Multi-band optical interference system based on unmanned aerial vehicle platform and control method

Abstract

The invention relates to the technical field of unmanned aerial vehicle interference, in particular to a multi-band optical interference control method based on an unmanned aerial vehicle platform, and solves the technical problems of limited confrontation scene, easiness in ambient light interference and serious self-interference caused by single-band interference in the existing unmanned aerial vehicle countering technology. According to the invention, composite interference light covering the wavebands of 450 nm, 650 nm and 850 nm is emitted through the multispectral explosion flashing module; laser irradiation and radio frequency interference signals are detected in real time by using the threat sensing subsystem; a pulse control unit synchronous with a flight control system clock is adopted, and a phase locking trigger signal is generated according to exposure period characteristics of enemy optical equipment; self-interference suppression is realized through polarization orthogonal design and a mechanical shutter; spectrum combination and pulse parameters are dynamically adjusted based on enemy equipment types, full-spectrum confrontation of visible light and infrared equipment is achieved, and the method can be widely applied to the fields of military anti-unmanned aerial vehicle, important facility protection and privacy protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle interference technology, and particularly relates to a multi-band optical interference system based on an unmanned aerial vehicle platform and a control method. BACKGROUND

[0002] In recent years, with the rapid development of photoelectric reconnaissance, ranging and guidance technology, the enemy has deployed a large number of optical equipment of multiple types, such as CCD cameras, laser range finders, infrared detectors, etc., which pose a serious threat to our own targets. The traditional optical interference means mostly uses single-band strong light flashing or single-frequency laser jamming, and when countering new optical sensors of multiple bands and wide dynamic range, there are problems such as unstable interference effect, poor synchronization, and easy to cause self-interference of our own equipment.

[0003] Chinese patent publication No. CN119471595A discloses a method for unmanned aerial vehicle-borne reconnaissance and rapid interference, which selects the interference strategy gradually by stages, uses different interference modes, and reduces the dependence on external independent reconnaissance systems and resource scheduling, but still has the problem of unstable interference effect. Therefore, there is an urgent need for an advanced system with multi-band interference capability, intelligent parameter adjustment, real-time feedback optimization, and self-interference suppression to improve the countermeasures effect and system stability. SUMMARY

[0004] To this end, the present application provides a multi-band optical interference system based on an unmanned aerial vehicle platform and a control method to overcome the problems of unstable interference effect and easy self-interference of our own equipment in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a multi-band optical interference control method based on an unmanned aerial vehicle platform, comprising, obtaining a detection signal of an enemy optical device, performing feature extraction on the detection signal through a frequency domain feature extraction algorithm to obtain spectral sensitivity characteristics and exposure period parameters of the enemy device, wherein the spectral sensitivity characteristics at least include response sensitivities of 450nm, 650nm and 850nm bands; matching the spectral sensitivity characteristics with a preset interference band database to generate a multi-spectral interference scheme, the multi-spectral interference scheme containing band combinations, pulse frequency and duty cycle parameters; performing time domain analysis on the exposure period parameters to obtain an accurate synchronization trigger timing; controlling a multi-spectral interference device to emit a composite interference pulse based on the multi-spectral interference scheme and the accurate synchronization trigger timing; real-time acquisition of feedback signals of the enemy device, evaluation of interference effect through signal-to-noise ratio analysis, dynamic adjustment of band combinations and timing parameters of interference pulses when signal-to-noise ratio decay does not reach a threshold value; The adjusted interference parameters are fused with the status information of the flight control device. The anti-self-interference strategy is optimized through a dual mechanism of polarization filtering and mechanical shutter, and the flight control device is controlled to complete the anti-self-interference adjustment.

[0006] Furthermore, the detection signal is feature extracted using the frequency domain feature extraction algorithm:

[0007] In the formula, F For the instantaneous energy integral of the band, k For time sampling point index, i Band Index Corresponding to 450 / 650 / 850nm, The center wavelength of the characteristic band, Let be the response function of the optical system. For the exposure time window, For bandwidth, To detect signals, t It is a time variable; After feature extraction, feature vectors are constructed:

[0008] In the formula, V is a three-dimensional feature vector. v i For the first i The normalized sensitivity coefficient of the band, wavelength The maximum instantaneous intensity on, T This is the matrix transpose.

[0009] Furthermore, based on the mapping relationship between the spectral sensitivity characteristics and the preset interference band database, an optimized combination of interference parameters is generated; wherein, the spectral feature matching employs an improved SVM kernel function:

[0010] In the formula, V m For the first in the database m Template features of device classes v m,i For V m The i One portion, For kernel function, =10 -6 ; The confidence decision for device type is as follows:

[0011] In the formula, For support vector weights, For category labels, The argmax is the confidence score for the device type. Output the validated device type m and its confidence score. , N The number of support vectors, b This is a bias term.

[0012] Furthermore, the interference band database strategy is matrix A, with each row corresponding to the device type. m The preset parameters, among which,

[0013] In the formula, For band weights, The pulse frequency, This represents the duty cycle.

[0014] Based on confidence level Perform weighted optimization:

[0015] In the formula, This is the optimized interference parameter vector; Weighting coefficient The calculation is as follows:

[0016] In the formula, Vj represents any type of device in the database, and K is the Gaussian kernel function.

[0017] Furthermore, the optimized combination of interference parameters is synchronously matched with the exposure cycle parameters to obtain the precise synchronous triggering timing. Among them, enemy equipment exposure cycle Estimation via time-domain autocorrelation:

[0018] In the formula, This is the most sensitive band in the region. This refers to the nth sampling time point; Timing matching equations for precise synchronization of trigger timings Must meet:

[0019] In the formula, k ∈Z + , The sensitivity threshold for enemy equipment. For the amplitude of the interference pulse, it must meet the following requirements. , This is the additional time offset.

[0020] Furthermore, the device type identification process also includes constructing a feature space based on the spectral feature vector, calculating a similarity score for each feature dimension, and obtaining the device type identification result. Feature space construction includes starting from feature vectors. Extending to higher-dimensional discriminant space:

[0021] In the formula, W is the LDA projection matrix. The mean center of the feature vectors of all devices. d The feature dimensions after dimensionality reduction. These are the eigenvectors after dimensionality reduction; Similarity scores are calculated using a hybrid metric of Mahalanobis distance and cosine similarity.

[0022] In the formula: The Mahalanobis distance, Distance weighting factor m Let m be the reference feature vector of the m-th type of device; Equipment type decision:

[0023] In the formula, This represents the prior probability of the equipment category. The similarity score is given to the m-th type of device, where M is the total number of preset device types. The device type for the decision output.

[0024] Furthermore, the process of adjusting the band combination includes incorporating the signal-to-noise ratio analysis results into the interference effect evaluation, calculating the corrected band weight coefficients, and using the corrected band weight coefficients to dynamically adjust the multispectral interference scheme to obtain an updated interference parameter matrix, wherein... Signal-to-noise ratio evaluation modeling:

[0025] In the formula, For the first i Signal-to-noise ratio attenuation before and after band interference. The spectrum of the enemy signal before interference. The frequency spectrum of the jammed enemy signal. For the quadratic norm square transformation, the calculation window is synchronized with the enemy's exposure period; Dynamic weight adjustment based on SNR attenuation:

[0026] In the formula, The learning rate (interference factor) The signal-to-noise ratio threshold. This represents the system's maximum attenuation capability. Updated interference parameter matrix:

[0027] In the formula, , The standard deviation of SNR attenuation in the three bands. The total power of the interference system; The updated interference parameter matrix is ​​optimized using the interference effect factor as a correction factor to obtain the optimized interference scheme. The polarization compensation coefficient is then used to perform anti-self-interference processing on the optimized interference scheme to obtain the final interference command. Interference parameter optimization formula:

[0028] In the formula, Represents the Hadamard product. The relevant interference enhancement coefficient; Polarization compensation self-interference formula:

[0029] In the formula, For the final interference parameter vector, For the optimized basic interference parameters, For the roll angle of the drone, As the ideal reference angle, This is the roll angle. To calibrate the reference wavelength for the system, Δ This represents the offset between the current wavelength and the reference wavelength. The final jamming command is given by P final Generated after quantization:

[0030] in, To send control commands to the interfering light source, For the final interference parameter vector, The maximum permissible power of the light source. For quantification operations.

[0031] Furthermore, the implementation process of the anti-interference strategy includes dividing the flight control device state information according to attitude angle and position information and assigning values ​​to obtain anti-interference compensation parameters; Attitude angle compensation parameters:

[0032] In the formula, Ψ Let y be the yaw angle of the UAV, and q be the quaternion attitude vector; Position compensation parameters:

[0033] in, For a high rate of change, Current GPS coordinates For altitude, ( x 0, y 0) represents the reference point coordinates, which usually indicate the target point of the UAV; 50m is the distance constant and 100m is the altitude constant. Anti-self-interference comprehensive compensation matrix: .

[0034] Furthermore, the signal-to-noise ratio analysis results are dynamically evaluated to generate a hybrid optimization function. Based on the hybrid optimization function, the anti-self-interference parameters are jointly optimized to obtain the optimized anti-self-interference command. Signal-to-noise ratio dynamic evaluation function:

[0035] Distance weighting factor These are the weighting coefficients; Hybrid optimization function:

[0036] Final anti-interference command generation: .

[0037] The present invention also provides a multi-band optical jamming system based on an unmanned aerial vehicle (UAV) platform, including a main control computer installed inside the UAV platform, a threat detection device installed on the top of the UAV platform, a multispectral jamming device and a flight control device installed on the bottom of the UAV platform, and an own imaging system. Both the multispectral jamming device and the flight control device are electrically connected to the main control computer. The main control computer is used to control the threat sensing device to acquire the detection signals of the enemy's optical equipment, and to control the multispectral jamming device to emit composite jamming light pulses to interfere according to the generated multispectral jamming scheme; The main control computer can also control the flight control device to adjust the flight status of the UAV platform according to the optimized anti-interference strategy.

[0038] Compared with the prior art, the beneficial effects of the present invention are that, through the designed anti-self-interference processing link, polarization compensation parameters, attitude angle changes and flight control status information are integrated, an adaptive compensation matrix is ​​constructed based on pitch angle, roll angle, GPS altitude and altitude change rate, and a multi-channel redundancy protection strategy is formed in conjunction with the mechanical shutter mechanism, so as to ensure the stable operation of the image acquisition system in a strong interference environment and achieve stable anti-self-interference.

[0039] Furthermore, this invention enhances the adaptability to multiple optical devices by constructing a device identification mechanism based on a hybrid metric of prior probability and similarity. Ultimately, it generates a set of interference commands that includes band combinations, time triggering, and power distribution, enabling rapid identification, precise interference, and intelligent adaptation of enemy reconnaissance equipment. This significantly improves the system's applicability and robustness in real-world scenarios, overcoming existing technical bottlenecks such as easy avoidance of single-band strobe, fixed interference angles, and slow system response. It greatly enhances the effectiveness and deployment flexibility of optical jamming operations, demonstrating significant technological advancement, system integration, and engineering feasibility.

[0040] In particular, by integrating threat perception devices, main control computers, multi-band jamming devices, and flight control devices on a UAV platform, a multi-band optical jamming system with target recognition, adaptive control, and anti-self-interference capabilities was constructed. The system adopts a combined algorithm of improved support vector machine (SVM) and linear discriminant analysis (LDA) to extract frequency domain features and identify equipment type from the detection signals of enemy optical equipment. It extracts parameters such as response sensitivity, maximum intensity, and band center to construct a three-dimensional feature vector. Combined with a signal-to-noise ratio feedback mechanism, it dynamically optimizes the jamming band combination, pulse frequency, and duty cycle to form a highly matched, energy-efficient, and high-performance multispectral jamming scheme.

[0041] Furthermore, in terms of hardware control, this invention employs a high-precision time synchronization circuit to achieve multi-band strobe control of the blue LED array, red laser, and infrared emitter. Combined with temporal autocorrelation estimation of the exposure period and a dynamic threshold model, a precise synchronous triggering sequence is constructed to ensure that the interference pulses achieve peak superposition within the enemy equipment's imaging window, effectively improving interference efficiency. Regarding algorithm optimization, the system introduces a dynamic weight adjustment mechanism based on the signal-to-noise ratio attenuation rate, combined with the Hadamard product optimization operator and interference enhancement coefficients, to achieve real-time updating and adaptive enhancement of the interference parameter matrix. Attached Figure Description

[0042] Figure 1 This is a flowchart of the multi-band optical interference control method based on an unmanned aerial vehicle platform in this embodiment; Figure 2 This is a framework diagram of the multi-band optical jamming system based on an unmanned aerial vehicle platform in this embodiment; Figure 3This is a schematic diagram of the anti-self-interference mechanism of the UAV in this embodiment; Figure 4 This is a schematic diagram of the structure of the drone platform in this embodiment. Detailed Implementation

[0043] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0044] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0045] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0046] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0047] Please see Figure 1 The diagram shown is a flowchart of the multi-band optical interference control method based on an unmanned aerial vehicle (UAV) platform in this embodiment. This embodiment provides a multi-band optical interference control method based on an UAV platform, including... Step S1: Acquire the detection signal of the enemy optical equipment, extract features from the detection signal using a frequency domain feature extraction algorithm, and obtain the spectral sensitivity characteristics and exposure cycle parameters of the enemy equipment, wherein the spectral sensitivity characteristics include at least the response sensitivity of the 450nm, 650nm and 850nm bands. Step S2: Match the spectral sensitivity characteristics with a preset interference band database to generate a multispectral interference scheme. The multispectral interference scheme includes band combinations, pulse frequency, and duty cycle parameters. Perform time-domain analysis on the exposure cycle parameters to obtain a precise synchronization trigger timing. Step S3: Based on the multispectral jamming scheme and the precise synchronous triggering timing, control the multispectral jamming device to emit composite jamming pulses; Step S4: Real-time acquisition of feedback signals from enemy equipment; evaluation of interference effect through signal-to-noise ratio analysis; dynamic adjustment of band combination and timing parameters of interference pulses when signal-to-noise ratio attenuation does not reach the threshold. Step S5: The adjusted interference parameters are fused with the status information of the flight control device. The anti-self-interference strategy is optimized through a dual mechanism of polarization filtering and mechanical shutter, and the flight control device is controlled to complete the anti-self-interference adjustment.

[0048] Specifically, the feature extraction algorithm is a frequency domain feature extraction algorithm:

[0049] In the formula, F For the instantaneous energy integral of the band, k For time sampling point index, i Band Index Corresponding to 450 / 650 / 850nm, The center wavelength of the characteristic band, Let be the response function of the optical system. For the exposure time window, For bandwidth, To detect signals, t It is a time variable; After extracting features, feature vectors are constructed:

[0050] In the formula, V is a three-dimensional feature vector. v i For the first i The normalized sensitivity coefficient of the band, wavelength The maximum instantaneous intensity on, T This is the matrix transpose.

[0051] Specifically, based on the mapping relationship between the spectral sensitivity characteristics and the preset interference band database, an optimized combination of interference parameters is generated; Spectral feature matching employs an improved SVM kernel function:

[0052] In the formula, V m For the first in the database m Template features of device classes v m,i For V m The i One portion, For kernel function, =10 -6 ; Equipment type confidence decision:

[0053] In the formula, For support vector weights, For category labels, The argmax is the confidence score for the device type. Output the validated device type m and its confidence score. , N The number of support vectors, b This is a bias term.

[0054] Specifically, the interference band database strategy is matrix A, with each row corresponding to the device type. m The preset parameters,

[0055] In the formula, For band weights, The pulse frequency, This represents the duty cycle.

[0056] Specifically, based on confidence level Perform weighted optimization:

[0057] In the formula, This is the optimized interference parameter vector; Weighting coefficient Calculated using the following formula:

[0058] In the formula, V j For any type of device in the database, K This is the Gaussian kernel function.

[0059] Specifically, the optimized combination of interference parameters is synchronously matched with the exposure cycle parameters to obtain the precise synchronous triggering timing. Among them, enemy equipment exposure cycle Estimation via time-domain autocorrelation:

[0060] In the formula, This is the most sensitive band in the region. This refers to the nth sampling time point; Timing matching equations for precise synchronization of trigger timings Must meet:

[0061] In the formula, k ∈Z + , The sensitivity threshold for enemy equipment. For the amplitude of the interference pulse, it must meet the following requirements. , This is the additional time offset.

[0062] Specifically, the device type identification process includes constructing a feature space based on the spectral feature vector, calculating a similarity score for each feature dimension, and obtaining the device type identification result. Feature space construction includes starting from feature vectors. Extending to higher-dimensional discriminant space:

[0063] In the formula, W is the LDA projection matrix. The mean center of the feature vectors of all devices. d The feature dimensions after dimensionality reduction. These are the eigenvectors after dimensionality reduction; Similarity scores are calculated using a hybrid metric of Mahalanobis distance and cosine similarity.

[0064] In the formula: The Mahalanobis distance, Distance weighting factor m Let m be the reference feature vector of the m-th type of device; Equipment type decision:

[0065] In the formula, This represents the prior probability of the equipment category. The similarity score for the m-th type of device. M The total number of preset device types; The decision output represents the final identified device type.

[0066] Specifically, the system collects feedback signals from enemy equipment in real time, evaluates the interference effect through signal-to-noise ratio analysis, and dynamically adjusts the band combination and timing parameters of the interference pulses when the signal-to-noise ratio attenuation does not reach the threshold. The process of band combination adjustment includes: incorporating the signal-to-noise ratio analysis results into the interference effect assessment, calculating the corrected band weight coefficients; and using the corrected band weight coefficients to dynamically adjust the multispectral interference scheme to obtain the updated interference parameter matrix. Signal-to-noise ratio evaluation modeling:

[0067] In the formula, For the first i Signal-to-noise ratio attenuation before and after band interference. The spectrum of the enemy signal before interference. The frequency spectrum of the jammed enemy signal. For the quadratic norm square transformation, the calculation window is synchronized with the enemy's exposure period; Dynamic weight adjustment based on SNR attenuation:

[0068] In the formula, The learning rate (interference factor) The signal-to-noise ratio threshold. This represents the system's maximum attenuation capability. Updated interference parameter matrix:

[0069] In the formula, , The standard deviation of SNR attenuation in the three bands. This is to interfere with the total power of the system.

[0070] Specifically, the adjusted interference parameters are fused with the flight control device status information, and the anti-self-interference strategy is optimized through a dual mechanism of polarization filtering and mechanical shutter to ensure the normal operation of the imaging system.

[0071] The updated interference parameter matrix is ​​optimized using the interference effect factor as a correction factor to obtain the optimized interference scheme. The polarization compensation coefficient is then used to perform anti-self-interference processing on the optimized interference scheme to obtain the final interference command. Interference parameter optimization formula:

[0072] In the formula, Represents the Hadamard product. The relevant interference enhancement coefficient; Polarization compensation self-interference formula:

[0073] In the formula, For the final interference parameter vector, For the optimized basic interference parameters, For the roll angle of the drone, As the ideal reference angle, This is the roll angle. To calibrate the reference wavelength for the system, Δ This represents the offset between the current wavelength and the reference wavelength. The final jamming command is given by P final Generated after quantization:

[0074] in, To send control commands to the interfering light source, For the final interference parameter vector, The maximum permissible power of the light source. For quantification operations.

[0075] Specifically, the implementation process of the anti-interference strategy includes: dividing the flight control device state information according to attitude angle and position information and assigning values ​​to obtain anti-interference compensation parameters; Attitude angle compensation parameters:

[0076] In the formula, Ψ Let y be the yaw angle of the UAV, and q be the quaternion attitude vector; Position compensation parameters:

[0077] in, For a high rate of change, Current GPS coordinates For altitude, ( x 0, y 0) represents the reference point coordinates, which usually indicate the target point of the UAV; 50m is the distance constant, used to standardize the scale of exponential decay; and 100m is the altitude constant, used to adjust the period of the cosine function. Anti-self-interference comprehensive compensation matrix: .

[0078] Specifically, the signal-to-noise ratio analysis results are dynamically evaluated to generate a hybrid optimization function, and the anti-self-interference parameters are jointly optimized based on the hybrid optimization function to obtain the optimized anti-self-interference command. Signal-to-noise ratio dynamic evaluation function:

[0079] Distance weighting factor These are the weighting coefficients; Hybrid optimization function:

[0080] Final anti-interference command generation:

[0081] Where P is the vectorized representation of the parameters, and the final instruction is given by P. final Generated after quantization.

[0082] Please continue reading. Figures 2-3 As shown, this embodiment also provides a multi-band optical jamming system based on an unmanned aerial vehicle (UAV) platform, including a main control computer installed inside the UAV platform, a threat detection device installed on the top of the UAV platform, a multispectral jamming device and a flight control device installed at the bottom of the UAV platform, and an own imaging system. Both the multispectral jamming device and the flight control device are electrically connected to the main control computer; The main control computer is used to control the threat detection device to acquire the detection signals of the enemy's optical equipment, and to control the multispectral jamming device to emit composite jamming light pulses to interfere according to the generated multispectral jamming scheme; The main control computer can also control the flight control device to adjust the flight status of the UAV platform according to the optimized anti-interference strategy.

[0083] Please continue reading. Figure 4 As shown, this is a schematic diagram of the structure of the UAV platform in this embodiment, including: UAV platform 1, main control chip 10, RTK positioning device 13, IMU attitude sensor 14, blue LED array 21, infrared laser 22, infrared emitter 23, polarizing filter 27, mechanical shutter 28, optical receiver 33, and spectrum sampling module 34. The specific real-time configuration is as follows: the UAV platform 1 is equipped with a polarizing filter 27 and a mechanical shutter 28; the main control computer is the main control chip 10 integrated inside the UAV platform 1, which is used to perform feature extraction, equipment identification and interference control; the threat perception device is an optical receiver 33 and a spectrum sampling module 34 set on the top of the UAV platform 1, which can detect the exposure behavior of enemy optical devices such as CCD, CMOS and laser rangefinder in real time; the multispectral jamming device configuration includes a blue LED array 21, an infrared laser 22 and an infrared emitter 23, which cover three typical sensitive bands of 450nm, 650nm and 850nm respectively, and the pulse frequency, waveform intensity and duty cycle of all light sources can be independently adjusted; the flight control device configuration includes an RTK positioning device 13 and an IMU attitude sensor 14, which outputs roll, pitch, yaw angle and spatial position information in real time.

[0084] In summary, this embodiment provides a multi-band optical jamming system and control method based on an unmanned aerial vehicle (UAV) platform. This system can perform spectral identification based on the detection signals of enemy optical equipment and generate a matching multi-band jamming scheme. It dynamically adjusts jamming parameters through a signal-to-noise ratio feedback mechanism and introduces an anti-self-interference mechanism to ensure the normal operation of the user's image acquisition system. Compared to existing technologies, its advantages include: achieving targeted jamming based on device spectral characteristics; precise synchronization of jamming triggering with the enemy's exposure cycle for higher efficiency; support for dynamic adjustment of jamming strategies and closed-loop optimization of jamming effects; built-in anti-self-interference to improve the stability of the user's system; modular structure for easy integration into different UAV platforms; and applicability to various scenarios such as battlefield optical countermeasures, optical reconnaissance jamming, and tactical concealment.

[0085] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-band optical interference control method based on an unmanned aerial vehicle (UAV) platform, characterized in that, include, The detection signal of the enemy's optical equipment is acquired, and the detection signal is used to extract features from the detection signal through a frequency domain feature extraction algorithm to obtain the spectral sensitivity characteristics and exposure cycle parameters of the enemy equipment. The spectral sensitivity characteristics include at least the response sensitivity of the 450nm, 650nm and 850nm bands. The spectral sensitivity characteristics are matched with a preset interference band database to generate a multispectral interference scheme, which includes band combinations, pulse frequency, and duty cycle parameters; the exposure cycle parameters are then analyzed in the time domain to obtain a precise synchronization trigger timing. Based on the multispectral jamming scheme and the precise synchronous triggering timing, the multispectral jamming device is controlled to emit composite jamming pulses. Real-time acquisition of feedback signals from enemy equipment; evaluation of interference effect through signal-to-noise ratio analysis; dynamic adjustment of band combination and timing parameters of interference pulses when signal-to-noise ratio attenuation does not reach the threshold. The adjusted interference parameters are fused with the status information of the flight control device. The anti-self-interference strategy is optimized through a dual mechanism of polarization filtering and mechanical shutter, and the flight control device is controlled to complete the anti-self-interference adjustment.

2. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 1, characterized in that, The frequency domain feature extraction algorithm is used to extract features from the probe signal: In the formula, F For the instantaneous energy integral of the band, k For time sampling point index, i Band Index Corresponding to 450 / 650 / 850nm, The center wavelength of the characteristic band Let be the response function of the optical system. For the exposure time window, For bandwidth, To detect signals, t It is a time variable; After feature extraction, feature vectors are constructed: In the formula, V is a three-dimensional feature vector. v i For the first i The normalized sensitivity coefficient of the band, wavelength The maximum instantaneous intensity on, T This is the matrix transpose.

3. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 2, characterized in that, Based on the mapping relationship between the spectral sensitivity characteristics and the preset interference band database, an optimized combination of interference parameters is generated; wherein, the spectral feature matching adopts an improved SVM kernel function: In the formula, V m For the first in the database m Template features of device classes v m,i For V m The i One portion, For kernel function, =10 -6 ; The confidence decision for device type is as follows: In the formula, For support vector weights, For category labels, The argmax is the confidence score for the device type. Output the validated device type m and its confidence score. , N The number of support vectors, b This is a bias term.

4. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 3, characterized in that, The interference band database strategy is matrix A, with each row corresponding to the device type. m The preset parameters, among which, In the formula, For band weights, The pulse frequency, Duty cycle; Based on confidence level Perform weighted optimization: In the formula, This is the optimized interference parameter vector; Weighting coefficient The calculation is as follows: In the formula, Vj represents any type of device in the database, and K is the Gaussian kernel function.

5. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 4, characterized in that, The optimized combination of interference parameters is synchronously matched with the exposure cycle parameters to obtain the precise synchronous triggering timing. Among them, enemy equipment exposure cycle Estimation via time-domain autocorrelation: In the formula, This is the most sensitive band in the region. This refers to the nth sampling time point; Timing matching equations for precise synchronization of trigger timings Must meet: In the formula, k ∈Z + , The sensitivity threshold for enemy equipment. For the amplitude of the interference pulse, it must meet the following requirements. , This is the additional time offset.

6. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 5, characterized in that, The process of device type identification also includes constructing a feature space based on the spectral feature vector, calculating a similarity score for each feature dimension, and obtaining the device type identification result. Feature space construction includes starting from feature vectors. Extending to higher-dimensional discriminant space: In the formula, W is the LDA projection matrix. The mean center of the feature vectors of all devices. d The feature dimensions after dimensionality reduction. These are the eigenvectors after dimensionality reduction; Similarity scores are calculated using a hybrid metric of Mahalanobis distance and cosine similarity. In the formula: The Mahalanobis distance, Distance weighting factor m Let m be the reference feature vector of the m-th type of device; Equipment type decision: In the formula, This represents the prior probability of the equipment category. The similarity score for the m-th type of device. M The total number of preset device types; The device type for the decision output.

7. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 6, characterized in that, The process of adjusting the band combination includes incorporating the signal-to-noise ratio analysis results into the interference effect evaluation, calculating the corrected band weight coefficients, and dynamically adjusting the multispectral interference scheme using the corrected band weight coefficients to obtain an updated interference parameter matrix. Signal-to-noise ratio evaluation modeling: In the formula, For the first i Signal-to-noise ratio attenuation before and after band interference. The spectrum of the enemy signal before interference. The frequency spectrum of the jammed enemy signal. For the quadratic norm square transformation, the calculation window is synchronized with the enemy's exposure period; Dynamic weight adjustment based on SNR attenuation: In the formula, The learning rate (interference factor) The signal-to-noise ratio threshold. This represents the system's maximum attenuation capability. Updated interference parameter matrix: In the formula, , The standard deviation of SNR attenuation in the three bands. The total power of the interference system; The updated interference parameter matrix is ​​optimized using the interference effect factor as a correction factor to obtain the optimized interference scheme. The polarization compensation coefficient is then used to perform anti-self-interference processing on the optimized interference scheme to obtain the final interference command. Interference parameter optimization formula: In the formula, Represents the Hadamard product. The relevant interference enhancement coefficient; Polarization compensation self-interference formula: In the formula, For the final interference parameter vector, For the optimized basic interference parameters, For the roll angle of the drone, As the ideal reference angle, This is the roll angle. To calibrate the reference wavelength for the system, Δ This represents the offset between the current wavelength and the reference wavelength. The final jamming command is given by P final Generated after quantization: in, To send control commands to the interfering light source, For the final interference parameter vector, The maximum permissible power of the light source. For quantitative operations.

8. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 7, characterized in that, The implementation process of the anti-interference strategy includes dividing the flight control device state information according to attitude angle and position information and assigning values ​​to obtain anti-interference compensation parameters; Attitude angle compensation parameters: In the formula, ψ Let y be the yaw angle of the UAV, and q be the quaternion attitude vector; Position compensation parameters: in, For a high rate of change, Current GPS coordinates For altitude, ( x 0, y 0) represents the reference point coordinates, which usually indicate the target point of the UAV; 50m is the distance constant and 100m is the altitude constant. Anti-self-interference comprehensive compensation matrix: 。 9. The multi-band optical interference control method based on an unmanned aerial vehicle platform according to claim 8, characterized in that, The signal-to-noise ratio analysis results are dynamically evaluated to generate a hybrid optimization function. Based on the hybrid optimization function, the anti-self-interference parameters are jointly optimized to obtain the optimized anti-self-interference command. Signal-to-noise ratio dynamic evaluation function: Distance weighting factor These are the weighting coefficients; Hybrid optimization function: Final anti-interference command generation: 。 10. A multi-band optical interference system applied to the multi-band optical interference control method based on an unmanned aerial vehicle platform as described in any one of claims 1 to 9, characterized in that, This includes the main control computer located inside the drone platform, the threat detection device located at the top of the drone platform, the multispectral jamming device and flight control device located at the bottom of the drone platform, and the user's own imaging system. Both the multispectral jamming device and the flight control device are electrically connected to the main control computer. The main control computer is used to control the threat sensing device to acquire the detection signals of the enemy's optical equipment, and to control the multispectral jamming device to emit composite jamming light pulses to interfere according to the generated multispectral jamming scheme; The main control computer can also control the flight control device to adjust the flight status of the UAV platform according to the optimized anti-interference strategy.

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