Airborne early warning radar main lobe unintentional interference resisting method and device based on frequency domain filtering

Abstract

The invention relates to the technical field of signal processing, and provides an airborne early warning radar main lobe unintentional interference resisting method and device based on frequency domain filtering. The method comprises the following steps: performing interference spectrum estimation through adaptive beam forming and a frequency spectrum estimation algorithm based on a maximum likelihood criterion to obtain a power spectrum density estimator of each frequency point; determining a frequency range of the interference signal according to the power spectral density estimator of each frequency point; a plurality of channels are constructed according to the frequency range of the interference signal, and each channel corresponds to an interference filter; in each channel, filtering the interference signal by using an interference filter to obtain a non-interference signal of each channel, and for color noise caused by filtering, performing noise compensation on the non-interference signal of each channel to obtain a channel output signal of each channel; and selecting the channel output signal with the minimum power as a final output signal. According to the method, main lobe unintentional interference suppression is completed, and the target detection performance of the airborne radar is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular, 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, effectively solve 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 operating 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, spatial domain, frequency domain, polarization domain, and deep learning. However, the above 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 coexist. 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: 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: 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 estimator of each frequency point; Determining the frequency range of the interference signal according to the power spectral density estimator of 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, an interference filter is used to filter out the interference signals, and the interference-free signals of each channel are obtained. For the color 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; Select the channel output signal with the minimum power as the final output signal.

[0007] 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: 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 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; 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 .

[0008] Preferably, determining the frequency range of the interference signal according to the power spectral density estimators of each frequency point specifically includes: Based on the power spectral density estimators of the signal , establish the signal observation model as ; where represents the frequency point without interference, represents the frequency point with interference; 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; 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 value 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; According to 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 .

[0009] 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 value specifically includes: When the power spectral density estimator of the frequency point is greater than , it is determined that there is interference at the frequency point When the power spectral density estimator of the frequency point is less than , it is determined that there is no interference at the frequency point F is the false alarm probability determined according to historical experience, .

[0010] Preferably, according to the frequency range of the interference signal, multiple channels are constructed, and each channel corresponds to an interference filter, specifically including: 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; Set 2 m +1 channels, and according to the reference range, the filtering range of the i-th channel is determined as ; 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 window function with a width of and a frequency shift of .

[0011] 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: 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.

[0012] 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: 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 ; wherein, ; passing the compound Gaussian white noise through the noise filter of the i-th channel and then superimposing it on the output channel of the interference filter of the i-th channel to achieve noise compensation for the interference-free signal.

[0013] Preferably, the output signal of the channel with the minimum power is selected as the final output signal 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.

[0014] In a second aspect, the present invention also provides an airborne early warning radar main lobe unintentional interference suppression device based on frequency domain filtering, which is used to implement the airborne early warning radar main lobe unintentional interference suppression method described in the first aspect. The device includes: 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 main lobe unintentional interference suppression method described in the first aspect.

[0015] In a third aspect, the present invention also 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.

[0016] 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 from the memory and execute the method as described in the first aspect.

[0017] In a fifth aspect, a computer program product containing instructions is provided, and 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.

[0018] Based on the subarray 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 subarray, interference suppression is performed through frequency domain filtering; again, 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. Description of the Drawings

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0020] 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; 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; 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; 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; 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; 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; 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; 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 implementation manners

[0021] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, 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.

[0022] Unless the context otherwise requires, 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 - mentioned terms do not necessarily refer 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 - mentioned terms due to reasons such as the order and position of appearance, there is no limitation that they can be carried by one embodiment or example in a combined manner.

[0023] In the description of the present invention, the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, 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 stated, 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 should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features.

[0024] In the description of the present invention, there will be a description of the form "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.

[0025] 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).

[0026] 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.

[0027] Example 1: 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.

[0028] 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: 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; where this embodiment is based on the subarray fast time domain sampling data within multiple pulse repetition periods.

[0029] 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.

[0030] In step 203, construct multiple channels according to the frequency range of the interference signal, and each channel corresponds to an interference filter.

[0031] 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.

[0032] In step 205, select the channel output signal with the minimum power as the final output signal. Among them, the selection of 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.

[0033] This embodiment is based on the subarray fast time domain sampling data within multiple pulse repetition periods, 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 the interference signal; 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.

[0034] Among them, the interference spectrum is estimated based on the maximum likelihood criterion through adaptive beamforming and frequency spectrum estimation algorithms to obtain the power spectral density estimators at each frequency point, as Figure 2 shown, and specifically includes: 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 .

[0035] In step 302, after performing a mixed-radix FFT transform on , the observation model at the frequency point is ; where is the observed value of the noise signal strength 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.

[0036] 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 the frequency ; is the estimated value of the noise signal strength at the frequency .

[0037] In a specific application scenario, determining the frequency range of the interference signal according to the power spectral density estimators at each frequency point, as Figure 3 shown, specifically includes: In step 401, 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.

[0038] In step 402, according to the signal observation model, the estimated value of the power spectral density without interference at the frequency point is determined ; obeys an exponential distribution; where the estimated value of the power spectral density without interference is the estimated value of the power spectral density corresponding to the case where there is no interference at the corresponding frequency point.

[0039] In step 403, the estimated value of the power spectral density at the frequency point is set as the detection unit, the estimated value of the power spectral density at the frequency point is set as the protection unit, and the estimated value of the power spectral density at the frequency point is set as the reference unit. According to the estimated value of the power spectral density of the reference unit, the reference mean value is determined, and the expression is ; where is the estimated value of the power spectral density at the frequency point , is the estimated value of the power spectral density at the frequency point , is the length of the protection unit, is the length of the reference unit.

[0040] In step 404, according to the estimated values of the power spectral density at each frequency point and the reference mean value , it is determined whether there is interference at each frequency point, and according to the range of the frequency points with interference, the frequency range of the interference signal is determined .

[0041] Among them, the determination of whether there is interference at each frequency point according to the estimated values of the power spectral density at each frequency point and the reference mean value specifically includes: when the estimated value of the power spectral density at the frequency point is , it is determined that there is interference at the frequency point , and when the estimated value of the power spectral density at the frequency point is , it is determined that there is no interference at the frequency point ; where P F is the false alarm probability determined according to historical experience, . It is expressed in the form of a mathematical formula as: 。

[0042] 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: 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.

[0043] 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.

[0044] 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 .

[0045] 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.

[0046] 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: 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.

[0047] In a specific application scenario, for the color 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: In step 601, in the passive mode, the noise power spectral density of the i 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.

[0048] In step 602, according to the noise power spectral density of the i 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 the Gaussian distribution.

[0049] 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.

[0050] 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 be able to reduce the influence brought by the frequency change of the interference signal and the estimation error.

[0051] Embodiment 2: 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: interference spectrum estimation is performed 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; the frequency range of the interference signal is determined according to the power spectral density estimators at each frequency point; an interference filter is constructed according to the frequency range of the interference signal; the interference filter is used to filter out the interference signal to obtain an interference-free signal, and noise compensation is performed on the interference-free signal for the colored noise caused by filtering to obtain the final output signal.

[0052] Among them, the implementation of 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 and determining the frequency range of the interference signal according to the power spectral density estimators at each frequency point is based on the same concept as that 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: On the basis of identifying the frequency range of the interference signal further set the filtering range of the interference filter to be:

[0053] where B represents the receiver bandwidth and L represents the number of range cells.

[0054] On the basis of 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:

[0055] where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0056] Construct the interference filter such that the response of the interference filter within the filtering range is 0, that is:

[0057] where is the bandwidth of the filtering range; is the center frequency of the interference signal; is a rectangular function with a width of and a frequency shift of .

[0058] Therefore, the spectrum of the output signal after frequency-domain filtering (i.e., the interference-free signal) is as follows:

[0059] Regarding the color noise caused by filtering, noise compensation is performed on the interference-free signal, which specifically includes: 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:

[0060] where represents the length of the frequency band not affected by interference.

[0061] 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.

[0062] 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.

[0063] A noise filter is constructed, and its amplitude-frequency response has the following relationship with the interference filter :

[0064] 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.

[0065] Example 3: 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.

[0066] 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: 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 up so that the output noise still follows a Gaussian distribution. To reduce the influence of 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 a band-stop filter h 1,i and a 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: 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:

[0067] where is the output signal of the first pulse's interference signal after adaptive beamforming, is the noise signal, and the noise signal follows a Gaussian distribution with a mean of 0 and a variance of .

[0068] Then, after performing a mixed-radix FFT transformation on this signal, we get .

[0069] Since the interference pattern is narrowband interference, the interference signal only appears as 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:

[0070] where is the observed value of the noise signal intensity at the frequency u in the th observation, and it 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 .

[0071] According to the maximum likelihood estimation, the estimator of the signal at frequency is:

[0072] where 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 .

[0073] Then the power spectral density of the signal is:

[0074] 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:

[0075] Since when there is no interference signal at frequency , the estimator of its power spectral density is:

[0076] Then follows an exponential distribution. According to the CFAR principle, set the estimator of the power spectral density at frequency as the detection unit, the estimator of the power spectral density at frequency as the protection unit, and the estimator of the power spectral density at frequency as the reference unit. According to the estimator of the power spectral density of the reference unit, determine the reference mean , and the expression is , where is the estimator of the power spectral density at frequency , is the estimator of the power spectral density at frequency , is the length of the protection unit, is the length of the reference unit; after determining the false alarm probability P F according to historical experience, determine whether there is interference in the detection unit through the following formula:

[0077] Among them 。

[0078] By detecting each frequency point respectively, the frequency range of the interference signal can be obtained. To reduce the estimation error caused by the spectral resolution, based on the identified frequency range of the interference signal further set the interference frequency range as:

[0079] where B represents the receiver bandwidth and L represents the number of range cells.

[0080] In step 702, the noise power spectral density is estimated, specifically including: in the passive mode, using the power spectral density within the interference-free frequency band of the i th channel to estimate the noise power spectral density of this channel, that is:

[0081] where represents the length of the frequency band not affected by interference.

[0082] In step 703, frequency domain filtering is performed, specifically including: according to the estimated interference frequency range, on the basis of the all-pass filter, setting the amplitude-frequency response within the interference signal frequency range 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:

[0083] where is the target signal, is the interference signal, is the clutter signal, is the noise signal.

[0084] Construct the interference filter of the i-th channel , by estimating the frequency range of the interference signal , making the response of the interference filter zero within this frequency range, that is:

[0085] where 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 .

[0086] Therefore, the output signal spectrum of the i-th channel after frequency domain filtering is:

[0087] In step 704, noise compensation is performed, specifically including: to eliminate the influence of color noise, a noise compensation link is added, specifically including: Since in the passive mode, the noise power spectral density of the i-th channel 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.

[0088] According to the estimated power spectral density , for the i-th channel, Gaussian white noise is simulated and generated within the system.

[0089] Construct a noise filter , whose amplitude-frequency response and that of the interference filter are related as:

[0090] The Gaussian white noise signal obtained through simulation is passed through the noise filter and then superimposed on the output channel of the interference filter .

[0091] In step 705, multi-channel decision is performed to obtain the output signal, specifically including: 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.

[0092] 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 realized 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 is performed to complete the suppression of the main lobe unintentional interference, improving the target detection performance of the airborne radar.

[0093] 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 the fast-time sampling data; and also, through noise compensation processing, the influence of color noise on the target detection performance after filtering processing is effectively solved.

[0094] Embodiment 4: As Figure 8As shown in the figure, it is a schematic structural diagram of an 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 Take one processor 21 as an example.

[0095] The processor 21 and the memory 22 can be connected through a bus or other means, Figure 8 Take the connection through the bus as an example.

[0096] 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 method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering in Embodiment 1. The processor 21 executes the method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering by running the non-volatile software programs and instructions stored in the memory 22.

[0097] The memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic 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 relative to the processor 21, and these remote memories can be connected to the processor 21 through a network. Examples of the above networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The program instructions / modules are stored in the memory 22, and when executed by the one or more processors 21, they execute the method for an airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering in Embodiment 1 above.

[0099] It should be noted that for the information interaction, execution process, etc. between the modules and units in the above 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.

[0100] 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 this program can be stored in a computer-readable storage medium. The storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc.

[0101] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering, characterized in that: include: Based on the maximum likelihood criterion, the interference spectrum is estimated through adaptive beamforming and frequency spectrum estimation algorithm to obtain the power spectrum density estimate of each frequency point; Determine the frequency range of the interference signal based on the power spectrum density estimation of each frequency point; According to the frequency range of the interference signal, a plurality of channels are constructed, each channel corresponding to an interference filter; In each channel, an interference filter is used to filter out the interference signal to obtain the interference-free signal of each channel. The interference-free signal of each channel is compensated for the color noise caused by filtering to obtain the channel output signal of each channel. The channel output signal with the smallest power is selected as the final output signal.

2. The method for anti-main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 1 is characterized in that: The interference spectrum estimation is performed based on the maximum likelihood criterion through adaptive beamforming and frequency spectrum estimation algorithm to obtain the power spectrum density estimation of each frequency point, specifically including: Determine in a Gaussian white noise environment, u After the pulse signal is adaptively beamformed, the output signal corresponding to the corresponding spatial angle is ;in, For the u The output signal of the interference signal of a pulse after adaptive beamforming; is a noise signal, and The mean is 0 and the variance is Gaussian distribution of right After mixed basis FFT transformation, we get The observation model at is ;in, For the u In the observations, the frequency The observed value of the noise signal strength at , which has a mean of 0 and a variance of The complex Gaussian distribution of ; For the u In the observations, the frequency The observed value of the interference signal strength at K is the number of groups of received signals; When the array receives K When the group signal is calculated, the frequency The power spectral density estimator at is ;in, For the frequency The amplitude estimate at , ; The mean is , the variance is The complex Gaussian distribution of ; For the frequency An estimate of the interference signal strength at For the frequency An estimate of the noise signal strength at .

3. The method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 2 is characterized in that: Determining the frequency range of the interference signal according to the power spectrum density estimate of each frequency point specifically includes: According to the power spectral density estimate of the signal , the signal observation model is established as ;in, Representative frequency No interference, Representative frequency There is interference; According to the signal observation model, the frequency point is determined The interference-free power spectral density estimator ; Obeying exponential distribution; wherein the interference-free power spectrum density estimator is the power spectrum density estimator corresponding to the absence of interference at the corresponding frequency point; Set frequency The power spectral density estimator is the detection unit, frequency point The power spectral density estimator is the protection unit, the frequency point The power spectral density estimator of the reference unit is used as the reference unit. According to the power spectral density estimator of the reference unit, the reference mean is determined , the expression is ;in, For the frequency The power spectral density estimator of For the frequency The power spectral density estimator of is the length of the protection unit, is the length of the reference unit; According to the power spectrum density estimate 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 based on the range of the frequency points where interference exists .

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

5. The method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 1 is characterized in that: The step of constructing a plurality of channels according to the frequency range of the interference signal, wherein each channel corresponds to an interference filter, specifically includes: According to the frequency range of the interference signal , determine the benchmark range as ; Where B represents the receiver bandwidth and L represents the number of range units; Setup 2 m +1 channel, according to the reference range, the filtering range of the i-th channel is determined to be ; 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 ;in, is the bandwidth of the reference range, ; is the center frequency of the filtering range of the i-th channel, ; The width is , the frequency shift is The gate function.

6. The method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 5 is characterized in that: In each channel, an interference filter is used to filter out the interference signal to obtain an interference-free signal of each channel, specifically including: When the echo signal of the corresponding range unit received by the subarray of the airborne radar within a pulse repetition period is When the interference signal is filtered out by the interference filter of the i-th channel, the spectrum of the interference-free signal of the i-th channel is ;in, is the target signal, is the interference signal, is the clutter signal, is a noise signal.

7. The method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 5 is characterized in that: The method of 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 specifically includes: In passive mode, the amplitude value within the interference-free frequency band is used to calculate the i The noise power spectral density of the channels ;in, Indicates the length of the frequency band not affected by interference; According to the said i The noise power spectral density of the channels , for i The power spectral density generated by each channel is Composite Gaussian white noise; For the i Channel noise filter ;in, ; The composite Gaussian white noise is passed through the i Noise filter for each channel Then superimpose to i Interference filter for each channel output channels to achieve noise compensation without interfering signals.

8. The method for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering according to claim 1 is characterized in that: The selection of the channel output signal with the minimum power as the final output signal is obtained by setting a power determiner at the output end of each channel and using the power determiner 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, which are executed by one or more processors to complete the method for airborne early warning radar to resist main lobe unintentional interference based on frequency domain filtering as described in any one of claims 1-8.

10. A device for preventing main lobe unintentional interference of airborne early warning radar based on frequency domain filtering, characterized in that: include: 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 anti-main lobe unintentional interference of airborne early warning radar based on frequency domain filtering as described in any one of claims 1-8.

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