Cognitive Space-Time Adaptive Radar

Abstract

The present invention relates to the technical field of signal processing, and provides a cognitive space-time adaptive radar. It includes an identification module and a decision-making processing module; the identification module is used to identify the types of interference signals using a convolutional neural network, and identify the types of clutter signals according to the characteristics of each clutter signal; the decision-making processing module is used to select a corresponding interference suppression method according to the type of interference signal, and select a corresponding clutter suppression method according to the type of clutter signal, and use the interference suppression method and the clutter suppression method to process the echo signal. By identifying the types of interference signals and clutter signals, and then selecting corresponding suitable processing methods according to the identified types, the present invention can cope with various types of interference and clutter, and improve the detection accuracy of targets in complex environments.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and particularly to a cognitive space-time adaptive radar. Background Art

[0002] Relying on the altitude advantage of the platform, airborne radars not only overcome the shielding effects of the earth's curvature and terrain features, greatly expanding the detection range, but also can be flexibly deployed in actual use, providing valuable early warning time and planning horizons for various operations. Due to the down-looking operation, airborne radars need to detect moving targets in a strong ground / sea clutter background. In particular, the high-speed movement of the platform causes the coupling of the clutter Doppler frequency and the echo direction, resulting in a serious broadening of its Doppler frequency and causing weak moving targets to be submerged in strong clutter signals in the frequency domain. Fortunately, with the increasing maturity of phased array antennas and pulse Doppler technology, as well as the rapid improvement of hardware processing capabilities, airborne radars can perform two-dimensional joint signal processing in the spatial and temporal domains through the received multi-channel pulse echo data, significantly improving the detection performance of moving targets. Currently, space-time adaptive processing (STAP) technology has become one of the core key technologies of airborne radar systems.

[0003] With the continuous expansion of radar application requirements, airborne radars are facing increasingly complex terrain and electromagnetic environments. The weights of STAP are related to the echo data, so it can adapt to environmental changes. However, when the environment faced by the radar changes too quickly or the array form is a non-side-looking array, conformal array, end-fire array, or bistatic configuration, the radar echoes show a seriously non-uniform and non-stationary distribution. Since there are not enough training samples that meet the independent and identically distributed conditions, the performance of the STAP method drops significantly. How to effectively detect weak moving targets in complex environments such as non-uniform clutter, non-stationary clutter, wind farm clutter, and dense unintentional interference is a severe challenge faced by airborne radars.

[0004] In view of this, overcoming the defects of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a cognitive space-time adaptive radar.

[0006] The present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a cognitive space-time adaptive radar, including an identification module and a decision processing module;

[0008] The identification module is used to identify the type of interference signal using a convolutional neural network and identify the type of clutter signal according to the characteristics of each clutter signal;

[0009] The decision processing module is configured to select a corresponding interference suppression method according to the type of interference signal, select a corresponding clutter suppression method according to the type of clutter signal, and process the echo signal using the interference suppression method and the clutter suppression method.

[0010] Preferably, the type of the clutter signal includes one or more of uniform clutter, non-uniform clutter, non-stationary clutter, and wind farm clutter;

[0011] The step of selecting a corresponding clutter suppression method according to the type of clutter signal specifically includes:

[0012] When the type of the clutter signal is uniform clutter, select to use the STAP algorithm or the space-time polarization adaptive processing algorithm for clutter suppression;

[0013] When the type of the clutter signal is non-uniform clutter, select to use a geographical information-based sample selection method to select uniform training samples, perform power difference correction on the selected uniform training samples, and based on the corrected uniform training samples, select the STAP algorithm or the color loading STAP method for clutter suppression;

[0014] When the type of the clutter signal is non-stationary clutter, select to use the 3D-STAP method or a non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation for clutter suppression;

[0015] When the type of the clutter signal is wind farm clutter, select to use a wind farm isolated point clutter suppression method based on micro-Doppler characteristics for suppression.

[0016] Preferably, the type of the interference signal includes one or more of unintentional interference, main lobe interference, side lobe interference, and main lobe unintentional interference;

[0017] The step of selecting a corresponding interference suppression method according to the type of interference signal specifically includes:

[0018] When the type of the interference signal is unintentional interference or main lobe unintentional interference, select to use an airborne radar unintentional interference suppression method based on frequency domain adaptive filtering for interference suppression;

[0019] When the type of the interference signal is main lobe interference, select to use a space-time polarization joint adaptive anti-main lobe interference method for interference suppression;

[0020] When the type of the interference signal is side lobe interference, select to use the STAP method in an interference environment for interference suppression.

[0021] Preferably, the airborne radar unintentional interference suppression method based on frequency domain adaptive filtering specifically includes:

[0022] Based on the maximum likelihood criterion, interference spectrum estimation is carried out through adaptive beamforming and frequency spectrum estimation algorithms to obtain the power spectral density estimator at each frequency point;

[0023] According to the power spectral density estimators at each frequency point, the frequency range of the interference signal is determined;

[0024] According to the frequency range of the interference signal, multiple channels are constructed, and each channel corresponds to an interference filter;

[0025] In each channel, the interference filter is used to filter out the interference signal to obtain the interference-free signal of each channel. For the colored noise caused by filtering, noise compensation is performed on the interference-free signal of each channel to obtain the channel output signal of each channel;

[0026] Select the channel output signal with the minimum power as the final output signal.

[0027] Preferably, the interference spectrum estimation is carried out through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion to obtain the power spectral density estimators at each frequency point, specifically including:

[0028] Determine that in a Gaussian white noise environment, the output signal corresponding to the u th pulse signal after adaptive beamforming at the corresponding spatial angle is ; where is the output signal of the u th pulse interference signal after adaptive beamforming; is the noise signal, and obeys a Gaussian distribution with a mean of 0 and a variance of ;

[0029] After performing a mixed-radix FFT transformation on , the observation model at the frequency point is ; where is the observed value of the noise signal intensity at the frequency u in the th observation, which obeys a complex Gaussian distribution with a mean of 0 and a variance of , that is ; is the observed value of the interference signal intensity at the frequency u in the th observation; K is the number of groups of received signals;

[0030] When the array receives K groups of signals, it is calculated that at the frequency point The power spectral density estimator at [location] is ; where is the amplitude value estimator at frequency point , ; follows a complex Gaussian distribution with mean and variance , that is ; is the estimated value of the interference signal strength at frequency ; is the estimated value of the noise signal strength at frequency .

[0031] Preferably, determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point specifically includes:

[0032] Based on the power spectral density estimator of the signal , establish a signal observation model as ; where represents that there is no interference at frequency point , represents that there is interference at frequency point ;

[0033] According to the signal observation model, determine the non-interference power spectral density estimator of frequency point ; follows an exponential distribution; where the non-interference power spectral density estimator is the power spectral density estimator corresponding to the case where there is no interference at the corresponding frequency point;

[0034] Set the power spectral density estimator of frequency point as the detection unit, the power spectral density estimator of frequency point as the protection unit, the power spectral density estimator of frequency point as the reference unit, and determine the reference mean according to the power spectral density estimator of the reference unit, and the expression is ; where is the power spectral density estimator at frequency point , is the power spectral density estimator at frequency point , is the length of the protection unit, is the length of the reference unit;

[0035] According to the power spectral density estimators of each frequency point and the reference mean , determine whether there is interference at each frequency point, and determine the frequency range of the interference signal according to the range of the frequency points with interference ;

[0036] Among them, when the power spectral density estimator of the frequency point satisfies , it is determined that there is interference at the frequency point ; when the power spectral density estimator of the frequency point satisfies F , it is determined that there is no interference at the frequency point . Among them, P

[0037] Preferably, it further includes a feedback control module, which is used to evaluate the indexes of the processed signal. If it is evaluated that the processed signal does not meet the preset indexes, a message indicating that the indexes are not reached is fed back to the decision processing module;

[0038] According to the message indicating that the indexes are not reached, the decision processing module switches to the next clutter suppression method for processing, or increases the polarization dimension information in the clutter suppression method until the feedback control module evaluates that the processed signal meets the preset indexes.

[0039] Preferably, the switching to the next clutter suppression method for processing according to the message indicating that the indexes are not reached, or increasing the polarization dimension information in the clutter suppression method specifically includes:

[0040] If the type of the corresponding clutter signal corresponds to multiple clutter suppression methods, when receiving the message indicating that the indexes are not reached, switch to the next clutter suppression method for processing until switching to the last clutter suppression method;

[0041] If after processing the echo signal using the last clutter suppression method, the processed signal still does not meet the preset indexes, select one of the multiple clutter suppression methods and process the echo signal after increasing the polarization dimension information.

[0042] Preferably, it further includes an algorithm library module, which is used to store various clutter suppression methods and various interference suppression methods for the decision processing module to select and use.

[0043] Preferably, the identifying the type of the clutter signal according to the characteristics of each clutter signal specifically includes:

[0044] Identifying the type of the clutter signal according to the distance where the clutter signal is located.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By identifying the types of interference signals and clutter signals, and then selecting corresponding suitable processing methods according to the identified types, the present invention can cope with various types of interference and clutter, and improve the detection accuracy of targets in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0047] Figure 1 FIG. 8 is a schematic structural diagram of the first cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0048] Figure 2 FIG. 12 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the first cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0049] Figure 3 FIG. 16 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the second cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0050] Figure 4 FIG. 20 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the third cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0051] Figure 5 FIG. 24 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the fourth cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0052] Figure 6 FIG. 28 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the fifth cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0053] Figure 7 FIG. 32 is a schematic structural diagram of the second cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0054] Figure 8 FIG. 36 is a schematic structural diagram of the third cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0055] Figure 9 FIG. 40 is a schematic flowchart of a method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering in the sixth cognitive space-time adaptive radar provided by the embodiment of the present invention;

[0056] Figure 10It is a schematic diagram of an airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering in the seventh cognitive space-time adaptive radar provided by an embodiment of the present invention;

[0057] Figure 11 It is a schematic diagram of the architecture of the fourth cognitive space-time adaptive radar provided by an embodiment of the present invention;

[0058] Figure 12 It is a schematic diagram of a cognitive space-time adaptive radar provided by an embodiment of the present invention;

[0059] Figure 13 It is a schematic diagram of another cognitive space-time adaptive radar provided by an embodiment of the present invention;

[0060] Figure 14 It is a schematic diagram of yet another cognitive space-time adaptive radar provided by an embodiment of the present invention;

[0061] Figure 15 It is a schematic diagram of still another cognitive space-time adaptive radar provided by an embodiment of the present invention. Detailed implementation manners

[0062] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] Unless otherwise required by the context, throughout the specification and claims, the term "comprising" is interpreted in an open, inclusive sense, i.e., "including, but not limited to". In the description of the specification, the terms "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples" or "some examples", etc., are intended to indicate that specific features, structures, materials or characteristics related to the embodiment or example are included in at least one embodiment or example of the present disclosure. The schematic representations of the above terms are not necessarily referring to the same embodiment or example. In addition, the specific features, structures, materials or characteristics may be included in any one or more embodiments or examples in any appropriate manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order of appearance and position, etc., but it does not limit that they can be carried by one embodiment or example in a combined manner.

[0064] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present disclosure, unless otherwise specified, the meaning of "a plurality" is two or more. In addition, for example, in the description, for the same type of nouns, the method of adding "A" and "B" at the end is used to describe them as two independent individuals. In this case, the features defined with "A" and "B" are only used for the purpose of distinguishing similar individuals and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features.

[0065] In the description of the present invention, there will be a description method of "A and / or B" (where A and B are used to formally represent specific feature contents), and the corresponding description method includes the following three combinations: only A, only B, and the combination of A and B.

[0066] As used in the present invention, "about", "substantially" or "approximate" includes the stated value and the average value within an acceptable deviation range of the specific value, where the acceptable deviation range is determined by those of ordinary skill in the art considering the measurement being discussed and the errors associated with the measurement of the specific quantity (i.e., the limitations of the measurement system).

[0067] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0068] Example 1:

[0069] In the prior art, there have been some methods to use prior knowledge such as roads, terrain, surface cover types, etc. for adaptive signal processing to improve the target detection performance of airborne radars in complex environments. There is also a knowledge-aided STAP (KA-STAP) implementation scheme. With the support of a geographic information database, it effectively suppresses distributed clutter, main-lobe discrete clutter, and side-lobe discrete clutter based on color loading technology, selects uniform samples based on surface cover type information, and iteratively eliminates interfering targets based on radar tracking information feedback. The effectiveness of this scheme has been verified by the measured data of an X-band 8-channel airborne radar. There is also another implementation scheme, which features mainly color loading technology and supplemented by optimized sample selection based on prior knowledge. Although the above two schemes give the preliminary implementation process of the KA-STAP technology, there are still the following deficiencies: (1) The refined classification processing of airborne radar clutter cannot be achieved; (2) The deep fusion of prior knowledge and radar echo data is insufficient; (3) The problem of interference suppression in complex electromagnetic environments is not considered.

[0070] To solve the above problems, Embodiment 1 of the present invention provides a cognitive space-time adaptive radar, as Figure 1 shown, which includes an identification module and a decision-making processing module.

[0071] The identification module is used to identify the type of interference signal using a convolutional neural network, and identify the type of clutter signal according to the characteristics of each clutter signal. Among them, those skilled in the art pre-collect data of various interference signals in advance, and perform type annotation on the data to obtain a training set, and use the training set to train the original convolutional neural network to obtain the convolutional neural network.

[0072] The identifying the type of clutter signal according to the characteristics of each clutter signal specifically includes: identifying the type of clutter signal according to the distance where the clutter signal is located. For example, for non-stationary clutter, since non-stationarity is generated by clutter blocks closer to the carrier aircraft, the characteristic of non-stationary clutter is that the Doppler characteristic of the clutter is related to the distance, so that the type of clutter signal can be identified through different distances. In actual use, it can be: those skilled in the art preset multiple distance intervals, and one distance interval corresponds to one type of clutter signal. When it is analyzed that the clutter signal is within the corresponding distance interval, it is determined that the type of the clutter signal is the type of clutter signal corresponding to the distance interval.

[0073] The decision-making processing module is used to select a corresponding interference suppression method according to the type of interference signal, select a corresponding clutter suppression method according to the type of clutter signal, and use the interference suppression method and the clutter suppression method to process the echo signal.

[0074] In this embodiment, by identifying the types of interference signals and clutter signals, and then selecting corresponding suitable processing methods according to the identified types, it is possible to cope with various types of interference and clutter, and improve the detection accuracy of targets in complex environments.

[0075] In an actual application scenario, the types of the clutter signals include one or more of uniform clutter, non-uniform clutter, non-stationary clutter, and wind farm clutter.

[0076] Select a corresponding clutter suppression method according to the type of clutter signal, which specifically includes: when the type of the clutter signal is homogeneous clutter, select to use the STAP algorithm (also called the traditional STAP algorithm) or the space-time polarization adaptive processing algorithm for clutter suppression; when the type of the clutter signal is non-homogeneous clutter, select to use the sample selection method based on geographic information to select uniform training samples, perform power difference correction on the selected uniform training samples, and based on the corrected uniform training samples, select the STAP algorithm or the color loading STAP method for clutter suppression; wherein, the step of selecting the STAP algorithm or the color loading STAP method for clutter suppression based on the corrected uniform training samples specifically is: estimate the clutter noise covariance matrix using the corrected uniform training samples, then calculate the corresponding filtering vector according to the clutter noise covariance matrix, and use the filtering vector to process the clutter signal. The methods for estimating the clutter noise covariance matrix and calculating the filtering vector are the STAP algorithm or the color loading STAP method.

[0077] When the type of the clutter signal is non-stationary clutter, select to use the 3D-STAP method or the non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation for clutter suppression; when the type of the clutter signal is wind farm clutter, select to use the wind farm isolated point clutter suppression method based on micro-Doppler characteristics for suppression.

[0078] The space-time polarization adaptive processing algorithm is also called the non-stationary clutter suppression method based on space-time polarization adaptive processing, the sample selection method based on geographic information is also called the prior knowledge training sample selection method for STAP after Doppler, the color loading STAP method is also called an airborne radar knowledge-assisted color loading clutter suppression method and device, the non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation is also called the conformal array airborne radar space-time clutter spectrum adaptive compensation and clutter suppression method, and the wind farm isolated point clutter suppression method based on micro-Doppler characteristics is also called the airborne MIMO radar adaptive clutter suppression method based on space-time sampling matrix.

[0079] In actual use, there is also a situation where wind farm clutter is mixed with other types of clutter signals. At this time, while using the method for suppressing isolated clutter points in a wind farm based on micro-Doppler characteristics to suppress interference, other methods corresponding to other types of clutter signals are also used for interference suppression; when one type of clutter signal corresponds to multiple clutter suppression methods, those skilled in the art preset the priorities of each method, and in actual use, the method with a higher priority is preferentially selected for processing. For example, in actual use, for uniform clutter, the priority of the STAP algorithm is higher than that of the space-time polarization adaptive processing algorithm; for non-uniform clutter, the priority of the STAP algorithm is higher than that of the color-loading STAP method; for non-stationary clutter, the priority of the 3D-STAP method is higher than that of the non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation.

[0080] The types of the interference signals include one or more of unintentional interference, main lobe interference, side lobe interference, and main lobe unintentional interference.

[0081] Selecting the corresponding interference suppression method according to the type of the interference signal specifically includes: when the type of the interference signal is unintentional interference or main lobe unintentional interference, selecting to use the method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering for interference suppression; when the type of the interference signal is main lobe interference, selecting to use the space-time polarization joint adaptive anti-main lobe interference method for interference suppression; when the type of the interference signal is side lobe interference, selecting to use the STAP method under an interference environment for interference suppression.

[0082] The space-time polarization joint adaptive anti-main lobe interference method is also called the method for suppressing clutter by space-time polarization joint adaptive processing of an airborne radar. Combining the above various embodiments, the following table can be obtained:

[0083]

[0084] That is, the decision processing module selects the corresponding clutter suppression method and interference suppression method according to the above table, so as to suppress the clutter signal and the interference signal.

[0085] Among them, the method for suppressing unintentional interference of an airborne radar based on frequency-domain adaptive filtering, as Figure 2 shown, specifically includes:

[0086] In step 201, based on the maximum likelihood criterion, interference spectrum estimation is performed through an adaptive beamforming and frequency spectrum estimation algorithm to obtain the power spectral density estimator at each frequency point; among them, this method is based on the subarray fast time-domain sampling data within multiple pulse repetition periods.

[0087] In step 202, according to the power spectral density estimator at each frequency point, the frequency range of the interference signal is determined.

[0088] In step 203, according to the frequency range of the interference signal, a plurality of channels are constructed, and each channel corresponds to an interference filter.

[0089] In step 204, in each channel, the interference filter is used to filter the interference signal to obtain the interference-free signal of each channel. For the color noise caused by filtering, noise compensation is performed on the interference-free signal of each channel to obtain the channel output signal of each channel.

[0090] In step 205, the channel output signal with the minimum power is selected as the final output signal. Among them, the operation of selecting the channel output signal with the minimum power as the final output signal may be to set a power discriminator at the output end of each channel, and use the power discriminator to output the channel output signal with the minimum power.

[0091] This method is based on the subarray fast time-domain sampling data within multiple pulse repetition periods. Based on the maximum likelihood criterion, interference spectrum estimation is realized through adaptive beamforming and frequency spectrum estimation algorithms, and the frequency range of the interference signal is identified; secondly, for the fast time sampling signal received by each subarray, interference suppression is performed through frequency-domain filtering; thirdly, the filtered output noise is compensated, thereby completing the suppression of main lobe unintentional interference and improving the target detection performance of the airborne radar.

[0092] Among them, the interference spectrum estimation is performed based on the maximum likelihood criterion through adaptive beamforming and frequency spectrum estimation algorithms to obtain the power spectral density estimation of each frequency point, as Figure 3 shown, specifically including:

[0093] In step 301, it is determined that in the Gaussian white noise environment, the output signal corresponding to the u th pulse signal after adaptive beamforming at the corresponding spatial angle is ; where is the output signal of the u th pulse interference signal after adaptive beamforming; is the noise signal, and obeys a Gaussian distribution with a mean of 0 and a variance of .

[0094] In step 302, after performing a mixed-radix FFT transformation on , the observation model at the frequency point is obtained as ; where is the observed value of the noise signal intensity at the frequency u in the th observation, and it obeys a complex Gaussian distribution with a mean of 0 and a variance of , that is ; is the observed value of the interference signal strength at frequency u in the -th observation; K is the number of groups of received signals.

[0095] In step 303, when the array has received K groups of signals, the estimated power spectral density at frequency is calculated as ; where is the estimated amplitude value at frequency , ; follows a complex Gaussian distribution with mean and variance , that is ; is the estimated value of the interference signal strength at frequency ; is the estimated value of the noise signal strength at frequency .

[0096] In a specific application scenario, determining the frequency range of the interference signal according to the estimated power spectral density at each frequency point, as shown in Figure 4 , specifically includes:

[0097] In step 401, according to the estimated power spectral density of the signal, a signal observation model is established as ; where represents that there is no interference at frequency , represents that there is interference at frequency .

[0098] In step 402, according to the signal observation model, the estimated value of the power spectral density without interference at frequency is determined; follows an exponential distribution; where the estimated power spectral density without interference is the corresponding estimated power spectral density when there is no interference at the corresponding frequency point.

[0099] In step 403, the estimated power spectral density at frequency is set as the detection unit, the estimated power spectral density at frequency is set as the protection unit, the estimated power spectral density at frequency is set as the reference unit, and according to the estimated power spectral density of the reference unit, the reference mean is determined, and the expression is ; where For the power spectral density estimator at the frequency point , For the power spectral density estimator at the frequency point , is the length of the protection unit, is the length of the reference unit.

[0100] In step 404, according to the power spectral density estimators at each frequency point and the reference mean , determine whether there is interference at each frequency point, and according to the range of the frequency points with interference, determine the frequency range of the interference signal .

[0101] Among them, when the power spectral density estimator at the frequency point is , it is determined that there is interference at the frequency point ; when the power spectral density estimator at the frequency point is , it is determined that there is no interference at the frequency point ; among them, P F is the false alarm probability determined according to historical experience, . It is expressed in the form of a mathematical formula as: .

[0102] In a preferred embodiment, according to the frequency range of the interference signal, construct multiple channels, and each channel corresponds to an interference filter, as Figure 5 shown, specifically including:

[0103] In step 501, according to the frequency range of the interference signal , determine that the reference range is ; where B represents the receiver bandwidth and L represents the number of range cells.

[0104] In step 502, set 2 m +1 channels, and according to the reference range, determine that the filtering range of the i-th channel is ; where i is the channel number, and m is obtained by those skilled in the art through empirical analysis.

[0105] In step 503, on the basis of the all-pass filter, set the amplitude-frequency response within the filtering range of the i-th channel to zero to obtain the interference filter of the i-th channel, that is, the interference filter of the i-th channel ; where is the bandwidth of the reference range, ; is the center frequency of the filtering range of the i-th channel, ; is the width of , with a frequency shift of gate function.

[0106] Among them, the input of each channel is the received echo signal, that is, the power of the input signals of each channel is the same, so that the output signal of the channel with the minimum power after filtering is the signal with the maximum degree of interference removed.

[0107] In an alternative embodiment, in each channel, an interference filter is used to filter out the interference signal to obtain the interference-free signal of each channel, specifically including:

[0108] When the echo signal of the corresponding range cell received by the subarray within one pulse repetition period of the airborne radar is , the spectrum of the interference-free signal of the i-th channel obtained after filtering out the interference signal using the interference filter of the i-th channel is ; where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0109] In a specific application scenario, for the colored noise caused by filtering, noise compensation is performed on the interference-free signals of each channel to obtain the channel output signal of each channel, as shown in Figure 6 , specifically including:

[0110] In step 601, in the passive mode, the noise power spectral density i of the -th channel is calculated through the amplitude values within the interference-free frequency band range; where represents the length of the frequency band not affected by interference.

[0111] In step 602, according to the noise power spectral density i of the -th channel, a compound Gaussian white noise with a power spectral density of i is generated for the -th channel; among them, the compound Gaussian white noise with a power spectral density of is generated by the computer in a way of generating random numbers obeying the Gaussian distribution.

[0112] In step 603, a noise filter is constructed for the i-th channel; where ; the compound Gaussian white noise is passed through the noise filter of the i-th channel and then superimposed on the output channel of the interference filter of the i-th channel to achieve noise compensation for the interference-free signal.

[0113] The airborne radar unintentional interference suppression method based on frequency domain adaptive filtering described above can effectively reduce the influence brought by the change of interference signal frequency and estimation error, so as to effectively suppress unintentional interference or main lobe unintentional interference.

[0114] In a preferred embodiment, as Figure 7 shown, the cognitive space-time adaptive radar described in this embodiment further includes a feedback control module, and the feedback control module is used to evaluate the indexes of the processed signal. If it is evaluated that the processed signal does not meet the preset indexes, a message that the indexes are not reached is fed back to the decision-making processing module;

[0115] According to the message that the indexes are not reached, the decision-making processing module switches to the next clutter suppression method for processing, or increases the polarization dimension information in the clutter suppression method until the feedback control module evaluates that the processed signal meets the preset indexes. The preset indexes are obtained by those skilled in the art through empirical analysis. In actual use, the preset indexes may be that the Minimum Detectable Velocity (MDV) is less than a preset value, and the preset value is obtained by those skilled in the art through requirement analysis.

[0116] Among them, switching to the next clutter suppression method for processing according to the message that the indexes are not reached, or increasing the polarization dimension information in the clutter suppression method specifically includes:

[0117] If the type of the corresponding clutter signal corresponds to multiple clutter suppression methods, when receiving the message that the indexes are not reached, switch to the next clutter suppression method for processing until switching to the last clutter suppression method;

[0118] If after processing the echo signal using the last clutter suppression method, the processed signal still does not meet the preset indexes, select one of the multiple clutter suppression methods and process the echo signal after increasing the polarization dimension information. For example, if after processing the homogeneous clutter using the traditional STAP algorithm, the processed signal fails to meet the preset indexes, switch to using the space-time polarization adaptive processing algorithm to process the homogeneous clutter. If after processing the homogeneous clutter using the space-time polarization adaptive processing algorithm, it still cannot meet the preset indexes, any one of the traditional STAP algorithm or the space-time polarization adaptive processing algorithm can be used to process the homogeneous clutter after increasing the polarization dimension information. In actual use, a method with the processed signal relatively closer to the preset indexes can be selected to increase the polarization dimension information for processing.

[0119] In a specific application scenario, as Figure 8As shown, it further includes an algorithm library module, which is used to store various clutter suppression methods and various interference suppression methods for the decision-making processing module to select and use.

[0120] Example 2:

[0121] Based on the method described in Embodiment 1, this invention combines specific application scenarios and elaborates on the airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering in Embodiment 1 through technical expressions in relevant scenarios.

[0122] The airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering provided in this embodiment specifically includes:

[0123] Based on the subarray fast-time domain sampling data within multiple pulse repetition periods, interference spectrum estimation is achieved through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion. The Constant False Alarm Rate Detector (CFAR) is used to determine the threshold to identify the frequency range of the interference signal; in the passive mode, the noise power spectral density is estimated using the power spectral density within the interference-free frequency band range; for the fast-time sampling signal received by each subarray, the amplitude-frequency response within the frequency range of the interference signal is set to zero; for the colored noise caused by filtering, a noise compensation link is set so that the output noise still follows a Gaussian distribution; in order to reduce the influence brought by the frequency change of the interference signal and the estimation error, based on the estimated frequency range of the interference signal as a reference, 2 m +1 groups of channels composed of a band-stop filter h 1,i and a band-pass filter h 2,i are set, and their stopband and passband ranges are respectively . Finally, a power comparator is set at the output end of each channel, and the signal of the channel with the lowest output power is selected as the final output signal. Here, B represents the receiver bandwidth and L represents the number of range cells. Specifically, as shown in Figure 9 and Figure 10 , it includes the following steps:

[0124] In step 701, the frequency range of the interference signal is identified, which specifically includes: In a Gaussian white noise environment, the output signal corresponding to a certain spatial angle after the first pulse signal passes through adaptive beamforming is:

[0125]

[0126] where is the output signal of the interference signal of the first pulse after adaptive beamforming, is the noise signal, and the noise signal follows a normal distribution with a mean of 0 and a variance of Gaussian distribution

[0127] After performing a mixed - radix FFT transformation on this signal, we obtain .

[0128] Since the interference pattern is narrow - band interference, in the frequency domain, the interference signal only has peaks at finite frequency points, while the noise is Gaussian white noise, and it still follows a Gaussian distribution at the frequency points in the frequency domain. Therefore, when the array receives K groups of signals, that is, observes the signals K times, at the frequency the observation model is:

[0129]

[0130] where is the observed value of the noise signal strength at the frequency u in the th observation. It follows a complex Gaussian distribution with a mean of 0 and a variance of , that is , is the observed value at the frequency in the th observation.

[0131] According to the maximum - likelihood estimation, the estimator of the signal at the frequency is:

[0132]

[0133] where follows a complex Gaussian distribution with a mean of and a variance of , that is , is the estimated value of the interference signal strength at the frequency , is the estimated value of the noise signal strength at the frequency .

[0134] Then the power spectral density of the signal is:

[0135]

[0136] Based on the power spectral density estimation model, the above - mentioned interference - frequency - band estimation problem can be transformed into a typical binary hypothesis - testing problem, and its observation model can be expressed as:

[0137]

[0138] Since when there is no interference signal at the frequency point , the estimator of its power spectral density is:

[0139]

[0140] Then It follows an exponential distribution. According to the CFAR principle, set the power spectral density estimator of the frequency point as the detection cell, the power spectral density estimator of the frequency point as the protection cell, and the power spectral density estimator of the frequency point as the reference cell. Determine the reference mean according to the power spectral density estimator of the reference cell, and the expression is where is the power spectral density estimator at the frequency point , is the power spectral density estimator at the frequency point , is the length of the protection cell, is the length of the reference cell; after determining the false alarm probability P F based on historical experience, determine whether there is interference in the detection cell through the following formula:

[0141]

[0142] where .

[0143] Detect each frequency point respectively, and the frequency range of the interference signal can be obtained. To reduce the estimation error caused by the spectral resolution, on the basis of identifying the frequency range of the interference signal, further set the interference frequency range as:

[0144]

[0145] where B represents the receiver bandwidth and L represents the number of range cells.

[0146] In step 702, perform noise power spectral density estimation, which specifically includes: in the passive mode, estimate the noise power spectral density of the channel using the power spectral density within the interference-free frequency band range in the i th channel, that is:

[0147]

[0148] where represents the length of the frequency band not affected by interference.

[0149] In step 703, perform frequency domain filtering, which specifically includes: according to the estimated interference frequency range, based on the all-pass filter, set the amplitude-frequency response within the interference signal frequency range to zero. In the active mode, assume that the echo signal of a certain range cell received by the subarray of the airborne radar within a pulse repetition period is:

[0150]

[0151] wherein is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0152] Construct the interference filter for the i-th channel , by estimating the frequency range of the interference signal , so that the response of the interference filter in this frequency range is 0, that is:

[0153]

[0154] wherein is the filter bandwidth; is the center frequency of the interference signal; is a rectangular function with a width of and a frequency shift of .

[0155] Therefore, the output signal spectrum of the i-th channel after frequency-domain filtering is:

[0156]

[0157] In step 704, noise compensation is performed, specifically including: In order to eliminate the influence of colored noise, a noise compensation link is added, specifically including:

[0158] Since in the passive mode, the noise power spectral density of the i-th channel has been estimated through the amplitude values in the interference-free frequency band range, that is ; wherein, represents the length of the frequency band not affected by interference.

[0159] According to the estimated power spectral density , for the i-th channel, corresponding Gaussian white noise is simulated and generated in the system.

[0160] Construct a noise filter , and its amplitude-frequency response and the interference filter have the following relationship:

[0161]

[0162] The Gaussian white noise signal obtained by simulation is passed through the noise filter and then superimposed on the output channel of the interference filter .

[0163] In step 705, multi-channel decision is performed to obtain an output signal, which specifically includes: setting a power comparator at the output end of each channel, and selecting the signal of the channel with the lowest output power as the final output signal.

[0164] In this embodiment, a method for suppressing the main lobe unintentional interference of an airborne radar based on frequency domain filtering is provided. First, based on the subarray fast-time domain sampling data within multiple pulse repetition periods, interference spectrum estimation is achieved through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion, and the frequency range of the interference signal is identified by using CFAR to determine the threshold. Secondly, interference suppression is performed on the fast-time sampling signals received by each subarray through frequency domain filtering. Thirdly, the filtered output noise is compensated. Finally, multi-channel decision is performed to complete the suppression of the main lobe unintentional interference, improving the target detection performance of the airborne radar.

[0165] This embodiment can achieve accurate estimation of interference parameters; moreover, the fast-time frequency domain filtering technology proposed in this embodiment effectively suppresses narrowband interference signals with different modulation methods without changing the length of the fast-time sampling data. Furthermore, through noise compensation processing, the influence of colored noise on the target detection performance after filtering is effectively solved.

[0166] Example 3:

[0167] This embodiment also conducts a more detailed functional division of the cognitive space-time adaptive radar from another perspective, such as Figure 11 shown, including a database module, an algorithm library module, an identification module, a decision processing module, and a feedback control module.

[0168] Among them, the database module is used to store data in four parts, namely geographic information data, radar data, platform system parameters, and radar system parameters, providing important theoretical support for subsequent airborne radar zoning processing. The geographic information database mainly contains two types of data, namely surface coverage data and digital elevation model data; the radar database contains measured data of multiple types of airborne radars in different terrain environments and electromagnetic environments. Among them, the terrain environments include cities, mountains, deserts, hills, seas, land-sea boundaries, etc.; the interference patterns include suppression noise interference, deception interference, smart interference, and unintentional communication interference, etc.; the platform system parameters include parameters such as the flight altitude, speed, and heading of the airborne radar; the radar system parameters include radar wavelength, beam pointing, beam width, pulse repetition frequency, and array offset angle, etc.

[0169] The algorithm library module includes an expert decision-making unit, a model library unit, and a STAP algorithm library unit. Among them, the STAP algorithm library unit is used to store airborne clutter suppression algorithm models, which contain more than a hundred airborne radar STAP algorithms to cope with dynamic terrain and complex electromagnetic environments. For example: reduced-dimension STAP, reduced-rank STAP, STAP in interference environment, non-uniform STAP, non-stationary STAP, STAP with new system, and STAP angle measurement, etc. The model library unit is based on prior knowledge such as the surface coverage database and the digital elevation model database, and realizes the simulation of the echo of the airborne radar according to the refined spatio-temporal clutter signal model of the airborne radar. This unit first maps many information points contained in the geographic database to each clutter block, secondly obtains the clutter scattering coefficient of the information points based on the statistical analysis model of the Lincoln Laboratory experimental data, and calculates the resolution cell area of the information points. Thirdly, it performs information point occlusion judgment, and finally obtains the refined clutter signal of the airborne radar. Based on this simulation method, high-fidelity simulation of the echo of the airborne radar under complex terrain environment, complex electromagnetic environment, and complex target environment can be realized, and at the same time, the echo of conventional airborne radar, conformal array airborne radar, end-fire array airborne radar, bistatic airborne radar, and MIMO airborne radar can be simulated. The expert decision-making unit selects the STAP algorithm with the highest matching degree with each clutter area and interference based on certain rules according to the distribution characteristics and interference patterns of the airborne radar clutter in the range-Doppler two-dimensional domain for filtering processing to achieve the global optimum of the target detection performance of the airborne radar. When there are two algorithms corresponding to the same echo type, the first algorithm has a higher priority. If the index requirements are not met, the second algorithm is selected for processing. The decision rules stored in the expert decision-making unit are shown in the following table:

[0170]

[0171] The recognition module first realizes interference recognition based on a convolutional neural network; secondly, according to the clutter signals received by the airborne radar in the actual working environment, the clutter signals are classified into uniform clutter, non-uniform clutter, non-stationary clutter, wind farm clutter, main lobe clutter, etc.; finally, according to the clutter characteristics in different regions, a specific STAP algorithm is selected for clutter suppression. Among them, the convolutional neural network does not require an additional feature extraction process, and only needs to input the labeled sample data into the network to automatically extract image features using the convolutional layer.

[0172] After identifying various types of clutter signals and interference signals, the decision processing module selects the corresponding processing methods from the algorithm library module for processing, and the various processing methods are shown in the above table.

[0173] Among them, the suppression process of non-uniform clutter is as Figure 12As shown, the method first constructs a prior knowledge information database using airborne radar platform parameters, topographic and geomorphic parameters, and geographic elevation parameters; secondly, determines the information points corresponding to each range-Doppler cell; thirdly, constructs a weighted Euclidean distance measure based on geomorphic type and geomorphic slope, as shown in Equations (1) and (2), to complete the selection of uniform samples, that is, selects uniform samples using a sample selection method based on geographic information; finally, corrects the power difference of the selected samples, and then uses the corrected uniform training samples for clutter suppression.

[0174] Assume that the k-th Doppler channel and the l-th range-Doppler (RD) cell are the cells to be detected. Then, the normalized vectors of the geomorphic types of this cell and its two adjacent RD cells on both sides are respectively:

[0175] (1)

[0176] Among them, represents the proportion of 8 geomorphic types in the l-th RD cell.

[0177] The weighted Euclidean distance measure is:

[0178] (2)

[0179] Among them, α, β, and γ represent the weighting coefficients.

[0180] The process of clutter suppression for non-stationary clutter using a non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation is as Figure 13 shown. This method first selects uniform samples based on geographic information, and transforms the conformal array receiving subarray into an equivalent uniform linear array through virtual uniform linear array transformation; secondly, transforms the space-time echo data into the space-time four-dimensional power spectrum domain, as shown in Equation (3), to realize the decoupling of non-stationary clutter and targets; thirdly, compensates the short-range non-stationary clutter to the maximum unambiguous range cell based on the selected uniform samples; finally, estimates the clutter covariance matrix of the range cell to be detected using the compensated space-time data and the long-range stationary clutter data, forms the space-time adaptive weights, and completes the clutter suppression process.

[0181] (3)

[0182] Among them represents the space-time four-dimensional steering vector, and R l represents the echo covariance matrix.

[0183] The process of clutter suppression in a wind farm is as Figure 14As shown in the figure, the method first processes the echo range-Doppler spectrogram using the Sobel operator and determines the range bin where the wind turbine is located in combination with geographical information. Secondly, the micro-Doppler characteristic parameters such as the estimated rotational speed and initial phase of the wind turbine blades are used to construct dictionary atoms. Finally, the orthogonal projection matrix formed by the dictionary atoms is used to suppress the clutter in the wind farm.

[0184] The flow of the airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering is as Figure 15 shown in the figure. The method first cognizes the external electromagnetic environment through adaptive beamforming (abbreviated as ADBF) technology in the passive mode to obtain the interference signals at each wave position, and then estimates the frequency and bandwidth of the interference signals based on the maximum likelihood criterion and the CFAR criterion. Finally, in the active mode, the main lobe unintentional interference is suppressed through fast frequency-domain filtering. At the same time, the filtered colored noise is whitened into white noise through noise compensation, which further improves the target detection performance while achieving interference suppression.

[0185] The feedback control module determines whether it is necessary to add polarization dimension information to improve the radar performance by judging whether the performance of the airborne radar after anti-interference and clutter suppression processing meets the index requirements. For example, the result after CFAR detection is used to judge whether the performance of the airborne radar after anti-interference and clutter suppression processing meets the technical and tactical index requirements, or it is measured by the minimum detectable speed index. If this index cannot meet the requirements, the polarization dimension information is added in the clutter suppression link to improve the radar performance.

[0186] It should be noted here that the processing methods in Embodiment 1 and the method in Embodiment 2 are all applicable in this embodiment and will not be elaborated here.

[0187] It is worth noting that the information interaction, execution process, etc. between the modules and units in the above-mentioned device and system, due to being based on the same concept as the method embodiment of the present invention, the specific content can be referred to the description in the method embodiment of the present invention and will not be elaborated here.

[0188] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc.

[0189] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A cognitive space-time adaptive radar, characterized in that, It includes an identification module and a decision-making processing module; The identification module is used to identify the type of interference signal using a convolutional neural network and, based on the characteristics of each clutter signal, identify the type of clutter signal; The decision-making processing module is used to select a corresponding interference suppression method according to the type of interference signal, select a corresponding clutter suppression method according to the type of clutter signal, and process the echo signal using the interference suppression method and the clutter suppression method; The types of interference signals include one or more of unintentional interference, main lobe interference, side lobe interference, and main lobe unintentional interference; The selection of the corresponding interference suppression method according to the type of interference signal specifically includes: when the type of interference signal is unintentional interference or main lobe unintentional interference, select to use the airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering for interference suppression; when the type of interference signal is main lobe interference, select to use the space-time polarization joint adaptive anti-main lobe interference method for interference suppression; when the type of interference signal is side lobe interference, select to use the STAP method in an interference environment for interference suppression; The airborne radar unintentional interference suppression method based on frequency-domain adaptive filtering specifically includes: performing interference spectrum estimation through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion to obtain the power spectral density estimators at each frequency point; determining the frequency range of the interference signal according to the power spectral density estimators at each frequency point; constructing multiple channels according to the frequency range of the interference signal, with each channel corresponding to an interference filter; in each channel, using the interference filter to filter the interference signal to obtain the interference-free signals of each channel, and performing noise compensation on the interference-free signals of each channel for the color noise caused by filtering to obtain the channel output signal of each channel; selecting the channel output signal with the minimum power as the final output signal.

2. The cognitive space-time adaptive radar according to claim 1, wherein The types of clutter signals include one or more of uniform clutter, non-uniform clutter, non-stationary clutter, and wind farm clutter; The selection of the corresponding clutter suppression method according to the type of clutter signal specifically includes: when the type of clutter signal is uniform clutter, select to use the STAP algorithm or the space-time polarization adaptive processing algorithm for clutter suppression; when the type of clutter signal is non-uniform clutter, select to use the sample selection method based on geographic information to select uniform training samples, perform power difference correction on the selected uniform training samples, and based on the corrected uniform training samples, select the STAP algorithm or the color loading STAP method for clutter suppression; when the type of clutter signal is non-stationary clutter, select to use the 3D-STAP method or the non-stationary clutter suppression method based on space-time clutter spectrum adaptive compensation for clutter suppression; when the type of clutter signal is wind farm clutter, select to use the wind farm isolated point clutter suppression method based on micro-Doppler characteristics for suppression.

3. The cognitive space-time adaptive radar according to claim 1, characterized in that Performing interference spectrum estimation through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion to obtain the power spectral density estimators at each frequency point specifically includes: Determine that in the Gaussian white noise environment, the output signal corresponding to the u th pulse signal after adaptive beamforming at the corresponding spatial angle is ; where is the output signal of the interference signal of the u th pulse after adaptive beamforming; is the noise signal, and obeys a Gaussian distribution with a mean of 0 and a variance of . After performing the mixed - radix FFT transformation on , the observation model at the frequency point is ; where is the observed value of the noise signal intensity at the frequency u in the -th observation. It follows a complex Gaussian distribution with a mean of 0 and a variance of , that is ; is the observed value of the interference signal intensity at the frequency u in the -th observation; K is the number of groups of the received signals; When the array receives K a set of signals, the estimated power spectral density at the frequency point is ; where is the estimated amplitude value at the frequency point , ; obeys a complex Gaussian distribution with a mean of and a variance of , that is ; is the estimated value of the interference signal strength at the frequency ; is the estimated value of the noise signal strength at the frequency .

4. The cognitive space-time adaptive radar according to claim 3, characterized in that, Determining the frequency range of the interference signal according to the power spectral density estimation quantities of each frequency point specifically includes: Based on the estimator of the power spectral density of the signal , a signal observation model is established as ; where represents the frequency point without interference, represents the frequency point with interference; Determine the frequency point according to the signal observation model of the interference-free power spectral density estimator ; obeys an exponential distribution; wherein, the interference-free power spectral density estimator is the power spectral density estimator corresponding to the case where there is no interference at the corresponding frequency point; Set frequency point The power spectral density estimator is the detection unit, and the frequency point The power spectral density estimator is the protection unit, and the frequency point The power spectral density estimator is the reference unit. According to the power spectral density estimator of the reference unit, the reference mean value is determined, and the expression is ; where is the power spectral density estimator at the frequency point , is the power spectral density estimator at the frequency point , is the length of the protection unit, is the length of the reference unit; Based on the power spectral density estimators of each frequency point and the reference mean value , determine whether there is interference at each frequency point, and determine the frequency range of the interference signal according to the range of the frequency points with interference ; Among them, when the power spectral density estimator of the frequency point is , it is determined that there is interference at the frequency point ; when the power spectral density estimator of the frequency point is , it is determined that there is no interference at the frequency point ; among them, P F is the false alarm probability determined according to historical experience, .

5. The cognitive space-time adaptive radar according to claim 1, wherein It further includes a feedback control module which is used to evaluate the indexes of the processed signal. If it is evaluated that the processed signal does not meet the preset indexes, the feedback control module feeds back a message that the indexes are not reached to the decision processing module; The decision processing module switches to the next clutter suppression method for processing according to the message that the indexes are not reached, or increases the polarization dimension information in the clutter suppression method until the feedback control module evaluates that the processed signal meets the preset indexes.

6. The cognitive space-time adaptive radar according to claim 5, wherein, Switching to the next clutter suppression method for processing according to the message that the indexes are not reached, or increasing the polarization dimension information in the clutter suppression method specifically includes: If there are multiple clutter suppression methods corresponding to the type of the corresponding clutter signal, when receiving the message that the indexes are not reached, switch to the next clutter suppression method for processing until switching to the last clutter suppression method; If the processed signal still does not meet the preset indexes after processing the echo signal using the last clutter suppression method, select one of the multiple clutter suppression methods and process the echo signal after increasing the polarization dimension information.

7. The cognitive space-time adaptive radar according to claim 1, wherein It further includes an algorithm library module which is used to store each clutter suppression method and each interference suppression method for the decision processing module to select and use.

8. The cognitive space-time adaptive radar according to claim 1, characterized in that, Identifying the type of the clutter signal according to the characteristics of each clutter signal specifically includes: Identifying the type of the clutter signal according to the distance where the clutter signal is located.

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