Anti-detection camouflage method based on missile-borne terminal guidance characteristics

Abstract

The invention provides an anti-detection camouflage method based on missile-borne terminal guidance characteristics, and relates to the technical field of anti-detection camouflage. The invention constructs a characteristic data set and establishes a reflection characteristic calculation model through reflection characteristic parameters of radar emission waves with different emission characteristics under different environmental data, emission angles and distances, obtains the terminal guidance characteristics of the missile, and after pre-processing according to the emission angle, distance and environmental data of the missile radar, predicts the reflection characteristic parameters through the model, modulates the radar interference wave and the reflection wave with the same reflection characteristic parameters to generate an interference blind area, controls the position and size of the interference blind area, covers the position of the missile, calculates the blinding index of the missile in the time period by calculating the fluctuation coefficient of the missile's running trajectory and the change coefficient of the landing area after interference, judges whether the attack target is lost, and chooses whether to release a lure signal.

Description

Technical Field

[0001] The invention relates to the technical field of anti-detection camouflage, and in particular to an anti-detection camouflage method based on missile-borne terminal guidance characteristics. Background Art

[0002] On the modern battlefield, the confrontation between missiles and target objects increasingly relies on high-precision terminal guidance technology. Radar-guided missiles use the radar characteristics of the target's reflected waves to carry out precise terminal attacks. However, with the development of electronic warfare, anti-missile technology has gradually introduced active electronic interference (AEI), which aims to confuse or deviate missiles from their targets by interfering with radar signals. Especially in the terminal guidance stage, the radar reflection waves of missiles to target objects will be affected by multiple factors such as the environment, distance, and angle, making the radar signals easily captured by the enemy's jamming system and misleading. In this environment, the improvement of camouflage targets and anti-interference capabilities has become the key to improving target survivability and missile interception success rate.

[0003] Current radar-guided missile systems usually rely on traditional jamming and camouflage methods, such as using noise jamming or false target signals for jamming. However, these methods often lack accurate simulation of specific radar reflection characteristics and cannot provide all-round anti-jamming protection in a changing battlefield environment. Especially when facing complex backgrounds and camouflaged targets, existing defense methods are insufficient and vulnerable to enemy countermeasures. Therefore, how to effectively use radar reflection characteristics and real-time environmental data to design camouflage and jamming technologies that can effectively counter enemy missile guidance systems has become a technical problem that needs to be solved urgently.

[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 an anti-detection camouflage method based on missile-borne terminal guidance characteristics to solve the problems raised in the above-mentioned background technology.

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

[0007] An anti-detection camouflage method based on missile-borne terminal guidance characteristics, the specific steps include:

[0008] Step 1: Detect the camouflaged target object through radar transmission waves, build a feature data set, establish a reflection feature calculation model, and train the model;

[0009] Step 2: According to the radar signal received at the end of the missile, time domain and frequency domain analysis are performed to obtain the terminal guidance characteristics of the missile, and at the same time, the relative position of the missile and the target object is captured, the launch angle and distance of the missile radar are obtained, and after preprocessing with the real-time environmental data, the reflection characteristic parameters are input into the reflection parameter calculation model to predict.

[0010] Step 3: According to the reflection characteristic parameters, a radar interference wave with interference characteristic parameters identical to the reflection characteristic parameters is modulated to generate an interference blind zone with the reflection wave. According to the relative position of the missile and the target object and the control of the position and size of the interference blind zone, the position of the missile is covered with a blind zone.

[0011] Step 4: Obtain the continuous coordinate position of the missile, obtain the discrete coordinate position at the same time interval, calculate the missile's trajectory fluctuation coefficient and the change coefficient of the landing area after interference, calculate the missile's blinding index in this time period, and determine whether the target object is lost based on the blinding index, and choose whether to release the lure signal;

[0012] Step 5: In a safe area far away from the target object to be camouflaged, modulate the decoy signal wave identical to the reflected wave based on the reflection characteristics to further lure and camouflage the missile.

[0013] The method for constructing the feature data set is: using radars with different emission characteristics to detect camouflaged target objects from different angles and distances under different environmental data, obtaining reflection characteristic parameters of corresponding radar reflection waves, and constructing the feature data set in one-to-one correspondence with the radar emission characteristics, environmental data, distance, and angle;

[0014] Furthermore, the emission characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0015] The environmental data include temperature, humidity, wind speed and particle concentration;

[0016] The reflection characteristic parameters include reflection amplitude, reflection frequency, reflection phase, reflection direction, scattering characteristics, time delay, and polarization characteristics of the reflected wave.

[0017] Furthermore, the specific steps of establishing the reflection feature calculation model and training the model are:

[0018] The reflection parameter calculation model is based on a CNN neural network, which specifically includes a convolution layer, an activation layer, a pooling layer, and an output layer:

[0019] Convolution layer: extracts features from the input feature data set through convolution operation. The calculation formula is:

[0020]

[0021] in, For the emission characteristics, environmental data, distance, and angle in the input feature data set, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column;

[0022] The activation layer is calculated as:

[0023]

[0024] in, The coordinates of the output matrix of the convolutional layer output;

[0025] The calculation formula of the pooling layer is:

[0026]

[0027] in, is the input matrix, is the maximum pooling output;

[0028] The calculation formula of the output layer is:

[0029]

[0030] in, is the output of the pooling layer, is the reflection characteristic parameter, is the weight, is the bias parameter;

[0031] The feature data set is preprocessed, and the emission features, environmental data, distance, and angle in the feature data set are used as the input of the model, and the reflection feature parameters are used as the output to train the model.

[0032] Furthermore, the method for preprocessing the feature data set is to process it through maximum normalization, and the calculation formula is:

[0033]

[0034] in, The data after normalization of each data type of the feature dataset, is the largest number of each data type in the feature dataset, is the smallest number of each data type in the feature data set. For each data type in the feature dataset data, is the number of data in each data type of the feature dataset.

[0035] Furthermore, the missile terminal guidance characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0036] The specific steps of performing time domain and frequency domain analysis to obtain the terminal guidance characteristics of the missile are:

[0037] The waveform of the observed signal is analyzed in the time domain to extract the amplitude, frequency, pulse duration, and phase;

[0038] Frequency domain analysis obtains the spectrum of the signal through fast Fourier transform. According to the spectrum characteristics of spectrum analysis, the specific steps of obtaining the spectrum of the signal through fast Fourier transform are as follows:

[0039] The received radar signal at the end of the missile is sampled at the same time interval to obtain the discrete signal of the radar signal at the end of the missile, and the sampled discrete signal is fast Fourier transformed. The calculation formula of fast Fourier transform is:

[0040]

[0041] in, The radar signal is the first Quantity, is the time domain signal of the radar signal samples, is the total number of samples, is an imaginary unit, Represents the frequency domain frequency components, .

[0042] Furthermore, the interference characteristic parameters include: amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0043] The specific steps of adjusting the interference characteristic parameters of the interference wave and the reflected wave to produce the interference blind area are as follows:

[0044] The calculation formulas for interference wave and reflected wave are:

[0045]

[0046] in, is the interference signal, is the reflected signal, , are the wavelengths of the interference signal and the reflected signal, respectively. are the frequencies of the interference signal and the reflected signal, respectively. , are the initial phases of the interference signal and the reflected signal, , are the amplitudes of the interference signal and the reflected signal respectively;

[0047] The waveform after interference is:

[0048] ；

[0049] The coordinate position of the interference blind area after interference:

[0050] ;

[0051] when Generate interference blind area:

[0052]

[0053] in, For interference blind area The coordinate position at the time, is the wavelength after interference, is the frequency of the wave after interference, is a positive integer;

[0054] Combined with the position of the missile, a dynamic blind spot model is constructed:

[0055]

[0056] in, For interference waves The phase of the moment, is the phase of the reflected wave, is the frequency of the wave after interference, The missiles tested The location at the moment, is a positive integer used to represent the periodic changes of the wave.

[0057] Furthermore, the calculation steps of the missile's trajectory fluctuation coefficient are as follows:

[0058] According to the historical coordinate position, the historical running trajectory is calculated, and the calculation formula is:

[0059]

[0060] in, For The missile coordinate position detected at any moment, For The missile coordinate position detected at any moment, For The predicted coordinate position of the missile at time;

[0061] The running trajectory fluctuation coefficient is calculated based on the predicted coordinate position and the actual detected coordinate position. The calculation formula is:

[0062]

[0063] in, is the missile’s trajectory fluctuation coefficient, For The missile's trajectory fluctuation coefficient at any moment, For missiles The actual coordinate position is detected at all times. The continuous observation time of the missile.

[0064] Furthermore, the calculation formula of the coefficient of variation of the landing area is:

[0065]

[0066] in, is the coefficient of variation of the landing area, is the number of trajectory equations, is the continuous observation time interval of the missile, Is the number of whether the target object is on the missile's trajectory.

[0067] Furthermore, the calculation formula of the blinding index is:

[0068]

[0069] in, is the blinding index, are the weights of the running trajectory fluctuation coefficient and the landing area variation coefficient, , is the missile’s trajectory fluctuation coefficient, is the coefficient of variation of the landing area;

[0070] when When the missile is judged to have lost the signal of attacking the target object, no decoy signal will be released;

[0071] when When the missile is judged to be still attacking the target object, a decoy signal is released;

[0072] in, The blinding index threshold for determining when a missile loses the signal of attacking a target object.

[0073] Compared with the prior art, the invention has the following beneficial effects: the invention constructs a characteristic data set and establishes a reflection characteristic calculation model through reflection characteristic parameters of radar emission waves with different emission characteristics under different environmental data, emission angles and distances, obtains the terminal guidance characteristics of the missile, and after preprocessing according to the emission angle, distance and environmental data of the missile radar, predicts the reflection characteristic parameters through the model, modulates the radar interference wave and the reflection wave with the same reflection characteristic parameters to generate an interference blind area, controls the position and size of the interference blind area, covers the position of the missile, calculates the missile's blinding index in the time period by calculating the fluctuation coefficient of the missile's running trajectory and the change coefficient of the landing area after interference, judges whether the attack target is lost, and chooses whether to release the decoy signal;

[0074] The present invention is based on accurate modeling and training of radar wave emission and reflection characteristics at multiple angles and distances, combined with real-time environmental data and missile radar signal analysis, and can effectively identify and predict the terminal guidance characteristics of missiles. On this basis, the system can generate interference waves that are highly matched with the target reflection characteristics to achieve active interference with the missile's terminal guidance system, and generate interference blind spots through interference, causing the missile to lose the braking signal of the target object. By accurately calculating the characteristic parameters of the interference wave and the location and size of the blind spot, this solution not only improves the success rate of camouflage, but also avoids the risk of the missile being misled or failing when approaching the target;

[0075] The present invention also effectively guides the missile away from the real target by emitting decoy signal waves in an area far away from the target, thereby enhancing the survivability of the target. At the same time, by using the missile's trajectory fluctuation coefficient and landing area change coefficient for real-time calculation, it is possible to evaluate the interference effect and determine whether the missile has lost its attack capability, thereby further optimizing the countermeasure strategy. This highly targeted and real-time interference camouflage technology can effectively counter the enemy's high-precision missile guidance in a complex battlefield environment, significantly improving the target's anti-detection camouflage capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0077] 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.

[0078] 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.

[0079] Example:

[0080] See also Figure 1 , the present invention provides a technical solution:

[0081] An anti-detection camouflage method based on missile-borne terminal guidance characteristics, the specific steps include:

[0082] Step 1: Detect the camouflaged target object through radar transmission waves, build a feature data set, establish a reflection feature calculation model, and train the model.

[0083] The missile-borne terminal guidance feature refers to the technical characteristics of the missile using radar guidance to detect, lock and guide the target during flight, especially in the final stage of approaching the target. Radar guidance is a guidance method that relies on radar waves. Missiles or aircraft transmit radar waves, receive the returned reflected signals, and use these signals to obtain the target's location information, speed, direction and other data, thereby achieving precise navigation.

[0084] The reflection characteristics of radar waves, such as waveform, frequency, amplitude, etc., are affected by environmental factors, transmission angle and distance. By detecting under various environmental conditions and from different angles and distances, the radar reflection characteristics of the target object can be fully collected and analyzed. These multi-dimensional data can provide richer information for the model, making the extraction of reflection characteristics more comprehensive and facilitating the reflection characteristic calculation model to obtain accurate reflection characteristic parameters.

[0085] Amplitude is one of the key factors in electromagnetic wave strength. By adjusting the amplitude of the radar wave, the strength of the target's reflected signal can be changed, making it difficult for the enemy radar to accurately detect the target when it receives a weaker reflected signal. The change in amplitude can also be used to create a blind spot during the jamming process, thereby avoiding detection by the enemy radar.

[0086] Frequency determines the wavelength of radar waves, which affects the ability of radar waves to penetrate different objects. By choosing the right frequency, missile radars can be prevented from being identified by enemy radar systems with known frequencies. At the same time, frequency changes can produce spectral features, which help to generate interference in multiple frequency bands at the same time, making it difficult for enemy radars to effectively respond to a single frequency band.

[0087] The duration of the radar pulse determines the duration of the signal. Phase is a key parameter of the radar wave and determines the fluctuation characteristics of the signal. By changing the phase, the waveform can be modulated and an interference effect can be generated with the enemy's reflected wave, thereby interfering with the enemy's radar signal reception. Through precise phase control, an interference blind zone can also be created, making it impossible for the enemy radar to detect the true position of the missile.

[0088] Spectral characteristics include the frequency band, bandwidth, and other information of the radar signal. Different spectral characteristics can provide different detection capabilities and have a significant impact on the interference capability of the radar system.

[0089] Temperature directly affects the propagation speed and attenuation of electromagnetic waves. Humidity affects the propagation characteristics of electromagnetic waves in the air. Wind speed may affect the flight path of missiles and the propagation path of electromagnetic waves. Particle concentration affects the propagation of radar waves, especially when high-frequency radar is used. The increase in particle concentration may cause the attenuation of radar signals. By considering temperature, humidity, wind speed and particle concentration factors, the reflection characteristics of missiles can be more accurately predicted and adjusted, thereby interfering with the detection capabilities of enemy radars.

[0090] The reflection amplitude determines the intensity of the target's detection by the radar. The reflection frequency is closely related to the missile's launch frequency and the target's movement speed. The reflection phase determines the fluctuation pattern of the reflected signal. The reflection direction determines the target's position on the radar. The scattering characteristics of the missile refer to the missile's reflection and scattering behavior of radar waves. The time delay is the time difference from the radar wave's emission to its reflection. The polarization characteristics determine the polarization state of the reflected wave. By simulating the reflection characteristic parameters of the reflection amplitude, reflection frequency, reflection phase, reflection direction, scattering characteristics, time delay, and polarization characteristics, it can be better identified by the missile guidance radar and play a role in camouflaging and deceiving the missile.

[0091] In this embodiment, the method for constructing the feature data set is: using radar transmission waves with different transmission characteristics under different environmental data, and detecting the camouflaged target object from different angles and distances, respectively, obtaining the reflection characteristic parameters of the corresponding radar reflection waves, and constructing the feature data set in one-to-one correspondence with the radar transmission characteristics, environmental data, distance, and angle;

[0092] The emission characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0093] The environmental data include temperature, humidity, wind speed and particle concentration;

[0094] The reflection characteristic parameters include reflection amplitude, reflection frequency, reflection phase, reflection direction, scattering characteristics, time delay, and polarization characteristics of the reflected wave.

[0095] CNN neural network has a strong ability to automatically extract features, especially when processing spatial data or signals with local structures. The reflection characteristics of radar waves usually contain complex pattern information, especially considering different emission characteristics such as amplitude, frequency, phase, etc., environmental data such as temperature, humidity, etc., as well as multi-dimensional factors such as emission angle and distance, there is a highly nonlinear relationship between the data. Through its convolutional layer, CNN neural network can effectively extract these complex local features and optimize them layer by layer, thereby providing efficient computing power for the prediction of reflection features.

[0096] In missile radar signal processing, reflection features (such as reflection amplitude, frequency, phase, etc.) usually have spatial correlation. The signal detected by the radar system is not only related to the size, shape, material and other factors of the target, but also affected by multiple factors such as the emission angle, distance, and environmental noise. This makes it difficult for simple traditional methods to accurately model all possible reflection features. Through its multi-level convolutional structure, CNN can automatically identify important features in the signal, such as edges, textures, and spectrum changes, and optimize the expression of these features through network layer-by-layer transmission, and can efficiently extract valuable information from complex signals. For example, changes in temperature, humidity, wind speed, etc. will directly affect the propagation characteristics of the signal. In a dynamic environment, the CNN neural network can continuously adjust its prediction model to adapt to different environmental changes by learning historical data and real-time feedback. For example, when environmental conditions change, the CNN neural network can quickly adapt and adjust the prediction of reflection features, thereby optimizing the design of interference waves and improving the success rate of interference.

[0097] The specific steps of establishing the reflection feature calculation model and training the model are as follows:

[0098] The reflection parameter calculation model is based on the CNN neural network, which specifically includes a convolution layer, an activation layer, a pooling layer and an output layer:

[0099] Convolution layer: extracts features from the input feature data set through convolution operation. The calculation formula is:

[0100]

[0101] in, For the emission characteristics, environmental data, distance, and angle in the input feature data set, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column;

[0102] The activation layer is calculated as:

[0103]

[0104] in, The coordinates of the output matrix of the convolutional layer output;

[0105] The calculation formula of the pooling layer is:

[0106]

[0107] in, is the input matrix, is the maximum pooling output;

[0108] The calculation formula of the output layer is:

[0109]

[0110] in, is the output of the pooling layer, is the reflection characteristic parameter, is the weight, is the bias parameter;

[0111] The feature data set is preprocessed, and the emission features, environmental data, distance, and angle in the feature data set are used as the input of the model, and the reflection feature parameters are used as the output to train the model.

[0112] In this embodiment, the method for preprocessing the feature data set is to process it through maximum normalization, and the calculation formula is:

[0113]

[0114] in, The data after normalization of each data type of the feature dataset, is the largest number of each data type in the feature dataset, is the smallest number of each data type in the feature data set. For each data type in the feature dataset data, is the number of data in each data type of the feature dataset.

[0115] Step 2: Perform time domain and frequency domain analysis based on the radar signal received at the terminal of the missile to obtain the terminal guidance characteristics of the missile. At the same time, capture the relative position of the missile and the target object, obtain the launch angle and distance of the missile radar, and after preprocessing with the real-time environmental data, input it into the reflection parameter calculation model to predict the reflection characteristic parameters.

[0116] In this embodiment, the missile terminal guidance characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0117] The specific steps of performing time domain and frequency domain analysis to obtain the terminal guidance characteristics of the missile are:

[0118] The waveform of the observed signal is analyzed in the time domain to extract the amplitude, frequency, pulse duration, and phase;

[0119] Frequency domain analysis obtains the spectrum of the signal through fast Fourier transform. According to the spectrum characteristics of spectrum analysis, the specific steps of obtaining the spectrum of the signal through fast Fourier transform are as follows:

[0120] The received radar signal at the end of the missile is sampled at the same time interval to obtain the discrete signal of the radar signal at the end of the missile, and the sampled discrete signal is fast Fourier transformed. The calculation formula of fast Fourier transform is:

[0121]

[0122] in, The radar signal is the first Quantity, is the time domain signal of the radar signal samples, is the total number of samples, is an imaginary unit, Represents the frequency domain frequency components, .

[0123] The radar signal at the end of the missile usually contains complex time domain waveforms, which may be affected by multiple factors such as target motion, environmental changes, and interference. It is very difficult to directly analyze these signals in the time domain because time domain signals usually contain various irregular fluctuations, noise, and multipath effects. By transforming the signal to the frequency domain through fast Fourier transform, the periodicity, frequency components, and amplitude characteristics of the signal will become more obvious. Frequency domain information can often more clearly reveal the motion characteristics of the target such as speed and direction. In a complex battlefield environment, the terminal guidance system of the missile needs to quickly identify and track the target. By extracting frequency domain features through fast Fourier, the relative motion state of the target can be accurately judged.

[0124] Step 3: By modulating the radar interference wave with the same interference characteristic parameters as the reflection characteristic parameters according to the reflection characteristic parameters, an interference blind zone is generated with the reflected wave. According to the relative position of the missile and the target object and the control of the position and size of the interference blind zone, the blind zone of the missile's position is covered.

[0125] Interference is a phenomenon of wave interaction, when two or more waves propagate at the same or similar frequency and wavelength, they will superimpose on each other. Specifically, radar interference waves and reflected waves from targets will interfere under certain conditions. Interference may strengthen the intensity of the waves, where the amplitudes of the two waves are in the same direction and the wave strength increases after superposition, i.e. in-phase interference, or weaken the intensity of the waves, where the amplitudes of the two waves are in opposite directions and the wave strength decreases or disappears completely after superposition, forming an interference blind spot, i.e. anti-phase interference.

[0126] The interference blind zone is an area created by the anti-phase interference of the reflected wave and the interference wave. In this area, the radar cannot detect the missile or target. By adjusting the characteristic parameters of the interference wave, such as frequency, phase, amplitude, etc., the size and position of the interference blind zone can be controlled, thereby achieving "invisibility" of the target.

[0127] The characteristic parameters of the radar transmission wave and the target reflected wave are known. By modulating the characteristic parameters of the interference wave to make it similar to the reflected wave (especially frequency, amplitude and phase), the conditions for anti-phase interference are formed, thereby creating a blind spot in the radar's detection area.

[0128] By precisely adjusting the phase of the interference wave, especially the phase difference with the reflected wave, the position of the interference area can be controlled. If the missile changes its position relative to the target, the phase of the interference wave is adjusted accordingly so that the interference blind area covers the area where the missile is located.

[0129] By controlling the emission angle and distance of the interference wave, the expansion direction and size of the interference blind area can be affected. For example, if the missile is far away from the target, the frequency and phase of the interference wave can be adjusted to form a larger blind area at the midpoint between the target and the missile; if the missile is close to the target, the angle of the interference wave can be adjusted to accurately cover the missile's path.

[0130] The interference blind zone effectively makes the target object "disappear" within the radar's detection range, thus avoiding being discovered and hit by missiles. Through the interference of interference waves and reflected waves, the target object can hide its position, greatly improving its survivability.

[0131] In this embodiment, the interference characteristic parameters include: amplitude, frequency, pulse duration, phase, and spectrum characteristics;

[0132] The specific steps of adjusting the interference characteristic parameters of the interference wave and the reflected wave to produce the interference blind area are as follows:

[0133] The calculation formulas for interference wave and reflected wave are:

[0134]

[0135] in, is the interference signal, is the reflected signal, , are the wavelengths of the interference signal and the reflected signal, respectively. are the frequencies of the interference signal and the reflected signal, respectively. , are the initial phases of the interference signal and the reflected signal, , are the amplitudes of the interference signal and the reflected signal respectively;

[0136] The waveform after interference is:

[0137] ；

[0138] The coordinate position of the interference blind area after interference:

[0139] ;

[0140] when Generate interference blind area:

[0141]

[0142] in, For interference blind area The coordinate position at the time, is the wavelength after interference, is the frequency of the wave after interference, is a positive integer;

[0143] Combined with the position of the missile, a dynamic blind spot model is constructed:

[0144]

[0145] in, For interference waves The phase of the moment, is the phase of the reflected wave, is the frequency of the wave after interference, The missiles tested The location at the moment, is a positive integer used to represent the periodic changes of the wave.

[0146] Step 4: Get the continuous coordinate position of the missile, get the discrete coordinate position at the same time interval, calculate the missile's trajectory fluctuation coefficient and the change coefficient of the landing area after interference, calculate the missile's blinding index in this time period, and determine whether the target object is lost based on the blinding index, and choose whether to release the lure signal.

[0147] The trajectory fluctuation coefficient is usually expressed by measuring the volatility of the missile's continuous trajectory. It reflects the degree of deviation between the actual flight trajectory of the missile and the expected trajectory in a short period of time, or the volatility of the trajectory change. The larger the trajectory fluctuation coefficient, the more likely the missile is to be interfered with and lose the guidance signal of the target object, resulting in a larger trajectory fluctuation.

[0148] In this embodiment, the calculation steps of the missile's trajectory fluctuation coefficient are as follows:

[0149] According to the historical coordinate position, the historical running trajectory is calculated, and the calculation formula is:

[0150]

[0151] in, For The missile coordinates detected at all times, For The missile coordinates detected at all times, For The predicted coordinate position of the missile at time;

[0152] The running trajectory fluctuation coefficient is calculated based on the predicted coordinate position and the actual detected coordinate position. The calculation formula is:

[0153]

[0154] in, is the missile’s trajectory fluctuation coefficient, For The missile's trajectory fluctuation coefficient at any moment, For missiles The actual coordinate position is detected at all times. The duration of continuous observation of the missile.

[0155] In this embodiment, the calculation formula of the change coefficient of the landing area is:

[0156]

[0157] in, is the coefficient of variation of the landing area, is the number of trajectory equations, is the continuous observation time interval of the missile, Is the number of whether the target object is on the missile's trajectory.

[0158] The coefficient of variation of the landing zone reflects the degree of change in the landing position of the missile during flight due to deviation from the target trajectory or external interference. It is usually calculated based on the deviation between the predicted landing area of ​​the missile and the actual landing area. If the coefficient of variation of the landing zone is large, it means that the missile's flight trajectory has deviated greatly in the final stage, and the final landing position is far away from the target position. This usually indicates that the missile has lost the target or the target has been interfered with. If the coefficient of variation of the landing zone is smaller, it means that even if the trajectory fluctuates greatly, the missile is still adjusting the trajectory to fly towards the target object.

[0159] In this embodiment, the calculation formula of the blinding index is:

[0160]

[0161] in, is the blinding index, are the weights of the running trajectory fluctuation coefficient and the landing area variation coefficient, , is the missile’s trajectory fluctuation coefficient, is the coefficient of variation of the landing area;

[0162] when When the missile is judged to have lost the signal of attacking the target object, no decoy signal will be released;

[0163] when When the missile is judged to be still attacking the target object, a decoy signal is released;

[0164] in, The blinding index threshold for determining when a missile loses the signal of attacking a target object.

[0165] The blinding coefficient is an important basis for judging whether a missile has lost its target. The higher the blinding coefficient, the better the effect of missile interference, and the lower the blinding coefficient, the lower the blinding effect on the missile. The blinding coefficient plays a core role in anti-detection camouflage. By calculating the blinding coefficient of the missile in real time, it is possible to effectively judge whether the missile is in an attack failure state. If the blinding coefficient is high, it means that the attack accuracy of the missile has been seriously damaged. At this time, further interference operations can be stopped to avoid wasting interference resources. On the contrary, if the blinding coefficient is low, it means that the missile still has the ability to attack, then you can choose to release a decoy signal or continue to interfere, increasing the probability of the missile deviating from the target, thereby further protecting the target.

[0166] Step 5: In a safe area far away from the target object to be camouflaged, modulate the decoy signal wave identical to the reflected wave based on the reflection characteristics to further lure and camouflage the missile.

[0167] Active electronic jamming requires energy consumption and complex resources for continuous jamming. If the missile has lost its target through blinding, even if jamming continues, its attack trajectory cannot be changed, so there is no need to conduct further jamming. By judging whether the missile has been successfully blinded, unnecessary jamming can be avoided, the burden on the jamming system can be reduced, and resources can be effectively saved.

[0168] If the calculated blinding index indicates that the missile has lost its ability to lock onto the target and has lost its attack capability, the jamming can be stopped to avoid ineffective operations. Otherwise, if the missile is still effective or relies on inertial guidance to correct its trajectory, continuing to implement decoy signal jamming can cause it to further deviate from the target until it completely loses its attack capability.

[0169] By continuing to release decoy signals when the missile is not blinded, continuous counter-interference can be achieved. This method does not rely solely on a single interference method, but rather causes the missile to fall into a complex state of confusion through continuous interference. Even if the missile has lost radar target lock, the inertial guidance may still fly based on past information. At this time, continuous decoy signals can further disrupt the missile's flight trajectory, increase the difficulty of hitting the target, and achieve multi-dimensional interference.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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. An anti-detection camouflage method based on missile-borne terminal guidance characteristics, characterized in that: The specific steps include: Step 1: Detect the camouflaged target object through radar transmission waves, build a feature data set, establish a reflection feature calculation model, and train the model; Step 2: According to the radar signal received at the end of the missile, time domain and frequency domain analysis are performed to obtain the terminal guidance characteristics of the missile, and at the same time, the relative position of the missile and the target object is captured, the launch angle and distance of the missile radar are obtained, and after preprocessing with the real-time environmental data, the reflection characteristic parameters are input into the reflection parameter calculation model to predict. Step 3: According to the reflection characteristic parameters, a radar interference wave with interference characteristic parameters identical to the reflection characteristic parameters is modulated to generate an interference blind zone with the reflection wave. According to the relative position of the missile and the target object and the control of the position and size of the interference blind zone, the position of the missile is covered with a blind zone. Step 4: Obtain the continuous coordinate position of the missile, obtain the discrete coordinate position at the same time interval, calculate the missile's trajectory fluctuation coefficient and the change coefficient of the landing area after interference, calculate the missile's blinding index in this time period, and determine whether the target object is lost based on the blinding index, and choose whether to release the lure signal; Step 5: In a safe area far away from the target object to be camouflaged, according to the reflection characteristics, modulate the decoy signal wave that is the same as the reflected wave to further lure and camouflage the missile; The method for constructing the feature data set is: using radars with different emission characteristics to detect camouflaged target objects from different angles and distances under different environmental data, obtaining reflection characteristic parameters of corresponding radar reflection waves, and constructing the feature data set in one-to-one correspondence with the radar emission characteristics, environmental data, distance, and angle; The emission characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics; The environmental data include temperature, humidity, wind speed and particle concentration; The reflection characteristic parameters include reflection amplitude, reflection frequency, reflection phase, reflection direction, scattering characteristics, time delay, and polarization characteristics of the reflected wave.

2. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The specific steps of establishing the reflection feature calculation model and training the model are as follows: The reflection parameter calculation model is based on the CNN neural network, which specifically includes a convolution layer, an activation layer, a pooling layer and an output layer: Convolution layer: extracts features from the input feature data set through convolution operation. The calculation formula is: in, For the emission characteristics, environmental data, distance, and angle in the input feature data set, is the convolution kernel, are the coordinates of the output matrix, The convolution kernel is Row and The value of the column; The activation layer is calculated as: in, The coordinates of the output matrix of the convolutional layer output; The calculation formula of the pooling layer is: in, is the input matrix, is the maximum pooling output; The calculation formula of the output layer is: in, is the output of the pooling layer, is the reflection characteristic parameter, is the weight, is the bias parameter; The feature data set is preprocessed, and the emission features, environmental data, distance, and angle in the feature data set are used as the input of the model, and the reflection feature parameters are used as the output to train the model.

3. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 2 is characterized in that: The method for preprocessing the feature data set is to process it through maximum normalization, and the calculation formula is: in, The data after normalization of each data type of the feature dataset, is the largest number of each data type in the feature dataset, is the smallest number of each data type in the feature data set. For each data type in the feature dataset data, is the number of data in each data type of the feature dataset.

4. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The missile terminal guidance characteristics include amplitude, frequency, pulse duration, phase, and spectrum characteristics; The specific steps of performing time domain and frequency domain analysis to obtain the terminal guidance characteristics of the missile are: The waveform of the observed signal is analyzed in the time domain to extract the amplitude, frequency, pulse duration, and phase; Frequency domain analysis obtains the spectrum of the signal through fast Fourier transform. According to the spectrum characteristics of spectrum analysis, the specific steps of obtaining the spectrum of the signal through fast Fourier transform are as follows: The received radar signal at the end of the missile is sampled at the same time interval to obtain the discrete signal of the radar signal at the end of the missile, and the sampled discrete signal is fast Fourier transformed. The calculation formula of fast Fourier transform is: in, The radar signal is the first Quantity, is the time domain signal of the radar signal samples, is the total number of samples, is an imaginary unit, Represents the frequency domain frequency components, .

5. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The interference characteristic parameters include: amplitude, frequency, pulse duration, phase, and spectrum characteristics; The specific steps of adjusting the interference characteristic parameters of the interference wave and the reflected wave to produce the interference blind area are as follows: The calculation formulas for interference wave and reflected wave are: in, is the interference signal, is the reflected signal, , are the wavelengths of the interference signal and the reflected signal, respectively. are the frequencies of the interference signal and the reflected signal, respectively. , are the initial phases of the interference signal and the reflected signal, , are the amplitudes of the interference signal and the reflected signal respectively; The waveform after interference is: The coordinate position of the interference blind area after interference: when Generate interference blind area: in, To interfere with the blind area The coordinate position at the time, is the wavelength after interference, is the frequency of the wave after interference, is a positive integer; Combined with the position of the missile, a dynamic blind spot model is constructed: in, For interference waves The phase of the moment, is the phase of the reflected wave, is the frequency of the wave after interference, The missile to be tested The location at the moment, is a positive integer used to represent the periodic changes of the wave.

6. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The calculation steps of the trajectory fluctuation coefficient of the missile are as follows: According to the historical coordinate position, the historical running trajectory is calculated, and the calculation formula is: in, For The missile coordinates detected at all times, For The missile coordinates detected at all times, For The predicted coordinate position of the missile at time; The running trajectory fluctuation coefficient is calculated based on the predicted coordinate position and the actual detected coordinate position. The calculation formula is: in, is the missile’s trajectory fluctuation coefficient, For The missile's trajectory fluctuation coefficient at any moment, For missiles The actual coordinate position is detected at all times. The continuous observation time of the missile.

7. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The calculation formula of the coefficient of variation of the landing area is: in, is the coefficient of variation of the landing area, is the number of trajectory equations, is the continuous observation time interval of the missile, Is the number of whether the target object is on the missile's trajectory.

8. The anti-detection camouflage method based on missile-borne terminal guidance characteristics according to claim 1 is characterized in that: The calculation formula of blinding index is: in, is the blinding index, are the weights of the running trajectory fluctuation coefficient and the landing area variation coefficient, , is the missile’s trajectory fluctuation coefficient, is the coefficient of variation of the landing area; when When the missile is judged to have lost the signal of attacking the target object, no decoy signal will be released; when When the missile is judged to be still attacking the target object, a decoy signal is released; in, The blinding index threshold for determining when a missile loses the signal of attacking a target object.

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