Method and device for airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering

Abstract

The present invention relates to the technical field of signal processing, and provides a method and device for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering. The method includes: performing interference spectrum estimation through an adaptive beamforming and frequency spectrum estimation algorithm based on the maximum likelihood criterion to obtain the power spectral density estimation quantity of each frequency point; determining the frequency range of the interference signal according to the power spectral density estimation quantity of each frequency point; constructing a plurality of channels according to the frequency range of the interference signal, and each channel corresponds to an interference filter; in each channel, using the interference filter to filter out the interference signal to obtain the interference-free signal of each channel, and performing noise compensation on the interference-free signal 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. The present invention completes the suppression of main lobe unintentional interference and improves the target detection performance of the airborne radar.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and particularly to a method and device for anti-main lobe unintentional interference of an airborne early warning radar based on frequency domain filtering. Background Art

[0002] Unmanned aerial vehicle (UAV)-borne radars, with their advantages of long endurance and low cost, have effectively solved the problems of long-time and large-scale early warning detection. However, with the development of information and communication technologies, the number of various wireless communication devices is increasing exponentially. At the same time, in order to effectively detect stealth targets, the working frequency band of UAV-borne early warning radars is continuously extended to the low-frequency band, resulting in more and more communication devices sharing the frequency band with the radar system. Therefore, during the operation of UAV-borne early warning radars, communication signal interference is inevitable.

[0003] Since the bandwidth of communication signals is much smaller than that of radar detection signals, for UAV-borne radars, the interference generated by communication signals belongs to narrowband interference. Currently, research on narrowband interference suppression mainly focuses on aspects such as time domain, space domain, frequency domain, polarization domain, and deep learning. However, the above-mentioned interference suppression research does not consider the non-uniform distribution of radar echo signals in the range dimension caused by unintentional interference, which makes it difficult to estimate the interference covariance matrix and the scenario where clutter and interference exist simultaneously. During the independent operation of UAVs, the changes in unintentional interference and the coexistence of clutter and interference will both affect the target detection performance of airborne radars. Therefore, there is an urgent need to invent an effective anti-main lobe interference method to achieve the frequency identification and suppression of interference signals.

[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 method for anti-main lobe unintentional interference of an airborne early warning radar based on frequency domain filtering to achieve the frequency identification and suppression of interference signals.

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

[0007] In a first aspect, the present invention provides a method for anti-main lobe unintentional interference of an airborne early warning radar based on frequency domain filtering, including:

[0008] Performing interference spectrum estimation through an adaptive beamforming and frequency spectrum estimation algorithm based on the maximum likelihood criterion to obtain the power spectral density estimators of each frequency point;

[0009] Determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point;

[0010] Constructing a plurality of channels according to the frequency range of the interference signal, with each channel corresponding to an interference filter;

[0011] In each channel, an interference filter is used to filter out the interference signal, and the interference-free signal of each channel is obtained. 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;

[0012] The channel output signal with the minimum power is selected as the final output signal.

[0013] Preferably, the interference spectrum is estimated by the adaptive beamforming and frequency spectrum estimation algorithm based on the maximum likelihood criterion to obtain the power spectral density estimator of each frequency point, specifically including:

[0014] 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 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 ;

[0015] 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;

[0016] When the array receives K groups of signals, the power spectral density estimator at the frequency point is calculated as ; where is the amplitude value estimator at the frequency point , ; where obeys a complex Gaussian distribution with a mean of and a variance of , that is, ; is the estimated value of the interference signal intensity at the frequency ; is the estimated value of the noise signal intensity at the frequency .

[0017] Preferably, determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point specifically includes:

[0018] Based on the power spectral density estimator of the signal , establish the signal observation model as ; where represents the frequency point without interference, represents the frequency point with interference;

[0019] According to the signal observation model, determine the non-interference power spectral density estimator of the frequency point ; obeys the 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;

[0020] Set the power spectral density estimator of the frequency point as the detection unit, the power spectral density estimator of the frequency point as the protection unit, the power spectral density estimator of the 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 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;

[0021] 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 .

[0022] Preferably, determining whether there is interference at each frequency point according to the power spectral density estimators of each frequency point and the reference mean specifically includes:

[0023] When the power spectral density estimator of the frequency point is , it is determined that there is interference at the frequency point

[0024] When the frequency point Power spectral density estimator When it is determined that the frequency point There is no interference; where P F Is the false alarm probability determined according to historical experience, .

[0025] Preferably, according to the frequency range of the interference signal, a plurality of channels are constructed, and each channel corresponds to an interference filter, specifically including:

[0026] 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;

[0027] Set 2 m +1 channels, and according to the reference range, determine that the filtering range of the i-th channel is ;

[0028] 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 a gate function with a width of and a frequency shift of .

[0029] Preferably, in each channel, the interference filter is used to filter out the interference signal to obtain the interference-free signal of each channel, specifically including:

[0030] When the echo signal of the corresponding range cell received by the subarray of the airborne radar within a pulse repetition period 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.

[0031] Preferably, 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, specifically including:

[0032] In the passive mode, calculate the firsti Noise power spectral density of one channel ; wherein, represents the length of the frequency band not affected by interference;

[0033] According to the noise power spectral density of the i th channel , generate compound Gaussian white noise with a power spectral density of i for the th channel;

[0034] Construct a noise filter for the i-th channel ; wherein, ; Pass the compound Gaussian white noise through the noise filter of the i-th channel and then superimpose it on the output channel of the interference filter of the i-th channel to achieve noise compensation without interference signals.

[0035] Preferably, the step of selecting the output signal of the channel with the minimum power as the final output signal is obtained by setting a power discriminator at the output end of each channel and using the power discriminator to output the output signal of the channel with the minimum power.

[0036] In a second aspect, the present invention further provides an airborne early warning radar anti-main lobe unintentional interference device based on frequency domain filtering, which is used to implement the airborne early warning radar anti-main lobe unintentional interference method described in the first aspect. The device includes:

[0037] At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the airborne early warning radar anti-main lobe unintentional interference method described in the first aspect.

[0038] In a third aspect, the present invention further provides a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by one or more processors to complete the method described in the first aspect.

[0039] In a fourth aspect, a chip is provided, including: a processor and an interface, which are used to call and run a computer program stored in a memory and execute the method as described in the first aspect.

[0040] In a fifth aspect, a computer program product containing instructions is provided. When the instructions run on a computer or a processor, the computer or the processor is caused to execute the method as described in the first aspect.

[0041] Based on the sub - array fast - time domain sampling data within multiple pulse repetition periods, the present invention realizes interference spectrum estimation through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion, and realizes the identification of the frequency range of interference signals. Secondly, for the fast - time sampling signals received by each sub - array, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments of the present invention. Obviously, the following - described drawings 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.

[0043] Figure 1 is a schematic flowchart of the first method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0044] Figure 2 is a schematic flowchart of the second method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0045] Figure 3 is a schematic flowchart of the third method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0046] Figure 4 is a schematic flowchart of the fourth method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0047] Figure 5 is a schematic flowchart of the fifth method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0048] Figure 6 is a schematic flowchart of the sixth method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0049] Figure 7 is a schematic diagram of a method for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention;

[0050] Figure 8 is a schematic diagram of the architecture of a device for an airborne early - warning radar to resist main - lobe unintentional interference based on frequency - domain filtering provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, 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.

[0052] Unless the context requires otherwise, throughout the specification and claims, the term "comprising" is to be construed 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 directed 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 suitable manner, that is, although they may be carried in the embodiments or examples of the above terms due to reasons such as the order and position of appearance, but it is not limited that they can be carried by one embodiment or example in a combined manner.

[0053] 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 indicating 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 the same type of individuals and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features.

[0054] In the description of the present invention, there will be a description of the form of "A and / or B" (where A and B are used to formally represent specific feature contents), and the corresponding description includes the following three combinations: only A, only B, and the combination of A and B.

[0055] As used in the present invention, "about", "substantially" or "approximately" 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 measurements being discussed and the errors associated with the measurement of the specific quantity (i.e., the limitations of the measurement system).

[0056] 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.

[0057] Embodiment 1:

[0058] First, Embodiment 1 of the present invention provides a first method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering, which specifically includes: estimating the interference spectrum through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion to obtain the power spectral density estimators of each frequency point; determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point; constructing an interference filter according to the frequency range of the interference signal; using the interference filter to filter out the interference signal to obtain an interference-free signal, and compensating for the color noise caused by the filtering for the interference-free signal to obtain the final output signal. The specific implementation of this method will be described in detail in Embodiment 2 and will not be elaborated here.

[0059] To further optimize the effect of interference suppression, this embodiment also provides a second method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering, as Figure 1 shown, including:

[0060] In step 201, estimate the interference spectrum through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion to obtain the power spectral density estimators of each frequency point; wherein, this embodiment is based on the subarray fast time domain sampling data within multiple pulse repetition periods.

[0061] In step 202, determine the frequency range of the interference signal according to the power spectral density estimators of each frequency point; the frequency range of the interference signal is also called the threshold of the interference signal.

[0062] In step 203, construct multiple channels according to the frequency range of the interference signal, and each channel corresponds to an interference filter.

[0063] In step 204, in each channel, use the interference filter to filter out the interference signal to obtain an interference-free signal, and compensate for the color noise caused by the filtering for the interference-free signal of each channel to obtain the channel output signal of each channel.

[0064] In step 205, select the channel output signal with the minimum power as the final output signal. Among them, the step of selecting the channel output signal with the minimum power as the final output signal is obtained by setting a power discriminator at the output end of each channel and using the power discriminator to output the channel output signal with the minimum power.

[0065] Based on the subarray fast-time domain sampling data within multiple pulse repetition periods, this embodiment realizes interference spectrum estimation through adaptive beamforming and frequency spectrum estimation algorithms based on the maximum likelihood criterion, and realizes the identification of the frequency range of interference signals. Secondly, for the fast-time sampling signals 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.

[0066] Among them, the interference spectrum is estimated 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, as Figure 2 shown, specifically including:

[0067] In step 301, it is determined 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 interference signal of the u-th pulse after adaptive beamforming, where represents the interference signal; is the noise signal, and obeys a Gaussian distribution with a mean of 0 and a variance of .

[0068] 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, which obeys 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; K is the number of groups of received signals.

[0069] In step 303, when the array has received K groups of signals, the power spectral density estimator at the frequency point is calculated as ; where is the amplitude value estimator 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 frequency ; is the estimated value of the noise signal strength at frequency .

[0070] In a specific application scenario, determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point, as Figure 3 shown, specifically includes:

[0071] In step 401, according to 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 .

[0072] In step 402, according to the signal observation model, determine the interference-free power spectral density estimator of frequency point ; obeys an exponential distribution; where the interference-free power spectral density estimator is the power spectral density estimator corresponding to when there is no interference at the corresponding frequency point.

[0073] In step 403, 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.

[0074] In step 404, 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 according to the range of the frequency points with interference, determine the frequency range of the interference signal .

[0075] Among them, determining whether there is interference at each frequency point according to the power spectral density estimators of each frequency point and the reference mean , specifically includes: when the power spectral density estimator of frequency point ​ When it is determined that the frequency point has interference. When the frequency point of the power spectral density estimator is, it is determined that the frequency point has no interference; where P F is the false alarm probability determined according to historical experience, . It is expressed in the form of a mathematical formula as: .

[0076] In a preferred embodiment, according to the frequency range of the interference signal, a plurality of channels are constructed, and each channel corresponds to an interference filter, as Figure 4 shown, specifically including:

[0077] In step 501, according to the frequency range of the interference signal , the reference range is determined as ; where B represents the receiver bandwidth and L represents the number of range cells.

[0078] In step 502, 2 m +1 channels are set, and according to the reference range, the filtering range of the i-th channel is determined as ; where i is the channel number, and m is obtained by those skilled in the art through empirical analysis.

[0079] In step 503, on the basis of the all-pass filter, the amplitude-frequency response within the filtering range of the i-th channel is set 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 a gate function with a width of and a frequency shift of .

[0080] 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, and the output signal of the channel with the minimum power after filtering is the signal with the maximum degree of interference removed.

[0081] In an alternative embodiment, in each channel, the interference filter is used to filter out the interference signal to obtain the interference-free signal of each channel, specifically including:

[0082] When the echo signal of the corresponding range cell received by the subarray of the airborne radar within a pulse repetition period is When the interference signal is filtered by the interference filter of the i-th channel, the spectrum of the interference-free signal of the i-th channel obtained is ; where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0083] 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 Figure 5 shown, which specifically includes:

[0084] In step 601, in the passive mode, the noise power spectral density i of the -th channel is calculated through the amplitude value within the interference-free frequency band range; where represents the length of the frequency band not affected by interference.

[0085] In step 602, according to the noise power spectral density i of the -th channel, compound Gaussian white noise with a power spectral density of i is generated for the -th channel; where the compound Gaussian white noise with a power spectral density of is generated by the computer in a way of generating random numbers obeying Gaussian distribution.

[0086] 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.

[0087] While performing interference suppression and noise compensation in this embodiment, considering that in actual use, the frequency of the interference signal may also drift or there may be a small error between the estimated frequency range of the interference signal and the actual frequency range of the interference signal. To address this problem, this embodiment also sets multiple channels, sets different filters for each channel respectively, and finally selects the channel output signal with the minimum power as the final output signal, so as to reduce the influence brought by the frequency change of the interference signal and the estimation error.

[0088] Embodiment 2:

[0089] Based on Embodiment 1, this embodiment elaborates in detail on the first method for an airborne early warning radar to resist main-lobe unintentional interference based on frequency-domain filtering provided in Embodiment 1. The first method for an airborne early warning radar to resist main-lobe unintentional interference based on frequency-domain filtering is specifically as follows: Based on the maximum likelihood criterion, interference spectrum estimation is performed through adaptive beamforming and frequency spectrum estimation algorithms to obtain the power spectral density estimators of each frequency point; according to the power spectral density estimators of each frequency point, the frequency range of the interference signal is determined; according to the frequency range of the interference signal, an interference filter is constructed; the interference filter is used to filter out the interference signal to obtain an interference-free signal, and for the colored noise caused by filtering, noise compensation is performed on the interference-free signal to obtain the final output signal.

[0090] Among them, the interference spectrum estimation based on the maximum likelihood criterion through adaptive beamforming and frequency spectrum estimation algorithms to obtain the power spectral density estimators of each frequency point; and the specific implementation of determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point are implemented based on the same concept as in Embodiment 1 and will not be elaborated here. The construction of the interference filter according to the frequency range of the interference signal is specifically as follows:

[0091] On the basis of identifying the frequency range of the interference signal further set the filtering range of the interference filter to be:

[0092]

[0093] where B represents the receiver bandwidth and L represents the number of range cells.

[0094] Based on the all-pass filter, set the amplitude-frequency response within the filtering 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:

[0095]

[0096] where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0097] Construct the interference filter such that the response of the interference filter within the filtering range is 0, that is:

[0098]

[0099] where is the bandwidth of the filtering range; is the center frequency of the interference signal; is a gate function with a width of and a frequency shift of .

[0100] Therefore, the spectrum of the output signal after frequency-domain filtering (i.e., the interference-free signal) is:

[0101]

[0102] Regarding the chromatic noise caused by filtering, noise compensation is performed on the interference-free signal, specifically including:

[0103] In the passive mode, the noise power spectral density is estimated using the power spectral density within the interference-free frequency band range, that is:

[0104]

[0105] where represents the length of the frequency band not affected by interference.

[0106] Since in the passive mode, the noise power spectral density has been estimated through the amplitude values within the interference-free frequency band range, that is ; where, represents the length of the frequency band not affected by interference.

[0107] According to the estimated power spectral density , a Gaussian white noise signal with a power spectral density of is simulated and generated within the system.

[0108] A noise filter is constructed, and its amplitude-frequency response has the following relationship with the interference filter :

[0109]

[0110] The simulated Gaussian white noise signal is passed through the noise filter and then superimposed on the output channel of the interference filter to perform noise compensation on the interference-free signal.

[0111] Example 3:

[0112] Based on the method described in Example 1, the present invention combines specific application scenarios and uses technical expressions in relevant scenarios to elaborate on the implementation process in the characteristic scenarios of the present invention.

[0113] The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency-domain filtering provided in this embodiment specifically includes:

[0114] 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 frequency range of the interference signal is identified by using CFAR to determine the threshold; 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, set 2 m +1 groups of channels composed of band-stop filter h 1,i and band-pass filter h 2,i The 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. Where B represents the receiver bandwidth and L represents the number of range cells. Specifically as Figure 6 and Figure 7 shown, including the following steps:

[0115] In step 701, the frequency range of the interference signal is identified, specifically including: 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:

[0116]

[0117] 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 Gaussian distribution with a mean of 0 and a variance of .

[0118] Then, after performing a mixed-radix FFT transformation on this signal, we get .

[0119] Since the interference pattern is narrowband interference, the interference signal only has peaks at limited frequency points in the frequency domain, while the noise is Gaussian white noise and 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, the observation model at the frequency point is:

[0120]

[0121] where is the u th observation at the frequency The observed value of the noise signal strength at a certain point, which follows a complex Gaussian distribution with a mean of 0 and a variance of , that is , is the observed value at the th observation at frequency .

[0122] According to the maximum likelihood estimation, the estimator of the signal at the frequency point is:

[0123]

[0124] 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 frequency , is the estimated value of the noise signal strength at frequency .

[0125] Then the power spectral density of the signal is:

[0126]

[0127] Based on the power spectral density estimation model, the above interference frequency band estimation problem can be transformed into a typical binary hypothesis testing problem, and its observation model can be expressed as:

[0128]

[0129] Since when there is no interference signal at the frequency point , its power spectral density estimator is:

[0130]

[0131] Then follows an exponential distribution. According to the CFAR principle, set the power spectral density estimator of the frequency point as the detection unit, the power spectral density estimator of the frequency point as the protection unit, and the power spectral density estimator of the frequency point as the reference unit. According to the power spectral density estimator of the reference unit, determine the reference mean , 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; the false alarm probability P is determined according to historical experience F After that, it is determined whether there is interference in the detection unit through the following formula:

[0132]

[0133] where .

[0134] Each frequency point is detected separately, and the frequency range of the interference signal can be obtained. In order to reduce the estimation error caused by the spectral resolution, on the basis of identifying the frequency range of the interference signal , the interference frequency range is further set as:

[0135]

[0136] where B represents the receiver bandwidth and L represents the number of range cells.

[0137] In step 702, the noise power spectral density is estimated, which specifically includes: in the passive mode, the power spectral density in the interference-free frequency band range of the i th channel is used to estimate the noise power spectral density of this channel, that is:

[0138]

[0139] where represents the length of the frequency band not affected by interference.

[0140] In step 703, frequency domain filtering is performed, which specifically includes: according to the estimated interference frequency range, on the basis of the all-pass filter, the amplitude-frequency response in the interference signal frequency range is set to zero. In the active mode, assuming that the echo signal of a certain range cell received by the subarray of the airborne radar within a pulse repetition period is:

[0141]

[0142] where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0143] Construct the interference filter for the th channel , and through the estimated interference signal frequency range

[0144]

[0145] where is the filtering bandwidth; is the center frequency of the interference signal; is with a width of and a frequency shift of is the gate function.

[0146] Therefore, the output signal spectrum of the i-th channel after frequency domain filtering is:

[0147]

[0148] In step 704, noise compensation is performed, which specifically includes: To eliminate the influence of color noise, a noise compensation link is added, which specifically includes:

[0149] 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 ; where represents the length of the frequency band not affected by interference.

[0150] According to the estimated power spectral density for the i-th channel, corresponding Gaussian white noise is simulated and generated within the system.

[0151] Construct a noise filter whose amplitude-frequency response has the following relationship with the interference filter :

[0152]

[0153] Pass the simulated Gaussian white noise signal through the noise filter and then superimpose it on the output channel of the interference filter .

[0154] In step 705, multi-channel decision-making is performed to obtain the output signal, which specifically includes: 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.

[0155] This embodiment provides a method for suppressing the main lobe unintentional interference of an airborne radar based on frequency domain filtering. 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, for the fast time sampling signals received by each subarray, interference suppression is performed through frequency domain filtering; thirdly, the filtered output noise is compensated; finally, multi-channel decision-making is performed to complete the suppression of the main lobe unintentional interference, improving the target detection performance of the airborne radar.

[0156] This embodiment can achieve accurate estimation of interference parameters; moreover, the fast-time frequency-domain filtering technology proposed in this embodiment realizes effective suppression of narrowband interference signals with different modulation methods without changing the length of fast-time sampling data; furthermore, through noise compensation processing, the influence of colored noise on the target detection performance after filtering processing is effectively solved.

[0157] Embodiment 4:

[0158] As Figure 8 shown, it is a schematic structural diagram of the airborne early warning radar anti-main-lobe unintentional interference device based on frequency-domain filtering according to an embodiment of the present invention. The airborne early warning radar anti-main-lobe unintentional interference device based on frequency-domain filtering in this embodiment includes one or more processors 21 and a memory 22. Among them, Figure 8 one processor 21 is taken as an example in

[0159] The processor 21 and the memory 22 can be connected through a bus or other means, Figure 8 and taking the connection through a bus as an example in

[0160] The memory 22, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs and non-volatile computer-executable programs, such as the airborne early warning radar anti-main-lobe unintentional interference method in Embodiment 1. The processor 21 executes the airborne early warning radar anti-main-lobe unintentional interference method by running the non-volatile software programs and instructions stored in the memory 22.

[0161] The memory 22 may include a high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 22 may optionally include a memory remotely provided with respect to the processor 21, and these remote memories can be connected to the processor 21 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0162] The program instructions / modules are stored in the memory 22 and, when executed by the one or more processors 21, execute the airborne early warning radar anti-main-lobe unintentional interference method in Embodiment 1 above.

[0163] It should be noted that for the information interaction, execution process, etc. between the modules and units in the above-mentioned device and system, since they are 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.

[0164] 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 disks or optical discs, etc.

[0165] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An airborne early warning radar anti-main lobe unintentional interference method based on frequency domain filtering, characterized in that Including: Performing interference spectrum estimation through an adaptive beamforming and frequency spectrum estimation algorithm 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 a plurality of channels according to the frequency range of the interference signal, with each channel corresponding to an interference filter; In each channel, filtering the interference signal using the interference filter to obtain the interference-free signals of each channel, and performing noise compensation on the interference-free signals of each channel for the colored noise caused by the filtering to obtain the channel output signals of each channel; Selecting the channel output signal with the minimum power as the final output signal.

2. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 1, wherein The performing interference spectrum estimation through an adaptive beamforming and frequency spectrum estimation algorithm based on the maximum likelihood criterion to obtain the power spectral density estimators at each frequency point specifically includes: 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 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 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 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 .

3. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 2, wherein The determining the frequency range of the interference signal according to the power spectral density estimators at each frequency point specifically includes: Based on the estimated 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 's power spectral density estimator is the detection unit, and the frequency point 's power spectral density estimator is the protection unit, and the frequency point 's 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 based on the range of the frequency points with interference, determine the frequency range of the interference signal .

4. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 3, characterized in that Based on the power spectral density estimation of each frequency point and the reference mean value , determine whether there is interference at each frequency point, specifically including: When the frequency point is the power spectral density estimator it is determined that there is interference at the frequency point ; When the frequency point power spectral density estimator is such that it is determined that the frequency point has no interference; where P F is the false alarm probability determined based on historical experience, .

5. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 1, wherein The constructing a plurality of channels according to the frequency range of the interference signal, with each channel corresponding to an interference filter specifically includes: 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; Setting 2 m +1 channel, and according to the reference range, determine the filtering range of the i-th channel as ; Based on 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 for the i-th channel, that is, the interference filter for 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 a rectangular function with a width of and a frequency shift of .

6. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 5, wherein The filtering the interference signal using the interference filter to obtain the interference-free signals of each channel in each channel specifically includes: 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 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.

7. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 5, characterized in that The performing noise compensation on the interference-free signals of each channel for the colored noise caused by the filtering to obtain the channel output signals of each channel specifically includes: In the passive mode, calculate the noise power spectral density of the i th channel through the amplitude values within the interference-free frequency band range ; where represents the length of the frequency band not affected by interference According to the noise power spectral density of the i th channel , generate compound Gaussian white noise with a power spectral density of i for the th channel; Construct a noise filter for the i th channel ; among which, Pass the said compound Gaussian white noise through the noise filter of the i th channel and then superimpose it on the output channel of the interference filter of the i th channel to achieve noise compensation without interference signals.

8. The method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to claim 1, wherein The selecting the channel output signal with the minimum power as the final output signal is obtained by setting a power decision maker at the output ends of each channel and using the power decision maker to output the channel output signal with the minimum power.

9. A non-volatile computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, and these computer-executable instructions are executed by one or more processors to complete the method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to any one of claims 1-8.

10. An airborne early warning radar anti-main lobe unintentional interference device based on frequency domain filtering, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the processor to execute the method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering according to any one of claims 1-8.

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