in terms of geometry, material composition, or surface texture. For example, seams, screw holes, and areas where different materials are joined will display specific reflectance or absorption spectra in hyperspectral images in the visible or near-infrared bands. For instance, visible light (VIS) or near-infrared (NIR) hyperspectral cameras can be used to identify the spectral reflectance differences of different materials (such as metals, plastics, and composite materials) at the connection point, as well as their unique geometric contours and texture information. Alternatively, by performing spatial and spectral analysis on hyperspectral data, structural information such as edges, corners, and texture patterns of the region can be extracted and matched with a pre-defined structural template for critical areas, thereby achieving accurate identification of critical areas.

[0101] In some of the solutions described above in this application, the real-time motion parameters of the target UAV are obtained to determine whether it is out of control. However, in this process, existing monitoring methods may not be able to capture the state changes of the target UAV, such as flight attitude, rate of altitude descent, or infrared characteristics, in real time and accurately, leading to misjudgments and an inability to promptly decide whether to re-countermeasures, thereby reducing the overall efficiency and reliability of the countermeasures system. To address this, this application further proposes obtaining the real-time motion parameters of the target UAV by monitoring changes in the target UAV's flight attitude, rate of altitude descent, or infrared characteristics using a hyperspectral imaging module to determine whether it has lost power or is out of control.

[0102] Specifically, the hyperspectral imaging module is an imaging device capable of simultaneously acquiring both spatial and spectral information of a target. Its working principle involves dividing the electromagnetic spectrum into multiple narrow bands and imaging each band independently, thereby obtaining the target's reflection, absorption, or emission characteristics at different wavelengths. This module can employ pushbroom imaging, where a sensor scans along one direction while simultaneously acquiring spectral information perpendicular to the scanning direction; or it can use staring imaging, where a two-dimensional detector array acquires both spatial and spectral information in a single exposure. This module provides richer and more detailed data than traditional visible light or infrared imaging, laying the foundation for subsequent accurate assessment of the target UAV's status.

[0103] Monitoring the flight attitude changes of a target UAV aims to assess its attitude stability in space in real time, such as changes in pitch, roll, and yaw angles. Abnormal attitude changes are a direct manifestation of a UAV losing control or experiencing power failure. Specifically, attitude angles can be calculated by analyzing the relative position and angular changes of the target UAV's geometric contours or specific structural features between different frames in a hyperspectral image sequence. Alternatively, attitude calculations can be performed by analyzing the changes in the reflectance characteristics of the UAV's surface material in the hyperspectral band under different attitudes, combined with a pre-defined attitude-spectral model.

[0104] The rate of descent refers to the change in velocity of a target drone in the vertical direction, and is a key indicator for determining whether a drone has lost lift or suffered power failure. An abnormally high and continuously increasing rate of descent usually indicates that the drone is falling out of control. In practice, image data acquired by a hyperspectral imaging module, combined with triangulation principles or stereo vision technology, can be used to calculate the three-dimensional position of the target drone relative to the ground, and then deduce its rate of altitude change. Another approach is to analyze the intensity or shape changes of specific atmospheric absorption lines (such as water vapor absorption lines) in hyperspectral images, combined with atmospheric models, to estimate the altitude of the target drone and calculate its rate of descent.

[0105] Infrared characteristic changes refer to variations in the radiation characteristics of a target drone in the infrared band, primarily reflecting the temperature status of its internal components (such as motors and batteries). Abnormal temperature increases or decreases may indicate power system failure, overload, or energy depletion. In practice, a hyperspectral imaging module can capture the radiation intensity of the target drone in different infrared bands. By analyzing this intensity data, the temperature distribution and trends of the drone's surface or internal components can be identified. For example, it can monitor whether the infrared radiation intensity in the motor area increases or decreases sharply, or whether the battery compartment temperature is abnormal.

[0106] Determining whether a drone has lost power or is out of control is a crucial step in the comprehensive evaluation based on the aforementioned multi-dimensional monitoring data. This aims to promptly identify the drone's failure state so the system can decide whether further intervention is necessary. In practice, a rule-based expert system can be used, with a set of preset thresholds and logical judgment conditions. For example, if the change in flight attitude exceeds a preset threshold, the rate of altitude descent exceeds a preset threshold, and the infrared signature of the motor area shows abnormal temperature, then it is judged as out of control. Alternatively, a machine learning model can be used, taking changes in flight attitude, rate of altitude descent, and infrared signature changes as input features. A trained classifier can then classify the drone's state in real time, outputting judgments such as "normal," "power failure," or "out of control."

[0107] The following is a more specific example to illustrate the above technical solution in greater detail:

[0108] In a specific area, such as location A, a high-speed drone electro-optical tracking and locking system is in operation. When an unauthorized high-speed small drone attempts to intrude into the area at a speed exceeding 15 meters per second, accompanied by highly maneuverable evasive maneuvers, the system immediately initiates a countermeasure process.

[0109] First, the hyperspectral imaging and locking module begins operation. This module utilizes its short-wave infrared and visible light cameras to detect and image the target drone. By analyzing the acquired hyperspectral image data, the feature band selection unit selects specific identification bands based on the spectral reflectance characteristics corresponding to the target drone's material type, thereby accurately identifying the drone's material type, such as composite materials or metal alloys. Simultaneously, this module can also lock onto the target drone's critical parts; for example, based on specific thermal radiation and structural features presented in the hyperspectral image, it identifies the connection between the drone's motor and battery compartment as a critical area. This process overcomes the latency problem of traditional optoelectronic systems in high-speed target identification and locking.

[0110] Next, the trajectory prediction and miss distance calculation module receives target position and velocity information from the hyperspectral imaging and locking module. Based on the Kalman filtering algorithm and a deep learning target detection network, this module calculates the target UAV's trajectory in three-dimensional space in real time. Since the target UAV is performing high-maneuver evasion, the interactive multi-model filter (IMF) comes into play at this stage, dynamically switching between different motion models according to the UAV's real-time motion state, thus significantly improving the accuracy of trajectory prediction and avoiding the problem of missed targets common in traditional systems when facing highly maneuverable targets. Simultaneously, this module also calculates the miss distance between the countermeasure munition and the target UAV in real time.

[0111] The cooperative control unit receives real-time trajectory data and miss distance information from the trajectory prediction and miss distance calculation module. When it determines that the target UAV has entered the countermeasure range, the cooperative control unit, based on the target UAV material type identified by the hyperspectral imaging and locking module and the motion parameters (such as motor power, speed, and protection level) provided by the trajectory prediction and miss distance calculation module, begins to calculate the minimum magnetic particle interference threshold required to cause its motor to fail. This threshold is calculated based on a pre-established model of the correspondence between magnetic particle intake and motor failure probability, selecting the minimum amount of magnetic particle required to achieve a motor failure probability of over 95% as the threshold. This refined calculation avoids the environmental pollution and ineffectiveness to autonomous navigation UAVs that may be caused by traditional electromagnetic interference.

[0112] After determining the minimum magnetic particle interference threshold, the cooperative control unit sends this threshold to the magnetohydrodynamic (MHD) countermeasure munition pre-positioning module. The powder loading dynamic adjustment mechanism in the MHD countermeasure munition pre-positioning module, based on the minimum magnetic particle interference threshold calculated by the cooperative control unit, controls the electromagnetic acceleration chamber to inject a predetermined amount of conductive and magnetically conductive powder into several miniature projectiles containing conductive and magnetically conductive powder. Simultaneously, to ensure that the relative velocity of the projectiles when they encounter the target at the predetermined interception point is within a preset range, the acceleration voltage of the electromagnetic acceleration chamber is dynamically adjusted according to the target UAV's speed. This process achieves customization and precision in countermeasure munitions, rather than the crude countermeasures of traditional net-trapping techniques, which have low hit rates and are prone to failure.

[0113] Subsequently, the electromagnetic acceleration chamber accelerates the pre-loaded projectile to a predetermined trajectory where it will encounter the target UAV, and then launches it. Upon reaching the predetermined intercept point, the projectile is controlled to disintegrate using a time-delay fuse or a laser proximity fuse. The fuse's delay time is dynamically adjusted based on the target UAV's miss distance and relative velocity, ensuring the projectile disintegrates at the optimal moment, releasing conductive and magnetic powder. This powder is forcibly drawn into the motor gaps by the downwash airflow generated by the target UAV's rotor, causing motor failure and resulting in the UAV losing power or control.

[0114] After the countermeasure projectile is launched, the system does not cease operation. The hyperspectral imaging and locking module continuously monitors changes in the target UAV's flight attitude, rate of altitude descent, or infrared signature to obtain real-time motion parameters and determine if it is out of control. If the target UAV remains in control, the cooperative control unit recalculates the countermeasures based on the new real-time motion parameters and triggers the pre-set magnetohydrodynamic countermeasure munition module again for adjustment and launch, forming a closed loop of "lock-assessment-precision countermeasure." This ensures effective countermeasures against high-speed maneuvering targets and solves the problems of high cost, slow response, and difficulty in dealing with swarm attacks inherent in traditional kinetic interception systems.

[0115] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A high-speed unmanned aerial vehicle (UAV) photoelectric tracking and locking system, characterized in that, include: The hyperspectral imaging and locking module includes a short-wave infrared camera and a visible light camera, used to acquire information about the target UAV, its material type, and key parts. The trajectory prediction and miss calculation module, based on the Kalman filtering algorithm and deep learning target detection network, calculates the motion trajectory and miss amount of the target UAV in three-dimensional space in real time. The laser ranging and dynamic focusing module adjusts the focal length of the laser emitter in real time based on the target UAV's motion trajectory and miss distance in three-dimensional space, ensuring that the laser spot always locks onto the target's vital parts; The pre-positioning module for magnetohydrodynamic countermeasure munitions includes an electromagnetic acceleration chamber and several micro-projectiles filled with conductive and magnetic powder. The electromagnetic acceleration chamber dynamically adjusts the powder loading amount of the projectiles according to the target classification results. The collaborative control unit is connected to the above modules and is used to receive data from the trajectory prediction module. When it is determined that the target has entered the countermeasure range, it calculates the minimum magnetic particle interference threshold according to the target material type and motion parameters, and triggers the magnetohydrodynamic countermeasure ammunition pre-set module to adjust the loading amount before launching based on the threshold.

2. The high-speed UAV photoelectric tracking and locking system according to claim 1, characterized in that, The hyperspectral imaging and locking module also includes: The feature band selection unit is used to select the corresponding identification band to distinguish the target material based on the spectral reflectance characteristics corresponding to the material type of the target UAV.

3. The high-speed UAV photoelectric tracking and locking system according to claim 1, characterized in that, The pre-positioning module for the magnetohydrodynamic countermeasure munition also includes: A powder loading dynamic adjustment mechanism controls the electromagnetic acceleration chamber to inject a predetermined amount of conductive and magnetic powder into the projectile based on the minimum magnetic powder interference threshold calculated by the collaborative control unit.

4. The high-speed UAV photoelectric tracking and locking system according to claim 1, characterized in that, The trajectory prediction and miss distance calculation module also includes: An interactive multi-model filter is used to switch between different motion models to improve prediction accuracy when the target UAV performs high-maneuver evasion.

5. A method for precise countermeasures against high-speed unmanned aerial vehicles (UAVs) based on the system described in any one of claims 1 to 4, characterized in that, Includes the following steps: Identify the target drone's data and motion parameters to pinpoint its vital parts; the data includes material data, volume data, motor power, rotation speed, and protection level; Calculate the velocity vector of the target UAV based on its motion parameters, and simultaneously calculate the minimum magnetic particle interference threshold required to cause its motor to fail based on the data of the target UAV. According to the minimum magnetic powder interference threshold, the amount of conductive powder loaded in the projectile is adjusted, and the projectile is accelerated to a predetermined trajectory where it meets the target UAV by electromagnetic acceleration, so that the projectile disintegrates after reaching the predetermined interception point, releasing the conductive and magnetic powder. The powder is then forcibly sucked into the motor gap by the downwash airflow generated by the rotor of the target UAV. The real-time motion parameters of the target drone are obtained to determine whether it is out of control. If it is not out of control, the countermeasures are recalculated and executed based on the real-time motion parameters.

6. The high-speed unmanned aerial vehicle (UAV) precision countermeasure method according to claim 5, characterized in that, The minimum magnetic particle interference threshold is obtained based on the following steps: Based on the motor power, speed, and protection level of the target UAV, a model is established to establish the correspondence between the amount of magnetic powder absorbed and the probability of motor failure. The minimum amount of magnetic powder used to achieve a motor failure probability of over 95% is selected as the threshold.

7. The high-speed unmanned aerial vehicle (UAV) precision countermeasure method according to claim 5, characterized in that, The amount of conductive powder inside the projectile is obtained by adjusting the acceleration voltage of the electromagnetic acceleration chamber according to the target's speed, so that the relative speed of the projectile when it meets the target at the predetermined interception point is within a preset range.

8. The high-speed unmanned aerial vehicle (UAV) precision countermeasure method according to claim 5, characterized in that, The projectile disintegration is controlled by a time-delay fuse or a laser proximity fuse. The delay time of the fuse is dynamically adjusted according to the motion parameters of the target UAV, which include the miss distance and relative velocity.

9. The high-speed unmanned aerial vehicle (UAV) precision countermeasure method according to claim 5, characterized in that, The critical part refers to the connection between the drone motor and the battery compartment, which exhibits specific thermal radiation and structural features in hyperspectral images.

10. The high-speed unmanned aerial vehicle (UAV) precision countermeasure method according to claim 5, characterized in that, The acquisition of the real-time motion parameters of the target UAV refers to monitoring the flight attitude changes, altitude descent rate, or infrared feature changes of the target UAV through a hyperspectral imaging module to determine whether it has lost power or gone out of control.

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