A control method for automatically collecting and eliminating the infrared characteristics of a target

Abstract

The present invention provides a control method for automatically collecting and eliminating the infrared characteristics of a target, which relates to the technical field of eliminating the infrared characteristics of a target. The present invention obtains the real-time target and environmental infrared data of the target whose infrared characteristics are to be eliminated, performs noise reduction processing, and then obtains continuous infrared spectral data through spectral integration. According to each environmental background temperature, the temperature of each target surface is adjusted. By performing wavelet transform on the infrared spectral data, the heating characteristics of the target are obtained, and the temperature adjustment is optimized. The adjusted target area is classified, and the classified areas exceeding the judgment threshold are smoothly temperature-adjusted according to the temperature at the center of the classified area. According to the difference value between the real-time infrared radiation distribution data of the target and the environment, the phase, frequency, and amplitude of the interference radiation spectrum are calculated with the position of the radiation interference source to adjust the interference wave, and the infrared characteristics of the target are eliminated through the interference principle.

Description

Technical Field

[0001] The present invention relates to the technical field of target infrared characteristic elimination, and specifically provides a control method for automatically collecting and eliminating target infrared characteristics. Background Art

[0002] In the past few decades, infrared detection technology has been widely used in target monitoring. However, with the continuous progress of infrared detection technology, the infrared characteristics of targets have become increasingly easy to detect and identify. This poses a great challenge to traditional infrared stealth technology. Traditional stealth technologies often reduce infrared characteristics by coating infrared absorption materials or changing the target shape, but these methods are easily detected by modern infrared detection devices and may not maintain long-term effectiveness in dynamic environments. With the continuous improvement of infrared detection technology, the elimination of target infrared characteristics has become more complex, and more precise, dynamic, and adaptable control methods are needed to cope with infrared radiation changes under different environmental conditions.

[0003] To overcome these technical problems, existing technologies have begun to explore methods based on intelligent algorithms and dynamic regulation. By collecting and processing real-time target infrared radiation data and combining technical means such as time-frequency domain feature analysis, temperature regulation, and noise interference, dynamic elimination of target infrared characteristics is achieved. However, these existing methods still face challenges: how to achieve precise temperature regulation and radiation characteristic elimination in a dynamically changing environment, how to prevent the target infrared signal from being accurately identified and countered by detection devices, and how to effectively avoid target exposure under different environmental conditions, and no reliable and efficient solution has been provided.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a control method for automatically collecting and eliminating target infrared characteristics to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A control method for automatically collecting and eliminating target infrared characteristics, the specific steps include:

[0008] Step 1: Obtain real-time target and environmental infrared data of the target whose infrared characteristics are to be eliminated, and for the infrared radiation distribution data after noise reduction processing by the Kalman algorithm, obtain continuous infrared spectral data through spectral integration;

[0009] Step 2: Divide the target surface and the environmental background into regions. According to the actual temperature of each environmental background region, adjust the temperature of each target surface region. Perform wavelet transform on the continuous infrared spectral data to obtain the time-frequency characteristics of the target radiation signal. Judge the heating characteristics of the target based on the time-frequency characteristics, and optimize the parameters for temperature adjustment;

[0010] Step 3: According to the temperature of the adjusted target region, perform region classification by the clustering method to obtain classification regions. Calculate the temperature difference value between adjacent classification regions and compare it with the judgment threshold. For the classification regions that exceed the judgment threshold, perform smooth temperature adjustment according to the temperature at the center of the classification region;

[0011] Step 4: Obtain the real-time infrared radiation distribution data of the target after temperature adjustment, calculate the radiation difference value with the infrared radiation data of the environment, and calculate the phase and frequency of the interference radiation spectrum according to the time-frequency domain characteristics of the infrared radiation data of the target and the position of the radiation interference source;

[0012] Step 5: According to the radiation difference value after adding noise, calculate the amplitude of the interference wave, and modulate the interference wave with the phase and frequency of the interference radiation spectrum to eliminate the target infrared characteristics through the interference principle.

[0013] Further, the target infrared data includes the target infrared radiation distribution data and the target surface temperature distribution data;

[0014] The environmental infrared data includes the environmental background infrared radiation distribution data and the environmental background temperature distribution data;

[0015] The specific steps for noise reduction processing of the infrared radiation distribution data by the Kalman algorithm are as follows:

[0016] State prediction equation:

[0017]

[0018] Among them, is the state prediction at time The predicted state estimate, representing the estimated value of the infrared radiation distribution at the current moment, is the state transition matrix, is the control matrix, is the control input;

[0019] Covariance prediction equation:

[0020]

[0021] Among them, is the error covariance matrix predicted at time The predicted error covariance matrix, is the error covariance matrix at time The predicted error covariance matrix, is the process noise covariance matrix, is the transpose matrix;

[0022] Kalman gain calculation:

[0023]

[0024] wherein, is the Kalman gain, is the measurement matrix, is the measurement noise covariance matrix;

[0025] State update equation:

[0026]

[0027] wherein, is the actual measured value of the infrared radiation distribution at time , is the corrected state estimate;

[0028]

[0029] wherein, is the corrected error covariance matrix at time , is the identity matrix.

[0030] Furthermore, the calculation formula for obtaining continuous infrared spectral data through spectral integration is:

[0031]

[0032] wherein, is the infrared spectral data at time, is the infrared radiation distribution data, , are the infrared wavelength ranges for which features need to be eliminated, respectively.

[0033] Furthermore, the specific steps for adjusting the temperature of each target surface area according to the actual temperature of each environmental background area are:

[0034] Calculate the actual temperature of the environmental background area according to the environmental temperature distribution data, randomly select the actual temperature of the environmental background area as the target temperature for adjusting the target surface area, and adjust the temperature of the target surface area through PID;

[0035] Calculate the actual temperature of the environmental background area according to the environmental temperature distribution data:

[0036]

[0037] Among them, is the actual temperature of the environmental background area, is the area of the divided region, is the environmental temperature distribution data, the temperature at ;

[0038] The calculation formula for temperature adjustment of the target surface area by PID is:

[0039]

[0040] Among them, is the temperature that needs to be adjusted on the target surface, is the proportional gain, is the integral gain, is the derivative gain, is the current the difference between the actual temperature of the environmental background area at the moment and the stability of the target surface area, is the current the difference between the actual temperature of the environmental background area at the moment and the stability of the target surface area, .

[0041] Furthermore, the calculation formula for wavelet transform of the continuous infrared spectrum data is:

[0042]

[0043] Among them, is the time-frequency feature of the infrared spectrum data on the wavelet basis function at the scale and position , is the wavelet basis function, is the scaling and translation of the wavelet basis function at the scale and position , is the frequency scale parameter, is the time translation parameter, is the infrared spectrum data at the moment, is the complex conjugate of;

[0044] Among them, the calculation formula for the wavelet basis function is:

[0045]

[0046] Among them, is the central frequency of the wavelet, is the time variable;

[0047] The specific steps for optimizing the parameters of temperature regulation by judging the heating characteristics of the target through time-frequency characteristics are as follows:

[0048] The specific method for judging the heating characteristics of the target through time-frequency characteristics is as follows:

[0049]

[0050] Among them, is the heating characteristic at time is the time-frequency characteristic, are the medium-frequency and high-frequency infrared waves in the time-frequency characteristic respectively;

[0051] The specific method for optimizing the parameters of temperature regulation is as follows:

[0052]

[0053] Among them, is the optimized proportional gain at time is the initial set proportional gain, is the damping coefficient, is the heating characteristic at time

[0054] Furthermore, the steps for regional classification by the clustering method are as follows:

[0055] Take the adjusted temperature of each region as a data node, and randomly select data nodes as the initial centroids, represents the number of categories after clustering, is a positive integer, where the th initial centroid is expressed as: , represents the temperature of the th data node that is used as the initial centroid, ;

[0056] For the temperature feature vector of each region, calculate its distance to each initial centroid, and assign it to the cluster represented by the nearest centroid. The formula for calculating the distance to the initial centroid is:

[0057]

[0058] Among them, represents the distance between the th initial centroid and the th data node, The temperature of the area corresponding to a data node;

[0059] For each cluster, after each clustering is completed, recalculate the mean of all points within the cluster, and use this mean as the feature data of the new centroid. The update formula for the feature data of the centroid is:

[0060]

[0061] where, represents the number of data nodes assigned to the th centroid, represents the temperature of the area corresponding to the data nodes assigned to the th centroid, represents the temperature of the updated centroid;

[0062] According to the feature vector of the updated centroid, re - cluster until the change in the position of all centroids is less than the threshold, then consider the clustering stable and end the clustering;

[0063] The calculation method for calculating the temperature difference value between adjacent classification areas is:

[0064]

[0065] where, represents the temperature difference value between the th and the th classification areas, is the adjusted temperature of the th area in the th classification area, is the adjusted temperature of the th area in the th classification area, , respectively represent the number of areas in the th and the th classification areas, , ;

[0066] When , it is determined that the temperature difference value is too large;

[0067] where, is the judgment threshold for the temperature difference value.

[0068] Furthermore, the calculation formula for smoothly adjusting the temperature of the classification area exceeding the judgment threshold according to the temperature at the center of the classification area is:

[0069]

[0070] Among them, is the temperature after smooth adjustment of the th area in the th classification area, is the temperature after adjustment of the th area in the th classification area, is the smoothing factor, , is the temperature influence weight, is the temperature at the center of the adjacent classification area , represents the and the th classification area temperature difference value.

[0071] Furthermore, the calculation method of the radiation difference value is:

[0072]

[0073] Among them, is the time radiation difference value, is the time target time-frequency domain feature, is the time environment time-frequency domain feature;

[0074] The calculation method for adjusting the phase and frequency of the interference radiation spectrum is:

[0075]

[0076] Among them, is the phase of the interference wave, is the frequency of the interference wave, is the phase of the target infrared wave, is the frequency of the target infrared wave, is the position of the radiation interference source, is the speed of light.

[0077] Furthermore, the specific method for adding random noise to the difference coefficient is:

[0078]

[0079] Among them, is the difference coefficient after adding noise, is the difference coefficient, is a random noise signal representing time variation, has a mean of 0 and a variance of Normal distribution;

[0080] The calculation method of the amplitude of the interference wave is:

[0081]

[0082] Wherein, is the amplitude of the interference wave;

[0083] The expression form of the interference wave is:

[0084]

[0085] Wherein, is the interference wave.

[0086] Compared with the prior art, the beneficial effects of the present invention are:

[0087] The present invention obtains real-time target and environmental infrared data of the target to be eliminated of infrared characteristics, performs noise reduction processing, then obtains continuous infrared spectral data through spectral integration, adjusts the temperature of each target surface according to each environmental background temperature, performs wavelet transform on the infrared spectral data to obtain the heating characteristics of the target, optimizes the parameters for temperature adjustment, classifies the adjusted target area, smooths the temperature adjustment for the classification area exceeding the judgment threshold, and calculates the phase, frequency, and amplitude of the interference radiation spectrum to adjust the interference wave according to the difference value between the real-time infrared radiation distribution data of the target and the environment and the position of the radiation interference source, and eliminates the target infrared characteristics through the interference principle;

[0088] The present invention can efficiently eliminate the target infrared characteristics by comprehensively applying technologies such as the Kalman algorithm, time-frequency analysis, wavelet transform, and clustering algorithm. First, the scheme ensures the high quality and accuracy of the infrared radiation data through precise real-time data acquisition and Kalman noise reduction processing. Then, through temperature adjustment and regional classification optimization, the infrared characteristics of the target are finely adjusted, avoiding the risk of overall temperature exposure. Finally, through the amplitude and frequency modulation of the interference wave, the infrared characteristics of the target are precisely interfered with using the interference principle to achieve the effect of dynamic elimination;

[0089] The present invention also combines dynamic temperature adjustment with time-frequency domain feature analysis, enabling the scheme to adapt to different environmental conditions and adjust the infrared characteristics of the target according to real-time feedback. The application of noise interference and the interference principle not only improves the concealment of the elimination effect but also the concealment ability of the target in the environment. Brief Description of the Drawings

[0090] Figure 1 It is a schematic diagram of the overall method flow of the present invention. Detailed Embodiment

[0091] To make the objectives, technical solutions, and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0092] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second", and similar terms used in the present invention do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0093] Embodiment:

[0094] Please refer to Figure 1 , the present invention provides a technical solution:

[0095] A control method for automatically collecting and eliminating the infrared characteristics of a target, the specific steps include:

[0096] Step 1: Obtain the real-time target and environmental infrared data of the target whose infrared characteristics need to be eliminated, and for the infrared radiation distribution data after noise reduction processing by the Kalman algorithm, continuous infrared spectral data is obtained through spectral integration.

[0097] The Kalman filter is an adaptive filtering algorithm that can effectively estimate the true state of the target from noisy observation data. Its state estimation accuracy in dynamic systems is very high. Especially in target infrared radiation data, the target signal is usually affected by various noises (such as environmental interference, electromagnetic noise, etc.). Compared with traditional filtering methods, the Kalman algorithm can more accurately estimate the actual radiation distribution and temperature distribution of the target. At the same time, the Kalman algorithm can effectively cope with the dynamic changes of the target infrared signal and provide more accurate infrared radiation data. Through dynamic filtering, the Kalman algorithm can eliminate these noises and only retain the real target signal. This is crucial for subsequent analyses such as target feature extraction and recognition of the heating trend.

[0098] In this embodiment, the target infrared data includes target infrared radiation distribution data and target surface temperature distribution data;

[0099] The environmental infrared data includes environmental background infrared radiation distribution data and environmental background temperature distribution data;

[0100] The specific steps for noise reduction processing of infrared radiation distribution data by the Kalman algorithm are as follows:

[0101] State prediction equation:

[0102]

[0103] Among them, is the predicted state estimate at time which represents the estimated value of the infrared radiation distribution at the current time, is the state transition matrix, is the control matrix, is the control input;

[0104] Covariance prediction equation:

[0105]

[0106] Among them, is the predicted error covariance matrix at time is the predicted error covariance matrix at time is the process noise covariance matrix, is the transpose matrix;

[0107] Kalman gain calculation:

[0108]

[0109] Among them, is the Kalman gain, is the measurement matrix, is the measurement noise covariance matrix;

[0110] State update equation:

[0111]

[0112] Among them, is the actual measured value of the infrared radiation distribution at time is the corrected state estimate;

[0113]

[0114] Among them, is the corrected error covariance matrix at time

[0115] is the identity matrix.

[0115] In this embodiment, the calculation formula for obtaining continuous infrared spectral data through spectral integration is as follows:

[0116]

[0117] Wherein, is the infrared spectral data at time is the infrared radiation distribution data, , are the infrared wavelength ranges for which the characteristics need to be eliminated, respectively.

[0118] Spectral integration is to obtain continuous infrared spectral data by integrating infrared radiation data, which can help to understand the infrared radiation characteristics of the target in detail. After spectral integration, more-dimensional data can be obtained, which is helpful for more accurate subsequent signal analysis and interference elimination. The acquisition of continuous infrared spectra can provide more spectral characteristics for subsequent time-frequency analysis, helping to identify the heating characteristics and thermal behavior of the target, so as to optimize the subsequent temperature regulation strategy.

[0119] By accurately obtaining the infrared radiation data and temperature distribution data of the target, higher-quality input data can be provided for subsequent temperature regulation and time-frequency feature analysis. This data support makes the effect of subsequent wavelet transform more ideal, thus making the temperature regulation more accurate, optimizing the regulation effect of the system, and reducing errors.

[0120] Step 2: Divide the target surface and the environmental background into regions. According to the actual temperature of each environmental background region, adjust the temperature of each target surface region. According to the wavelet transform of the continuous infrared spectral data, obtain the time-frequency characteristics of the target radiation signal. Judge the heating characteristics of the target through the time-frequency characteristics, and optimize the parameters for temperature regulation.

[0121] Divide the target surface and the environmental background into regions according to a ratio of 10:1. Since the region of the target surface is usually the focus of research, while the environmental background is usually a region with relatively constant and small temperature changes. By dividing according to a ratio of 10:1, it can be ensured that the region of the target surface occupies a larger proportion in the analysis, so as to accurately capture the temperature changes and heating characteristics of the target surface. Develop a personalized temperature regulation plan according to the different regional characteristics of the target surface to improve the regulation accuracy and efficiency.

[0122] Many existing technologies usually control the temperature of the target surface only through overall temperature regulation. This method often fails to cope with the dynamic changes in complex environments, and the regulation is not precise enough, prone to thermal non-uniformity. This solution divides the target surface and the environmental background into multiple regions through fine-grained zoning, and performs separate temperature regulation for the environmental temperature conditions of each region. This makes the temperature control more refined, and can optimize the regulation according to the temperature requirements of each region, improving the accuracy and flexibility of the overall temperature regulation.

[0123] Zoning the target surface and the environmental background makes the temperature regulation more meticulous and accurate. The temperature of each region can be adjusted separately according to the temperature of the surrounding environmental background and the actual requirements of the target surface. This method avoids the situation where after the overall temperature regulation in traditional technologies, the overall temperature of the target remains the same, and the overall contour of the target is identified based on the temperature consistency.

[0124] In infrared stealth technology, the elimination of the infrared signature of the target not only requires reducing the overall radiation intensity, but also ensuring that its infrared signature is highly matched with the background environment in terms of spatial distribution. If only global temperature regulation is performed on the target (such as overall cooling), there will be many problems. For example, the left side of the target is in the shadow area (low environmental temperature), and the right side is in the direct sunlight area (high environmental temperature). If the temperature is uniformly reduced, the temperature on the left side of the target is lower than the environment, and there is still an obvious temperature difference between the right side and the left side after cooling, forming an obvious contour. The environmental temperature distribution is usually non-uniform and changes over time (such as changes in illumination caused by cloud movement), and global regulation cannot track local changes in real time.

[0125] In this embodiment, the specific steps for regulating the temperature of each target surface region according to the actual temperature of each region of the environmental background are as follows:

[0126] According to the environmental temperature distribution data, calculate the actual temperature of the environmental background region, randomly select the actual temperature of the environmental background region as the target temperature for regulating the target surface region, and regulate the temperature of the target surface region through PID;

[0127] According to the environmental temperature distribution data, calculate the actual temperature of the environmental background region:

[0128]

[0129] Among them, is the actual temperature of the environmental background region, is the area of the divided region, is the environmental temperature distribution data, and the temperature at ;

[0130] The calculation formula for regulating the temperature of the target surface region through PID is:

[0131]

[0132] Among them, is the temperature to be adjusted for the target surface, is the proportional gain, is the integral gain, is the derivative gain, is the current the stable difference between the actual temperature of the area of the environmental background at the moment and the target surface area, is the current the stable difference between the actual temperature of the area of the environmental background at the moment and the target surface area, .

[0133] PID control can respond to temperature changes in real time and keep the system running stably near the target temperature. Since the adjustment of the target surface temperature needs to consider fast response and stability, the three control actions of PID control (proportional, integral, derivative) can effectively cope with temperature fluctuations and accurately adjust the temperature of the target surface to achieve fast and accurate temperature control. The PID control system can automatically adjust the control strategy according to the temperature change. Especially when facing changes in the environmental temperature or the characteristics of the target surface, it can ensure the accuracy of temperature adjustment. By appropriately adjusting the PID parameters (such as proportional gain, integral time, derivative time), it can adapt to different working environments and target surface states, thereby improving the control accuracy and robustness. The proportional action of PID control can adjust the response speed of the system, the integral action can eliminate the static error, and the derivative action can reduce the oscillation and overshoot of the system. Through these control characteristics, PID can accurately adjust the temperature of the target surface, especially in a complex environment, ensure that the temperature of the target is stable and close to the environmental background temperature, so as to optimize the infrared characteristic elimination effect.

[0134] In this embodiment, the calculation formula for performing wavelet transform on continuous infrared spectral data is:

[0135]

[0136] Among them, is the time-frequency characteristic of the infrared spectral data at the scale and position on the wavelet basis function , is the wavelet basis function, is the scaling and translation of the wavelet basis function at the scale and position , is the frequency scale parameter, is the time translation parameter, is Infrared spectral data at a moment, is the complex conjugate of;

[0137] Among them, the calculation formula of the wavelet basis function is:

[0138]

[0139] Among them, is the central frequency of the wavelet, is the time variable;

[0140] The specific steps for optimizing the parameters of the temperature regulation by judging the heating characteristics of the target through time-frequency characteristics are as follows:

[0141] The specific method for judging the heating characteristics of the target through time-frequency characteristics is:

[0142]

[0143] Among them, is the heating characteristic at the moment, is the time-frequency characteristic, are the intermediate frequency and high-frequency infrared waves in the time-frequency characteristic respectively;

[0144] The specific method for optimizing the parameters of the temperature regulation is:

[0145]

[0146] Among them, is the optimized proportional gain at the moment, is the initially set proportional gain, is the damping coefficient, is the heating characteristic at the moment.

[0147] Through wavelet transform, the instantaneous characteristics of infrared signals can be analyzed simultaneously in the time-frequency domain, and the detailed information of the target radiation signal changing with time can be captured. This method has significant advantages for analyzing the heating characteristics of the target and the dynamic changes of infrared radiation characteristics, and is especially suitable for real-time adjustment in complex scenarios.

[0148] By analyzing the heating process of the target through time-frequency characteristics, the heating speed and trend of the target can be accurately understood. This enables the proportional gain of the PID controller to be optimized according to the actual heating characteristics, avoiding problems of too fast or too slow system response caused by too large or too small proportional gain. Appropriate adjustment of the proportional gain can ensure that the system approaches the target temperature at the most appropriate speed and reduce unnecessary energy consumption.

[0149] Traditional PID control may have problems such as overshoot (the target temperature exceeds the set value) or oscillation (the temperature fluctuates repeatedly). Through time-frequency feature analysis, the proportional gain can be optimized in real time to reduce the occurrence of this phenomenon, making the temperature regulation smoother, avoiding drastic fluctuations in the target surface temperature, and enhancing the system stability.

[0150] The temperature response characteristics of different environmental backgrounds and target surfaces will affect the behavior of the heating process. Through time-frequency feature analysis, the PID controller can dynamically adjust the proportional gain based on real-time data, enabling the control system to quickly adapt to different environmental changes and the temperature characteristics of the target surface, ensuring more accurate and efficient temperature regulation.

[0151] As a common signal processing technique, wavelet transform is particularly good at analyzing the time-frequency features of non-stationary signals. In the field of infrared radiation signal processing, the temperature change of the target and the infrared radiation signal often change with time and have frequency components. Conventional Fourier transform may not be able to provide accurate time information when dealing with non-stationary signals, while wavelet transform can effectively process this complex time-frequency data.

[0152] A key advantage of wavelet transform is its ability to provide local information in both the time domain and the frequency domain, that is, it can capture the changes of the signal at different times and different frequencies. For the infrared radiation signal of the target, wavelet transform can help accurately analyze the infrared radiation characteristics of the target during the heating process, and then judge the heating rate, mutations and frequency fluctuations during the heating process of the target. This is what the traditional Fourier transform cannot do, because the Fourier transform can only provide the frequency domain information of the signal and cannot effectively retain the time information.

[0153] In practical applications, the temperature change of the target is usually complex, non-linear, and affected by multiple factors. Wavelet transform can accurately separate the information at different time scales when dealing with these complex signals, thereby revealing the temperature increase characteristics of the target. By extracting these time-frequency features, it is possible to better judge the temperature rise process of the target and provide accurate data support for temperature regulation.

[0154] Extracting the time-frequency features of the target radiation signal through wavelet transform can provide more accurate real-time data support for the entire temperature regulation and infrared characteristic elimination process. Through time-frequency features, it is possible to more precisely understand the temperature change of the target during the heating process and timely capture any changes during the heating process, such as the heating rate, the appearance of peaks, etc. These information directly affect the optimization and adjustment of parameters in the PID control system. Precise heating characteristic analysis makes the temperature regulation more in line with the actual needs of the target, thereby enhancing the overall infrared characteristic elimination effect.

[0155] Step 3: According to the temperature of the adjusted target area, perform area classification by the clustering method to obtain classification areas. By calculating the temperature difference value between adjacent classification areas and comparing it with the judgment threshold, smooth temperature adjustment is performed on the classification areas that exceed the judgment threshold according to the temperature at the center of the classification area.

[0156] In the infrared characteristic elimination system, the temperature adjustment of the target area is a complex task because the temperature adjustment requirements of different areas may vary, and these differences are usually affected by factors such as the surface morphology of the target, the environmental background, and the heat conduction characteristics. The clustering method is a commonly used unsupervised learning method. By dividing the entire target surface into different areas according to the temperature information of the target area, it can provide an effective basis for subsequent temperature smoothing adjustment.

[0157] The clustering method can automatically divide the target area into multiple sub-areas with similar temperature characteristics according to the actual distribution of the temperature of the adjusted target area. Different sub-areas may have differences in temperature distribution. Through this automatic partitioning, the temperature adjustment of each area can be more in line with its actual needs, rather than simply relying on the average value of the global temperature or a rough division.

[0158] The surface characteristics of the target are usually complex, with areas having large temperature differences. The clustering method can automatically adjust the area division according to the temperature change, enabling more precise temperature adjustment operations within areas with different temperature characteristics. In the case where the temperature difference between adjacent areas is large, the clustering method can promptly identify these differences and perform appropriate temperature smoothing adjustment to avoid an unstable temperature distribution caused by overly rapid adjustment.

[0159] In this embodiment, the steps of performing area classification by the clustering method are as follows:

[0160] Take the adjusted temperature of each area as a data node, and randomly select data nodes as the initial centroids, representing the number of categories after clustering, being a positive integer, where the th initial centroid is represented as: , representing the th temperature of the data node serving as the initial centroid, being a positive integer, and ;

[0161] For the temperature feature vector of each area, calculate its distance to each initial centroid and assign it to the cluster represented by the nearest centroid. The formula for calculating the distance to the initial centroid is:

[0162]

[0163] Among them, represents the distance between the th initial centroid and the th data node, represents the temperature of the area corresponding to the th data node;

[0164] For each cluster, after each clustering is completed, the mean value of all points within the cluster is recalculated, and this mean value is used as the characteristic data of the new centroid. The update formula for the th centroid characteristic data is:

[0165]

[0166] Among them, represents the number of data nodes assigned to the th centroid, represents the temperature of the area corresponding to the data nodes assigned to the th centroid, represents the temperature of the updated centroid;

[0167] According to the characteristic vector of the updated centroid, clustering is performed again until the change in the position of all centroids is less than the threshold, then it is considered that the clustering is stable and the clustering ends.

[0168] By calculating the temperature difference value between adjacent classification areas and performing smooth temperature adjustment on the classification areas with larger differences, the occurrence of obvious temperature distribution boundaries is avoided. The core purpose of this approach is to mimic the smooth transition characteristics of temperature changes in the natural environment. The temperature distribution in nature is usually continuous and gradual, and the change in temperature is usually progressive and does not suddenly fluctuate violently. During the artificial adjustment process, if the temperature change is too drastic, it may cause unnatural infrared characteristics or unbalanced heat distribution, and these problems are easily recognized in applications such as infrared detection and thermal imaging, affecting the concealment and function of the target surface.

[0169] Through smooth temperature adjustment, the obvious temperature difference and temperature mutation that may occur in the traditional method are avoided, so that the thermal distribution of the target is more uniform and natural. This method can effectively simulate the temperature change in nature, making the infrared radiation more concealed and reducing the risk of being detected.

[0170] The calculation method for calculating the temperature difference value between adjacent classification areas is:

[0171]

[0172] Among them, represents the temperature difference value between the th and the th classification areas, is the temperature after adjustment for the th area in the th classification area, is the temperature after adjustment for the th area in the th classification area, , are respectively the number of areas in the th and the th classification areas; , ;

[0173] When , it is determined that the temperature difference value is too large;

[0174] Among them, is the judgment threshold of the temperature difference value.

[0175] In this embodiment, the calculation formula for smoothly adjusting the temperature of the classification area exceeding the judgment threshold according to the temperature at the center of the classification area is:

[0176]

[0177] Among them, is the temperature after smooth adjustment for the th area in the th classification area, is the temperature after adjustment for the th area in the th classification area, is the smoothing factor, , is the temperature influence weight, is the temperature at the center of the adjacent classification area , represents the temperature difference value between the th and the th classification areas.

[0178] Step 4: Obtain the real-time infrared radiation distribution data of the target after temperature adjustment, calculate the radiation difference value with the infrared radiation data of the environment, and calculate the phase and frequency of the interference radiation spectrum according to the time-frequency domain characteristics of the infrared radiation data of the target and the position of the radiation interference source.

[0179] Based on the time-frequency domain characteristics of the target and the location of the radiation interference source, the phase and frequency of the interference radiation spectrum can be accurately calculated to ensure that the phase and frequency of the interference wave match the characteristics of the target's infrared radiation, thereby precisely canceling the target's infrared characteristics through the interference principle. Calculating the phase and frequency of the interference radiation spectrum based on the time-frequency domain characteristics of the target's infrared radiation data and the location of the radiation interference source can make the interference wave more accurately match the target's infrared radiation signal, thus achieving the best interference effect. This method can not only improve the pertinence of the interference effect, reduce unnecessary energy waste, but also adapt to the changes of the target and the environment in a dynamic environment, enhancing the concealment and anti-detection ability.

[0180] In this embodiment, the calculation method of the radiation difference value is as follows:

[0181]

[0182] where is the radiation difference value at time is the time-frequency domain characteristics of the target at time is the time-frequency domain characteristics of the environment at time;

[0183] The calculation method for adjusting the phase and frequency of the interference radiation spectrum is as follows:

[0184]

[0185] where is the phase of the interference wave, is the frequency of the interference wave, is the phase of the target infrared wave, is the frequency of the target infrared wave, is the location of the radiation interference source, is the speed of light.

[0186] Step 5: Calculate the amplitude of the interference wave based on the radiation difference value after adding noise, and modulate the interference wave with the phase and frequency of the radiation spectrum to eliminate the target's infrared characteristics through the interference principle.

[0187] Based on the radiation difference value after adding noise, the calculated amplitude characteristics of the interference wave become more unstable and random. This randomness can prevent the interference signal from being accurately identified and predicted by the enemy or detection equipment. Because traditional interference waves usually have a fixed frequency and fixed amplitude, and by introducing noise, this fixity is broken, making the infrared signal more complex and difficult to predict.

[0188] Completely eliminating the target's infrared characteristics simultaneously may lead to the detection device easily discovering the pattern of interference. By adding noise, even if the infrared characteristics of the target are effectively "eliminated", this is only partial interference, resulting in the infrared characteristics after elimination showing random characteristics. This "random" signal may instead cause the infrared detection device to be unable to recognize it as "interference" and consider the target to be in the infrared blind area.

[0189] If the amplitudes of the interference waves are exactly the same, the interference device may expose its interference characteristics and be recognized by the enemy. By adding noise to the amplitude, the interference waves can show fluctuations in amplitude, reducing the risk of being countered.

[0190] In this embodiment, the specific method of adding random noise to the difference coefficient is as follows:

[0191]

[0192] Among them, is the difference coefficient after adding noise, is the difference coefficient, is a random noise signal representing changes over time, is a normal distribution with a mean of 0 and a variance of ;

[0193] The calculation method of the amplitude of the interference wave is as follows:

[0194]

[0195] Among them, is the amplitude of the interference wave;

[0196] The expression form of the interference wave is as follows:

[0197]

[0198] Among them, is the interference wave.

[0199] Utilizing the interference principle, by adjusting the phase and frequency of the interference wave, precise interference of the target infrared radiation signal can be achieved. When the infrared signal of the target is precisely matched with the phase and frequency of the interference wave, the interference effect will cause the infrared signal of the target and the interference signal to cancel or weaken each other, thereby achieving the elimination of the target's infrared characteristics. Compared with the traditional broad-spectrum interference method, the elimination effect of this method is more precise.

[0200] The infrared characteristics of the target change with factors such as temperature and the external environment. By precisely adjusting the interference wave, dynamic response can be made according to the real-time infrared radiation data and environmental data of the target. The phase, frequency, and amplitude of the interference wave can be adjusted according to the time-frequency domain characteristics of the target infrared signal, so as to ensure that the interference wave can adapt to the changes of the target at any time and maintain an efficient concealment effect.

[0201] Using the interference principle to adjust the phase and frequency of the interference wave can effectively suppress the influence of external noise and other interference sources on the target infrared signal. The interference effect can counteract the influence of environmental noise, enemy interference sources, etc. on the target infrared radiation, enabling the interference wave to precisely eliminate the infrared characteristics of the target while avoiding interference to the environment and other non-targets.

[0202] Temperature change directly affects the blackbody radiation characteristics of an object. According to the Stefan-Boltzmann law, the higher the temperature of the target, the greater its radiation intensity, and the wavelength distribution of the radiation will also change. Therefore, adjusting the surface temperature of the target can change its infrared radiation characteristics, making its infrared characteristics not easily captured by fixed detectors or detection systems. By changing the surface temperature of the target, the regularity and characteristics of the target infrared signal can be effectively destroyed, making the radiation difference between the target and the environment more dynamic and variable. After the surface temperature of the target is adjusted, the infrared characteristics of the target have changed, but this is usually not enough to completely conceal the target. At this time, by further adjusting the phase and frequency of the interference wave and using the interference principle, the infrared characteristics of the target can be more precisely eliminated.

[0203] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0204] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by the combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0205] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0206] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A control method for automatically collecting and eliminating target infrared characteristics, characterized in that: The specific steps include: Step 1: Acquire real-time target and environmental infrared data of the target whose infrared characteristics are to be eliminated, and obtain continuous infrared spectrum data by spectral integration of the infrared radiation distribution data after denoising by the Kalman algorithm; Step 2: Divide the target surface and the environmental background into regions, adjust the temperature of each target surface region according to the actual temperature of each environmental background region, obtain the time-frequency characteristics of the target radiation signal by wavelet transforming the continuous infrared spectrum data, judge the temperature rise characteristics of the target by the time-frequency characteristics, and optimize the temperature adjustment parameters; Step 3: Based on the adjusted temperature of the target area, the clustering method is used to classify the area to obtain the classification area. The temperature difference between adjacent classification areas is calculated and compared with the judgment threshold. The classification area that exceeds the judgment threshold is smoothly adjusted according to the temperature of the center of the classification area. Step 4: Obtain the real-time infrared radiation distribution data of the temperature-adjusted target, calculate the radiation difference value with the infrared radiation data of the environment, and calculate the phase and frequency of the interference radiation spectrum according to the time-frequency domain characteristics of the target's infrared radiation data and the position of the radiation interference source; Step 5: Calculate the amplitude of the interference wave based on the radiation difference value after adding noise, and modulate the interference wave with the phase and frequency of the radiation spectrum to eliminate the target infrared characteristics through the interference principle.

2. A control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The target infrared data includes target infrared radiation distribution data and target surface temperature distribution data; Environmental infrared data include environmental background infrared radiation distribution data and environmental background temperature distribution data; The specific steps of using the Kalman algorithm to reduce the noise of infrared radiation distribution data are as follows: State prediction equation: in, For the moment The predicted state estimate represents the estimated value of the infrared radiation distribution at the current moment. is the state transfer matrix, is the control matrix, is the control input; Covariance prediction equation: in, It's time The predicted error covariance matrix, It's time The predicted error covariance matrix, is the process noise covariance matrix, is the transposed matrix; Kalman gain calculation: in, is the Kalman gain, is the measurement matrix, is the measurement noise covariance matrix; State update equation: in, For the moment The actual measured value of infrared radiation distribution, is the revised state estimate; in, For the moment The corrected error covariance matrix, is the identity matrix.

3. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The calculation formula for obtaining continuous infrared spectrum data through spectrum integration is: in, for Infrared spectrum data at the moment, is the infrared radiation distribution data, , are the infrared wavelength ranges whose features need to be eliminated.

4. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The specific steps of adjusting the temperature of each target surface area according to the actual temperature of each environmental background area are: According to the ambient temperature distribution data, the actual temperature of the ambient background area is calculated, the actual temperature of the ambient background area is randomly selected as the target temperature for adjusting the target surface area, and the temperature of the target surface area is adjusted through PID; According to the ambient temperature distribution data, calculate the actual temperature of the ambient background area: in, is the actual temperature of the ambient background area, is the area of ​​the divided region, is the ambient temperature distribution data, The temperature at The calculation formula for temperature regulation of the target surface area through PID is: in, The temperature of the target surface needs to be adjusted. is the proportional gain, is the integral gain, is the differential gain, For the current The actual temperature difference between the ambient background area and the target surface area at the moment, For the current The actual temperature difference between the ambient background area and the target surface area at the moment, .

5. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The calculation formula for wavelet transform of continuous infrared spectrum data is: in, For infrared spectral data in wavelet basis function The scale on and location The time-frequency characteristics of is the wavelet basis function, is the wavelet basis function at scale and location Zoom and pan under is the frequency scale parameter, is the time shift parameter, for Infrared spectrum data at the moment, for The complex conjugate of Among them, the calculation formula of wavelet basis function is: in, is the center frequency of the wavelet, is a time variable; The specific steps of judging the temperature rise characteristics of the target by using the time-frequency characteristics and optimizing the parameters for temperature regulation are as follows: The specific method of judging the temperature rise characteristics of the target by time-frequency characteristics is: in, for The temperature rise characteristics of the moment, is the time-frequency feature, They are the medium-frequency and high-frequency infrared waves in the time-frequency characteristics; The specific method for optimizing the parameters of temperature regulation is: in, for The proportional gain after optimization at all times, is the initial setting proportional gain, is the damping coefficient, for The heating characteristics of the time.

6. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The steps of performing regional classification by clustering method are as follows: The adjusted temperature of each area is used as a data node and randomly selected data nodes as the initial centroid, represents the number of categories after clustering, is a positive integer, where The initial centroid is expressed as: , Indicates The temperature of the data node as the initial centroid, is a positive integer, and ; For each region’s temperature feature vector, calculate its distance to each initial centroid and assign it to the cluster represented by the nearest centroid. The distance to the initial centroid is calculated based on the formula: in, Indicates The initial centroid and The distance between data nodes, Indicates The temperature of the area corresponding to each data node; For each cluster, after each clustering is completed, the mean of all points in the cluster is recalculated and used as the feature data of the new centroid. The update formula of the centroid feature data is: in, Indicates that the The number of data nodes of the centroid, Indicates that the The temperature of the area corresponding to the data node of the centroid, represents the temperature of the updated centroid; Based on the updated centroid feature vector, clustering is performed again until the change in the position of all centroids is less than the threshold, then the clustering is considered stable and the clustering is terminated; The calculation method for calculating the temperature difference value between adjacent classification areas is: in, Indicates With The temperature difference between the classification areas is For the In the classification area The temperature of each area is adjusted. For the In the classification area The temperature of each area is adjusted. , Respectively With The number of regions in a classification zone, , ; when When , it is judged that the temperature difference value is too large; in, is the judgment threshold of the temperature difference value.

7. A control method for automatically collecting and eliminating target infrared characteristics according to claim 6, characterized in that: The calculation formula for smooth temperature adjustment of the classification area exceeding the judgment threshold according to the temperature at the center of the classification area is: in, For the In the classification area The temperature of each area is smoothly adjusted. For the In the classification area The temperature of each area is adjusted. is the smoothing factor, , is the temperature influence weight, For adjacent classification areas The temperature at the center, Indicates With The temperature difference between the classification areas.

8. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The calculation method of the radiation difference value is: in, for The radiation difference value at time, for The time-frequency domain characteristics of the target at the moment, for The time-frequency domain characteristics of the environment at the moment; The calculation method for adjusting the phase and frequency of the interference radiation spectrum is: in, is the phase of the interference wave, is the frequency of the interference wave, is the phase of the target infrared wave, is the frequency of the target infrared wave, is the location of the radiation interference source, The speed of light.

9. The control method for automatically collecting and eliminating target infrared characteristics according to claim 1, characterized in that: The specific method of increasing random noise by the coefficient of variation is: in, is the coefficient of difference after adding noise, is the coefficient of variation, is a random noise signal that varies with time. The mean is 0 and the variance is Normal distribution of The amplitude of the interference wave is calculated as: in, is the amplitude of the interference wave; The interference wave is expressed as: in, For interference waves.

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