Control method for automatically collecting and eliminating infrared characteristics of target

Abstract

The invention provides a control method for automatically collecting and eliminating target infrared characteristics, and relates to the technical field of target infrared characteristic elimination, and the method comprises the steps: obtaining real-time target and environment infrared data of a target of which the infrared characteristics are to be eliminated, carrying out the noise reduction processing, and obtaining continuous infrared spectrum data through spectrum integration; according to each environment background temperature, temperature adjustment is performed on each target surface, wavelet transform is performed on infrared spectrum data to obtain the temperature rise characteristic of the target, parameter optimization is performed on temperature adjustment, and region classification is performed on the adjusted target region to obtain a target region classification result. And carrying out smooth temperature adjustment on the classification areas exceeding the judgment threshold according to the temperature of the centers of the classification areas, calculating the phase, frequency and amplitude of an interference radiation spectrum according to the difference value between the real-time infrared radiation distribution data of the target and the environment and the position of a radiation interference source, adjusting interference waves, and eliminating the infrared characteristics of the target according to the interference principle.

Description

Technical Field

[0001] The invention relates to the technical field of eliminating target infrared characteristics, and in particular to 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 advancement of infrared detection technology, the infrared characteristics of targets have become easier to detect and identify. This poses a great challenge to traditional infrared stealth technology. Traditional stealth technology often achieves infrared feature reduction by coating infrared absorbing materials or changing the target's appearance, but these methods are easily detected by modern infrared detection equipment and may not remain effective for a long time in a dynamic environment. With the continuous improvement of infrared detection technology, the elimination of target infrared characteristics has become more complicated, requiring more precise, dynamic and adaptable control methods to cope with changes in infrared radiation under different environmental conditions.

[0003] In order to overcome these technical problems, existing technologies have begun to explore methods based on intelligent algorithms and dynamic adjustments, which achieve dynamic elimination of target infrared features by real-time acquisition and processing of target infrared radiation data, combined with technical means such as time-frequency domain feature analysis, temperature adjustment, and noise interference. However, these existing methods still face challenges: how to achieve accurate temperature adjustment and radiation feature elimination in a dynamically changing environment, how to prevent the target infrared signal from being accurately identified and countered by the detection equipment, and how to effectively avoid target exposure under different environmental conditions. None of them provide a reliable and efficient solution.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one 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 technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A control method for automatically collecting and eliminating target infrared characteristics, 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 interference radiation spectrum to eliminate the target infrared characteristics through the interference principle.

[0007] Furthermore, 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.

[0008] Furthermore, the calculation formula for obtaining continuous infrared spectrum data through spectral 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.

[0009] Furthermore, 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 of the ambient background area at the moment and the stable difference between the target surface area, For the current The actual temperature of the ambient background area at the moment and the stable difference between the target surface area, .

[0010] Furthermore, 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; 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.

[0011] Furthermore, the step of performing regional classification by clustering method is: 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.

[0012] Furthermore, the calculation formula for smoothing the 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.

[0013] Furthermore, 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.

[0014] Furthermore, the specific method of increasing the random noise of the difference coefficient 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.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains 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 spectrum 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 spectrum data, the temperature rise characteristics of the target are obtained, and the temperature adjustment parameters are optimized. The adjusted target area is regionally classified, and the temperature of the classification area exceeding the judgment threshold is smoothly adjusted according to the temperature of the center of the classification area. 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, the phase, frequency, and amplitude of the interference radiation spectrum are calculated to adjust the interference wave, and the infrared characteristics of the target are eliminated through the interference principle. The present invention can achieve efficient elimination of target infrared features by comprehensively using Kalman algorithm, time-frequency analysis, wavelet transform, clustering algorithm and other technologies. First, the scheme ensures the high quality and accuracy of infrared radiation data through precise real-time data collection and Kalman noise reduction processing. Then, through temperature regulation and regional classification optimization, the infrared features of the target are finely adjusted to avoid the risk of overall temperature exposure. Finally, through the amplitude and frequency modulation of the interference wave, the infrared features of the target are accurately interfered with by the interference principle to achieve the effect of dynamic elimination. The present invention also combines dynamic temperature regulation with time-frequency domain feature analysis, so that the scheme can adapt to different environmental conditions and adjust the infrared characteristics of the target according to real-time feedback. The application of noise interference and interference principles 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

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example

[0020] See also Figure 1 , the present invention provides a technical solution: A control method for automatically collecting and eliminating target infrared characteristics, the specific steps include: Step 1: Obtain 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.

[0021] The Kalman filter is an adaptive filtering algorithm that can effectively estimate the true state of the target from noisy observation data. It has a very high state estimation accuracy in dynamic systems, especially in target infrared radiation data, where 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. The Kalman algorithm can remove these noises through dynamic filtering and only retain the real target signal. This is crucial for subsequent analysis such as target feature extraction and temperature rise trend identification.

[0022] In this embodiment, 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.

[0023] In this embodiment, 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.

[0024] Spectral integration is to obtain continuous infrared spectrum data by integrating infrared radiation data, which can provide a detailed understanding of the infrared radiation characteristics of the target. After spectral integration, more dimensional data can be obtained, which helps to more accurately perform subsequent signal analysis and interference elimination. The acquisition of continuous infrared spectra can provide more spectral features for subsequent time-frequency analysis, help identify the temperature rise characteristics and thermal behavior of the target, and thus optimize the subsequent temperature regulation strategy.

[0025] By accurately acquiring the infrared radiation data and temperature distribution data of the target, higher quality input data can be provided for subsequent temperature adjustment and time-frequency feature analysis. This data supports the subsequent wavelet transform to achieve a more ideal effect, thereby making the temperature adjustment more accurate, optimizing the adjustment effect of the system, and reducing errors.

[0026] 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.

[0027] The target surface and the environmental background are divided into regions at a ratio of 10:1, because the target surface area is usually the focus of research, while the environmental background is usually a relatively constant area with small temperature changes. The 10:1 ratio division can ensure that the target surface area occupies a larger proportion in the analysis, so that the temperature change and heating characteristics of the target surface can be accurately captured. According to the characteristics of different regions of the target surface, a personalized temperature adjustment plan is formulated to improve the adjustment accuracy and efficiency.

[0028] Many existing technologies usually control the temperature of the target surface only through overall temperature adjustment. This method often cannot cope with dynamic changes in complex environments, and the adjustment is not precise enough, which can easily lead to thermal unevenness. This solution divides the target surface and the environmental background into multiple areas through fine regional division, and performs separate temperature adjustment according to the ambient temperature conditions of each area. This makes the temperature control more precise, and can optimize the adjustment according to the temperature requirements of each area, improving the accuracy and flexibility of the overall temperature adjustment.

[0029] The target surface and the environmental background are divided into regions, making the temperature adjustment more detailed and precise. The temperature of each region can be adjusted separately according to the ambient background temperature and the actual needs of the target surface. This method avoids the problem that after the overall temperature is adjusted in traditional technology, the overall temperature of the target remains consistent, and the overall outline of the target is identified based on the consistency of temperature.

[0030] In infrared stealth technology, the elimination of the target's infrared characteristics not only requires reducing the overall radiation intensity, but also ensuring that its infrared characteristics are highly matched with the background environment in terms of spatial distribution. If only the global temperature adjustment of the target is performed (such as overall cooling), there will be many problems. For example, the left side of the target is in the shadow area (low ambient temperature) and the right side is in the direct sunlight area (high ambient temperature). If the temperature is uniformly cooled, the temperature on the left side of the target is lower than the environment. After the right side is cooled, there is still a significant temperature difference with the left side, forming a clear outline. The ambient temperature distribution is usually non-uniform and changes over time (such as changes in light caused by cloud movement), and global adjustment cannot track local changes in real time.

[0031] In this embodiment, 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 of the ambient background area at the moment and the stable difference between the target surface area, For the current The actual temperature of the ambient background area at the moment and the stable difference between the target surface area, .

[0032] 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, and differential) 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 change of temperature, especially when facing changes in ambient temperature or target surface characteristics, it can ensure the accuracy of temperature regulation. By properly adjusting the PID parameters (such as proportional gain, integral time, and differential time), it can adapt to different working environments and target surface states, thereby improving the accuracy and robustness of control. The proportional action of PID control can adjust the response speed of the system, the integral action can eliminate static errors, and the differential 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 complex environments, to ensure that the temperature of the target is stable and close to the ambient background temperature, thereby optimizing the infrared characteristic elimination effect.

[0033] In this embodiment, 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; 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.

[0034] Through wavelet transform, the instantaneous characteristics of infrared signals can be analyzed in both the time and frequency domains, capturing the detailed information of the target radiation signal changing over time. This method has significant advantages in analyzing the temperature rise characteristics of the target and the dynamic changes of infrared radiation characteristics, and is particularly suitable for real-time adjustment in complex scenes.

[0035] By analyzing the target's temperature rise process through time-frequency characteristics, we can accurately understand the target's temperature rise speed and trend. This allows the proportional gain of the PID controller to be optimized according to the actual temperature rise characteristics, avoiding the problem of the system responding too quickly or too slowly due to excessive or too small proportional gain. Proper adjustment of the proportional gain can ensure that the system approaches the target temperature at the most appropriate speed and reduce unnecessary energy consumption.

[0036] Traditional PID control may have overshoot (target temperature exceeds the set value) or oscillation (temperature fluctuates repeatedly) problems. Through time-frequency characteristic analysis, the proportional gain can be optimized in real time to reduce the occurrence of such phenomena, making temperature regulation smoother, avoiding drastic fluctuations in target surface temperature, and improving system stability.

[0037] Different environmental backgrounds and temperature response characteristics of the target surface 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, so that the control system can quickly adapt to different environmental changes and temperature characteristics of the target surface, ensuring that the temperature regulation is more accurate and efficient.

[0038] As a common signal processing technology, wavelet transform is particularly good at analyzing the time-frequency characteristics 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 provide accurate time information when processing non-stationary signals, while wavelet transform can effectively process such complex time-frequency data.

[0039] A key advantage of wavelet transform is that it can provide local information in both time domain and frequency domain, that is, it can capture the changes of signals at different times and 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 determine the heating speed of the target, the sudden change during the heating process, the frequency fluctuation and other characteristics. This is something that traditional Fourier transform cannot do, because Fourier transform can only provide the frequency domain information of the signal and cannot effectively retain the time information.

[0040] In practical applications, the temperature change of the target is usually complex, nonlinear, and affected by many factors. Wavelet transform can accurately separate the information on different time scales when processing these complex signals, thereby revealing the temperature rise characteristics of the target. By extracting these time-frequency features, the temperature rise process of the target can be better judged and accurate data support can be provided for temperature regulation.

[0041] By extracting the time-frequency characteristics of the target radiation signal through wavelet transform, more accurate real-time data support can be provided for the entire temperature adjustment and infrared characteristic elimination process. Through the time-frequency characteristics, the temperature changes of the target during the heating process can be understood more precisely, and any changes in the heating process, such as the heating rate and the peak value, can be captured in time. This information directly affects the optimization and adjustment of parameters in the PID control system. Accurate analysis of the heating characteristics makes the temperature adjustment more in line with the actual needs of the target, thereby improving the overall infrared characteristic elimination effect.

[0042] 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. By calculating the temperature difference between adjacent classification areas and comparing it 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.

[0043] In the infrared characteristic elimination system, temperature adjustment of the target area is a complex task, because the temperature adjustment requirements of different areas may be different, and these differences are usually affected by factors such as the target surface morphology, environmental background, and thermal conductivity. Clustering 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.

[0044] The clustering method can automatically divide the target area into multiple sub-areas with similar temperature characteristics according to the actual distribution of the target area temperature after adjustment. Different sub-areas may have different temperature distributions. Through this automatic partitioning, the temperature adjustment of each area can be more in line with its actual needs, rather than relying solely on the average value of the global temperature or a rough division.

[0045] The surface characteristics of the target are usually complex, and there are areas with large temperature differences. The clustering method can automatically adjust the regional division according to the temperature change, so that more accurate temperature adjustment can be achieved in areas with different temperature characteristics. When the temperature difference between adjacent areas is large, the clustering method can identify these differences in time and make appropriate temperature smoothing adjustments to avoid unstable temperature distribution caused by too fast adjustment.

[0046] In this embodiment, the steps of performing region classification by clustering method are: 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 feature vector of the centroid, clustering is performed again until the change of all centroid positions is less than the threshold, then the clustering is considered stable and the clustering is terminated.

[0047] The core purpose of this approach is to mimic the smooth transition characteristics of temperature changes in the natural environment by calculating the temperature difference between adjacent classification areas and smoothly adjusting the temperature of the classification areas with large differences to avoid obvious temperature distribution boundaries. The temperature distribution in nature is usually continuous and gradual, and the temperature change is usually gradual without sudden and drastic fluctuations. During the artificial adjustment process, if the temperature changes too drastically, it may cause unnatural infrared characteristics or unbalanced heat distribution, which are easily identified in applications such as infrared detection and thermal imaging, affecting the concealment and function of the target surface.

[0048] By smoothing the temperature, the obvious temperature difference and temperature mutation that may occur in traditional methods are avoided, making the target's heat distribution more uniform and natural. This method can effectively simulate the temperature changes in nature, making infrared radiation more concealed and reducing the risk of detection.

[0049] 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.

[0050] 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: 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.

[0051] 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.

[0052] According to 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 accurately offsetting 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, thereby achieving the best interference effect. This method can not only improve the pertinence of the interference effect and reduce unnecessary energy waste, but also adapt to changes in the target and environment in a dynamic environment, and enhance concealment and anti-detection capabilities.

[0053] In this embodiment, 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.

[0054] 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.

[0055] According to the radiation difference value after adding noise, the amplitude characteristics of the calculated 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 are usually fixed frequency and fixed amplitude, and by introducing noise, this fixity is broken, making the infrared signal more complex and difficult to predict.

[0056] At the same time, completely eliminating the target's infrared characteristics may make it easier for detection equipment to find interference patterns. By adding noise, even if the target's infrared characteristics are effectively "eliminated", this is only partial interference, causing the eliminated infrared characteristics to present random characteristics. This "random" signal may make it impossible for infrared detection equipment to identify it as "interference" and think that the target is in the infrared blind area.

[0057] If the amplitude of the jamming wave is exactly the same, the jamming device may expose its jamming characteristics and be identified by the enemy. By adding noise to the amplitude, the jamming wave can fluctuate in amplitude, reducing the risk of being counterattacked.

[0058] In this embodiment, the specific method of adding random noise to the difference coefficient 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.

[0059] By using the principle of interference and adjusting the phase and frequency of the interference wave, the target's infrared radiation signal can be precisely interfered with. When the target's infrared signal and the interference wave's phase and frequency precisely match, the interference effect will cause the target's infrared signal and the interference signal to cancel or weaken each other, thereby eliminating the target's infrared characteristics. Compared with traditional broad-spectrum interference methods, this method has a more precise elimination effect.

[0060] The infrared characteristics of the target will change with factors such as temperature and external environment. By precisely adjusting the interference wave, a dynamic response can be made based on the target's real-time infrared radiation data and environmental data. The phase, frequency and amplitude of the interference wave can be adjusted according to the time-frequency domain characteristics of the target's infrared signal, thereby ensuring that the interference wave can adapt to changes in the target at any time and maintain an efficient concealment effect.

[0061] 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, so that the interference wave can accurately eliminate the infrared characteristics of the target while avoiding interference with the environment and other non-targets.

[0062] Temperature changes directly affect 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 the infrared characteristics of its radiation, 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's infrared signal can be effectively destroyed, making the difference between the target's radiation characteristics and the radiation of the environment more dynamic and changeable. After the target surface temperature is adjusted, the infrared characteristics of the target have changed, but this is usually not enough to completely conceal the target. At this point, by further adjusting the phase and frequency of the interference wave and using the principle of interference, the infrared characteristics of the target can be eliminated more accurately.

[0063] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0064] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0065] 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, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0066] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in 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. The 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 of the ambient background area at the moment and the stable difference between the target surface area, For the current The actual temperature of the ambient background area at the moment and the stable difference between the target surface area, .

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; 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, Adjacent classification area 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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