阅读说明：本技术 一种无人机载雷达主瓣干扰抑制方法、装置及存储介质 (Unmanned aerial vehicle radar main lobe interference suppression method and device and storage medium ) 是由 陈曾平 吴建新 张磊 徐世友 胡刘博 于 2021-05-25 设计创作，主要内容包括：本发明公开了一种无人机载雷达主瓣干扰抑制方法、装置及存储介质,该方法包括多个分布式无人机载雷达同时对空间目标进行探测,获得回波数据；从回波数据中选取样本数据,样本数据为全部距离单元的回波数据；计算样本数据的第一协方差矩阵；根据样本数据的第一协方差矩阵,计算样本数据中每个距离单元的第一广义内积值；根据第一广义内积值,提取样本数据中的奇异样本数据；剔除奇异样本数据；获取剔除后的样本数据作为训练样本对干扰信息进行估计。本发明通过剔除奇异样本可以对干扰信息进行抑制进而使得目标检测更加准确；使用广义内积结果对目标进行检测可以大大减少由于自适应处理带来计算量。本发明可广泛应用于雷达信号处理技术领域。(The invention discloses a method, a device and a storage medium for suppressing interference of a main lobe of an unmanned airborne radar, wherein the method comprises the steps that a plurality of distributed unmanned airborne radars simultaneously detect a space target to obtain echo data; selecting sample data from the echo data, wherein the sample data is the echo data of all the distance units; calculating a first covariance matrix of the sample data; calculating a first generalized inner product value of each distance unit in the sample data according to a first covariance matrix of the sample data; extracting singular sample data in the sample data according to the first generalized inner product value; removing singular sample data; and acquiring the removed sample data as a training sample to estimate the interference information. According to the method, the interference information can be inhibited by removing singular samples, so that the target detection is more accurate; the generalized inner product result is used for detecting the target, so that the calculation amount caused by self-adaptive processing can be greatly reduced. The invention can be widely applied to the technical field of radar signal processing.)

1. A main lobe interference suppression method for an unmanned airborne radar is characterized by comprising the following steps:

a plurality of distributed unmanned airborne radars simultaneously detect a space target to obtain echo data;

selecting sample data from the echo data, wherein the sample data is echo data of all distance units;

calculating a first covariance matrix of the sample data;

calculating a first generalized inner product value of each distance unit in the sample data according to the first covariance matrix of the sample data;

extracting singular sample data in the sample data according to the first generalized inner product value, wherein the singular sample data is data of interference targets which are not uniformly distributed on an echo distance unit;

removing the singular sample data;

and acquiring the removed sample data as a training sample to estimate the interference information.

2. The method for suppressing interference of main lobe of unmanned airborne radar according to claim 1, wherein after the distributed unmanned airborne radar detects the spatial target at the same time and obtains the echo data, the method further comprises:

preprocessing the echo data, the preprocessing including at least one of pulse compression processing and pulse Doppler processing.

3. The method according to claim 1, wherein the calculating the first covariance matrix of the sample data is performed by the following equation:

R_X＝E[XX^H]；

in the formula, R_XA first covariance matrix, E [ alpha ], [ alpha ] representing the sample data]The mean operation is represented, H represents the conjugate transpose of the first covariance matrix, and X represents the sample data matrix after preprocessing.

4. The method according to claim 1, wherein the calculating a first generalized inner product value of each range bin in the sample data according to the first covariance matrix of the sample data is performed by the following formula:

in the formula, GIP_iA first generalized inner product value, X, representing the ith distance cell_iRepresents the data vector of the ith distance unit, H represents the conjugate transpose of the first covariance matrix,a first covariance matrix R representing the sample data_XThe inverse of (c).

5. The method according to claim 1, wherein the step of rejecting the singular sample data comprises:

setting a first elimination threshold value;

and rejecting all sample data of which the first generalized inner product value is larger than the first rejection threshold value.

6. The method according to claim 1, wherein the step of rejecting the singular sample data comprises:

setting a first numerical value, wherein the first numerical value is the number of rejected samples;

sorting the first generalized inner product values from large to small; obtaining an ordered list;

and removing sample data corresponding to the first generalized inner product value of the first numerical value in the sorted list.

7. The method for suppressing interference of main lobe of unmanned aerial vehicle-mounted radar according to claim 1, wherein after said removing said singular sample data, said method further comprises:

taking the sample data without the singular sample data as a training sample;

calculating a second covariance matrix of the training samples;

calculating a second generalized inner product value of each distance unit in the training sample according to the second covariance matrix;

and carrying out target detection according to the second generalized inner product value.

8. The method for suppressing interference of main lobe of unmanned airborne radar according to claim 1, wherein said method further comprises:

when the number of the singular sample data is larger than a first threshold value, extracting first data from the singular sample data, wherein the first data is the sample data of which the first generalized inner product value is larger than the elimination threshold value;

calculating a third covariance matrix of the first data;

calculating a third generalized inner product value of each distance unit in the first data according to the third covariance matrix;

setting a second elimination threshold value;

removing all data with the third generalized inner product value larger than the second removal threshold value in the first data to obtain second data;

calculating a fourth covariance matrix of the second data;

calculating a fourth generalized inner product value of each distance unit in the second data according to the fourth covariance matrix;

and carrying out target detection according to the fourth generalized inner product value.

9. An unmanned airborne radar mainlobe interference suppression device, comprising:

at least one processor;

at least one memory for storing at least one program;

when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1-8.

10. Computer-readable storage medium, on which a processor-executable program is stored, which, when being executed by a processor, is adapted to carry out the method according to any one of claims 1-8.

Technical Field

The invention relates to the technical field of radar signal processing, in particular to a method and a device for suppressing interference of a main lobe of an unmanned aerial vehicle radar and a storage medium.

Background

When the airborne radar detects the target, except the interested target object, inevitable non-interested clouds, rain or ground objects and the like can also reflect radar signals in a detection scene, various interferences intentionally released by an enemy can also exist in a battle scene, and the interferences can influence the correct detection of the target. In order to successfully detect the target, echo data detected by the radar needs to be processed from a time domain, a space domain, a frequency domain or a polarization domain, and the detection and the identification of the target are completed by suppressing non-interesting noise, clutter and interference or enhancing the information of the target in the echo. The distributed array can utilize the difference of the target and the interference in a space angle, self-adaptive suppression is carried out on the interference in a space domain, meanwhile, the echo data of each array element in the array are accumulated to complete the enhancement of target information, meanwhile, the length of a radar antenna baseline is equivalently increased due to the distributed array, the interference in a main lobe range of a single-node short baseline radar can be changed into the interference in a side lobe for distribution, so that the main lobe interference to the radar in the traditional sense can be well suppressed, and the problems of main lobe distortion, target signal cancellation and the like when the self-adaptive suppression is carried out on the main lobe interference can not be generated.

Most interference suppression algorithms need to know the information of interference to complete suppression. The samples selected for estimating the information of the interference are called training samples. In the training sample, there are data such as moving objects, false target interference, etc. which are not uniformly distributed on the echo range unit, and these data are called singular samples. The presence of singular samples can cause errors in the information estimation (where interference refers to active noise suppression interference distributed relatively uniformly on range cells, and these interferences can raise the noise floor of the echo, which is the main interference affecting target detection), fail to obtain an ideal interference suppression effect, and may even cause cancellation of target signals.

Disclosure of Invention

The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method and a device for suppressing interference of a main lobe of an unmanned airborne radar and a storage medium.

The technical scheme adopted by the invention is as follows:

in one aspect, an embodiment of the present invention includes a method for suppressing interference of a main lobe of an unmanned aerial vehicle, including:

a plurality of distributed unmanned airborne radars simultaneously detect a space target to obtain echo data;

selecting sample data from the echo data, wherein the sample data is echo data of all distance units;

calculating a first covariance matrix of the sample data;

calculating a first generalized inner product value of each distance unit in the sample data according to the first covariance matrix of the sample data;

extracting singular sample data in the sample data according to the first generalized inner product value, wherein the singular sample data is data of interference targets which are not uniformly distributed on an echo distance unit;

removing the singular sample data;

and acquiring the removed sample data as a training sample to estimate the interference information.

Further, after the multiple distributed unmanned airborne radars simultaneously detect the spatial target and obtain the echo data, the method further includes:

preprocessing the echo data, the preprocessing including at least one of pulse compression processing and pulse Doppler processing.

Further, the computing the first covariance matrix of the sample data is performed by:

R_X＝E[XX^H]；

in the formula, R_XA first covariance matrix, E [ alpha ], [ alpha ] representing the sample data]The mean operation is represented, H represents the conjugate transpose of the first covariance matrix, and X represents the sample data matrix after preprocessing.

Further, said calculating a first generalized inner product value of each distance unit in the sample data according to the first covariance matrix of the sample data is performed by the following formula:

in the formula, GIP_iA first generalized inner product value, X, representing the ith distance cell_iRepresents the data vector of the ith distance unit, H represents the conjugate transpose of the first covariance matrix,a first covariance matrix R representing the sample data_XThe inverse of (c).

Further, the step of rejecting the singular sample data includes:

setting a first elimination threshold value;

and rejecting all sample data of which the first generalized inner product value is larger than the first rejection threshold value.

Further, the step of rejecting the singular sample data includes:

setting a first numerical value, wherein the first numerical value is the number of rejected samples;

sorting the first generalized inner product values from large to small; obtaining an ordered list;

and removing sample data corresponding to the first generalized inner product value of the first numerical value in the sorted list.

Further, after the removing the singular sample data, the method further includes:

taking the sample data without the singular sample data as a training sample;

calculating a second covariance matrix of the training samples;

calculating a second generalized inner product value of each distance unit in the training sample according to the second covariance matrix;

and carrying out target detection according to the second generalized inner product value.

Further, the method further comprises:

when the number of the singular sample data is larger than a first threshold value, extracting first data from the singular sample data, wherein the first data is the sample data of which the first generalized inner product value is larger than the elimination threshold value;

calculating a third covariance matrix of the first data;

calculating a third generalized inner product value of each distance unit in the first data according to the third covariance matrix;

setting a second elimination threshold value;

removing all data with the third generalized inner product value larger than the second removal threshold value in the first data to obtain second data;

calculating a fourth covariance matrix of the second data;

calculating a fourth generalized inner product value of each distance unit in the second data according to the fourth covariance matrix;

and carrying out target detection according to the fourth generalized inner product value.

On the other hand, the embodiment of the invention also comprises an interference suppression device for the main lobe of the unmanned airborne radar, which comprises the following components:

at least one processor;

at least one memory for storing at least one program;

when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method for suppressing interference of the main lobe of the unmanned airborne radar.

In another aspect, the embodiment of the present invention further includes a computer-readable storage medium, on which a program executable by a processor is stored, where the program executable by the processor is used to implement the method for suppressing interference of main lobe of unmanned airborne radar.

The invention has the beneficial effects that:

according to the method, the singular sample data in the sample data are removed by calculating the first generalized inner product value of the sample data, so that a better suppression effect on uniform suppression interference is achieved; the removed sample data is obtained and used as a training sample to estimate interference information, and target detection is directly performed on the generalized inner product value, so that the processing complexity and the calculated amount can be reduced.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a flowchart illustrating steps of a method for suppressing interference of a main lobe of an unmanned airborne radar in this embodiment;

FIG. 2 is a flowchart illustrating steps of a method for suppressing interference to dense decoys according to an embodiment of the present invention;

fig. 3 is a flowchart of a method for suppressing interference of a main lobe of an unmanned airborne radar according to an embodiment of the present invention;

FIG. 4 is a flowchart of a singular sample elimination method based on a threshold according to an embodiment of the present invention;

FIG. 5 is a flowchart illustrating an iterative singular sample elimination method based on a specific elimination number according to an embodiment of the present invention;

fig. 6 is a schematic diagram of the distributed array arrangement position adopted in the simulation experiment according to the embodiment of the present invention;

FIG. 7 is a diagram illustrating a result of detecting a target by using a generalized inner product result when there is no dense decoy interference according to an embodiment of the present invention;

FIG. 8 is a diagram of generalized inner product results before singular sample elimination according to an embodiment of the present invention;

FIG. 9 is a diagram of generalized inner product results after singular samples are removed by using a threshold removal method according to an embodiment of the present invention;

FIG. 10 is a diagram of generalized inner product results obtained by the method of iteratively rejecting a fixed number of samples according to an embodiment of the present invention;

FIG. 11 is a diagram illustrating the results of rejecting and detecting singular samples again according to the embodiment of the present invention;

fig. 12 is a schematic structural diagram of an interference suppression device for a main lobe of an unmanned airborne radar according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.

In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.

The embodiments of the present application will be further explained with reference to the drawings.

Referring to fig. 1, an embodiment of the present invention provides an interference suppression method for a main lobe of an unmanned aerial vehicle radar, including:

s1, a plurality of distributed unmanned aerial vehicles simultaneously detect a space target to obtain echo data;

s2, selecting sample data from the echo data, wherein the sample data is echo data of all distance units;

s3, calculating a first covariance matrix of the sample data;

s4, calculating a first generalized inner product value of each distance unit in the sample data according to the first covariance matrix of the sample data;

s5, extracting singular sample data in the sample data according to the first generalized inner product value, wherein the singular sample data is data of interference targets which are not uniformly distributed on an echo distance unit;

s6, removing the singular sample data;

and S7, acquiring the removed sample data as a training sample to estimate the interference information.

The Generalized Inner Product (GIP) is a non-uniform detector that is mainly used at present. The method comprises the following steps of firstly calculating a covariance matrix of training sample data, wherein the obtained covariance matrix mainly reflects the characteristics of noise or interference which are distributed uniformly in data, detecting the generalized inner product detection quantity of each distance unit by using the covariance matrix, wherein the detection quantity of a unit without a singular sample is far smaller than that of a unit with the singular sample, detecting the singular sample by setting a proper threshold, and then eliminating the samples to estimate the interference information more accurately.

In this embodiment, the radar is a pulse system, the radar is placed on the unmanned aerial vehicle, the radar may be an array radar formed by a plurality of array elements, or may be a radar of a single array element, and each unmanned aerial vehicle and the radar carried by the unmanned aerial vehicle form a distributed node. And simultaneously detecting the space target by the distributed nodes to obtain echo data.

In this embodiment, after the echo data is obtained, the echo data is also preprocessed, which includes but is not limited toAnd by adopting processing technologies such as pulse compression, pulse Doppler and the like, noise, clutter and the like in the preprocessed data are suppressed, and target information is enhanced. Preprocessing data obtained at any observation time to form a data matrix, and recording as X ═ X (X)_i,j)_N×LWhere N is the number of array elements (if the data is subjected to subarray synthesis or dimensionality reduction, the number of synthesized channels is referred to herein), L is the number of distance units corresponding to the pulse, and x is the number of distance units corresponding to the pulse_i,jAnd data of the jth distance unit received by the ith array element after preprocessing is shown.

In this embodiment, all possible singular samples are removed, and after the interference information is estimated by using the removed samples as training samples, whether interference which may form dense false targets exists is further determined according to a set threshold value and a generalized inner product value. Specifically, referring to fig. 2, if the interference that may form the dense decoy exists as a result of the determination, the following steps are further performed:

s8, when the number of the singular sample data is larger than a first threshold value, extracting first data from the singular sample data, wherein the first data is the sample data of which the first generalized inner product value is larger than the elimination threshold value;

s9, calculating a third covariance matrix of the first data;

s10, calculating a third generalized inner product value of each distance unit in the first data according to the third covariance matrix;

s11, setting a second rejection threshold value;

s12, eliminating all data with the third generalized inner product value larger than the second elimination threshold value in the first data to obtain second data;

s13, calculating a fourth covariance matrix of the second data;

s14, calculating a fourth generalized inner product value of each distance unit in the second data according to the fourth covariance matrix;

and S15, carrying out target detection according to the fourth generalized inner product value.

In the embodiment, the generalized inner product value of the sample data is calculated, the singular samples in the training samples are removed, so that a better suppression effect on the uniform suppression interference is achieved, the removed interference and the moving targets are removed and screened again by utilizing the generalized inner product, the interference capable of forming the false target is obtained, meanwhile, the sample to be detected is directly selected as the training sample, self-adaptive processing is not needed after the generalized inner product value of the sample is calculated, target detection is directly performed on the generalized inner product value, and the complexity and the calculated amount of processing are reduced.

Referring to fig. 3, in this embodiment, the specific implementation steps of the method for suppressing interference of the main lobe of the unmanned airborne radar are as follows:

(1) preprocessing echo data received by a radar, preprocessing data obtained at any observation moment to form a data matrix, and recording as X ═ X (X)_i,j)_N×L；

In the step, in order to resist adverse factors influencing target detection, such as noise, clutter, interference and the like in the echo, pulse compression enables a radar waveform to have certain bandwidth by specifically modulating a transmitting signal, and improves the signal-to-noise ratio of a target by matching and receiving the echo signal and utilizing correlation; the pulse doppler is to convert data into a frequency domain for processing by using the difference of doppler characteristics caused by the motion characteristics of different objects, or to design a series of doppler filters to filter data in a time domain to separate clutter information from target information. In addition, the data can be processed by processing methods such as pulse cancellation and pulse accumulation. The dimension of an echo data matrix is not changed by the preprocessing, but the signal-to-noise ratio of the processed data is improved, clutter is basically suppressed, and interference and noise are main factors influencing target detection in the preprocessed data.

(2) And (3) calculating the covariance matrix of X, wherein the calculation method comprises the following steps: r_X＝E[XX^H]In the formula, R_XA covariance matrix representing X, E [ ]]Representing a mean operation, and H represents a conjugate transpose of a covariance matrix;

in this step, the echo data includes interference and noise data in addition to the target information, and it is assumed that the echo data includes P targets and N targets_JInformation on the amount of interference and noise,the interference is not only the suppression interference distributed in each distance unit in the echo, but also the dense false target interference distributed densely in partial distance window or the deception interference forwarded by multiple slicing, and also the isolated false target interference, the mathematical model of the echo can be expressed asWhere t denotes the reception time, a_i(θ_i)、a_j(θ_j) Steering vectors, theta, representing target and disturbance data, respectively_i、θ_jFor the space angle information of a target or an interference source, a guide vector represents the space phase difference of the current array configuration to an incoming wave signal, and under the condition that the target is far away from a radar (far field condition), the azimuth angle of the incoming wave direction is theta, and the pitch angle is thetaWhen (where azimuth is defined as the angle to the positive x-axis semiaxis in the three-dimensional cartesian coordinate system in space, pitch is defined as the angle to the positive y-axis semiaxis in pitch), the space is [ x [ [ x ] x [ ]_k y_k z_k]Array element of position, using coordinate origin as reference array element, said incoming wave signal is expressed as current array element guide vectorWhen the distance between the target and the radar does not meet the far field condition, the guide vector can be directly expressed by the wave path difference of different array elements; s_i(t)、s_j(t) represents complex envelope information, and n (t) represents observed noise. Echo data of all distance units are selected as sample data, and a covariance matrix of the sample data is estimated

(3) Calculating the matrix R_XContrary to, useCalculating the generalized inner product of each distance unitThe value is calculated by the following method:in the formula, GIP_iA first generalized inner product value, X, representing the ith distance cell_iA data vector representing the ith distance unit of the N array elements, H represents the conjugate transpose of the first covariance matrix,a first covariance matrix R representing the sample data_XPerforming inverse calculation of (1);

(4) and removing all possible singular samples in the sample data, and estimating the interference information by using the removed samples as training samples. The following two removal modes can be selected: firstly, data are divided according to a set threshold value and a generalized inner product value, and the data with the generalized inner product value lower than the threshold value are taken as samples to calculate a covariance matrix R of uniform interference_j1(ii) a Secondly, a certain number of samples to be removed are set, a set number of samples are removed, and the remaining data are taken as training samples to calculate the covariance matrix R of the uniform interference_j1；

In this embodiment, according to step (3), the generalized inner product can be understood as using a matrixThe energy of the vector after whitening the vector, so if vector X_iDistribution and calculation of covariance matrix R_XWhen the distribution of the used training sample X is greatly different, the calculated generalized inner product value is larger, and when the vector X is larger_iDistribution and calculation of covariance matrix R_XWhen the distribution of the training samples X is closer, the calculated generalized inner product value is smaller. If vector X_iDistribution and calculation of covariance matrix R_XThe distribution of the training samples X used is independent and infinite distributed (i.i.d.), and the calculated generalized inner product value converges to E [ GIP ]_i]I.e. vector X_iLength N of (a). Since in the training samples, the singular samples are distributed only in partial range units, R_XAnd (3) reflecting more noise and suppressing interference conditions, wherein the distribution of the singular samples is relatively undispersed compared with the noise and suppression, and under the condition that the signal-to-noise ratio of the target and the dry-to-noise ratio of the false target interference are not particularly low, the value of the singular sample is relatively large in the generalized inner product result obtained in the step (3), and the singular samples can be proposed by utilizing the difference of the generalized inner product values to obtain relatively pure suppression interference information estimation. The elimination can be accomplished by two methods, a threshold-based elimination method and a fixed number of iterative elimination methods. The following describes the two methods with reference to fig. 4 and 5.

(5) For the threshold elimination method, a covariance matrix R is calculated_j1Contrary to (2)Use ofCalculating the generalized inner product value of each distance unit; for a particular number of culling methods, a covariance matrix R is calculated_j1Contrary to (2)Use ofCalculating a generalized inner product value of each distance unit (the process of eliminating a certain number of samples and calculating covariance and the generalized inner product value can be carried out iteratively, the number of iteration is generally more than 3, and the number of eliminated samples mainly refers to the number of singular samples in the current data and the number of eliminated samples);

as shown in fig. 4, the physical meaning of the generalized inner product is the energy of the whitening vector, as described above, if the distribution of the current sample and the distribution of the sample X are independent and identically distributed, the generalized inner product value converges to N, and therefore the generalized inner product value of a unit without a singular sample does not theoretically exceed N, but considering that the training sample contains a singular sample, an appropriate threshold may be selected in practice according to the data condition, if the singular samples are obviously more, the threshold may be adjusted to be lower, if the threshold is set to be the data length N or even slightly less than N, and if the singular samples are less, the threshold may be adjusted to be higher, such as 1.2-1.5N.

As shown in fig. 5, the fixed reject number-based iterative rejection method depends on the selection of the reject threshold, and an appropriate threshold needs to be selected according to the actual situation, and if the reject threshold is fixed, the situations that the singular samples are not completely rejected or the remaining samples after rejection are too few may occur. And the rejection condition can be controlled based on the fixed rejection number, the generalized inner product value of the singular sample is smaller than that of the sample only with the suppression interference, and therefore the singular sample rejection can be completed by rejecting a certain number of samples with large generalized inner product values. In addition, samples with large generalized inner product values are removed step by step through iteration, the generalized inner product values of the singular samples become larger and larger as the number of the singular samples becomes smaller and smaller, and the situation of the remaining samples is converged to the actual suppression interference distribution situation. According to practical application, 1/3-1/2 samples are generally selected and removed, and the interference distribution of the remaining samples can be close to the actual suppression interference distribution situation when the iteration times are about 3 times.

In this embodiment, the eliminated data is used as a sample for interference estimation to estimate the covariance matrix R of the interference_j1And use of R_j1Contrary to (2)Recalculating the generalized inner product value according to the calculation mode of the generalized inner productCan be considered to useThe echo data is used as a weight vector to form a beam, so that the generalized inner product operation not only completes the self-adaptive suppression of interference, but also completes the non-coherent accumulation of the echo data. Therefore, when the time-varying target guide vector of the spatial position of each distributed node cannot be obtained, the generalized inner product can be used as non-coherent self-adaptionThe interference suppression method can directly carry out target detection on the generalized inner product result after completing the generalized inner product operation, and does not need to carry out self-adaptive processing on the echo.

(6) And judging whether the interference which can form the dense false target exists or not according to the set threshold value and the generalized inner product value. Considering the distance unit with the generalized inner product value higher than the threshold value as the distance unit with the false target interference and the target; if the distance units above the threshold are obviously larger than the target number, the interference which can form dense false targets is considered to exist, the interference can be formed by the interference of the dense false targets released by the enemy and can also be deceptive interference formed by the enemy performing multiple slice forwarding on the radar signal, and the covariance matrix R 'of the distance unit data is calculated'_X(ii) a If the number of the distance units higher than the threshold is less, judging that no interference which can form dense false targets exists in the echo, wherein the distance units higher than the threshold can be targets or isolated interference, directly carrying out target detection on the generalized inner product result calculated at the moment, and executing the step (10);

in this embodiment, the purpose of the rejection is to avoid the influence of singular samples on the covariance estimation of the suppressed interference, so as to better suppress the suppressed interference, but the rejected data is not processed, and no matter the data is further adaptively processed, a false target is formed, or as described in step (5), the generalized inner product result is directly detected, if there is dense interference, a large number of false alarms are formed by the dense interference, and if the dry-to-noise ratio is greater than the signal-to-noise ratio of the target, and the interference is distributed in a distance unit near the target, the target is submerged by the interference, so that the detection omission is caused. The best approach is therefore to further process the dense decoy interference in these rejected singular samples.

For processing the dense decoy interference, firstly, whether the dense decoy interference exists is judged, and the generalized inner product can still be used for judging. When there is dense interference, the number of samples with large generalized inner product in step (5) is larger than that when there are only a few targets and isolated interference, and a higher threshold, such as 3 times or more of data length, may be set as the threshold, and it is determined whether there is dense false target according to the number of samples above the threshold. Dense interference may be considered to be present if the sample units above the threshold are significantly larger than the number of targets and isolated interferers. And (5) if the dense false target interference exists, continuing to process the interference, and if the dense false target interference does not exist, carrying out target detection on the result obtained in the step (5).

For dense false target interference, whether the interference comes from a large number of interference sources released by an enemy or multiple slice forwarding of radar signals by the enemy, the distribution characteristics of the interference are similar and the number of the interference is usually far larger than the number of targets, the generalized inner product values of the sample units are calculated by using the covariance matrix of the rejected data, the generalized inner product values of the distance units of the targets are larger due to the difference between the distribution characteristics of the targets and the interference distribution characteristics, at the moment, the targets and the isolated interference are singular samples for the dense interference, and the covariance matrix estimation R 'of the dense interference can be obtained by repeating the singular sample rejection process'_X. It should be noted that if the number of array elements is large, such as a large onboard phased array radar, the number of array elements is greater than the number of samples left after the elimination, and R 'is obtained'_XIf the matrix is not full-rank, the subsequent processing by using the singular matrix of non-full rank can not obtain ideal processing results. However, if the echo is sub-array synthesized, R 'can be obtained when the number of synthesized channels is less than the number of samples after rejection'_XThe full rank estimation is sufficient. As long as the degree of freedom of the array after synthesis is greater than the number of interferences, the suppression of the interferences can be completed.

(7) Calculate matrix R'_XTo (R)'_X)^-1From (R'_X)^-1Calculating the generalized inner product value of the distance unit which is higher than the threshold and is obtained in the step (6);

in this example, R 'is used analogously to step (3)'_XTo (R)'_X)^-1And recalculating the generalized inner product value of the distance unit where the sample is located.

(8) Similar to the step (4), a method of threshold elimination or specific sample number elimination is adopted to eliminate possible moving targets or isolateInterference, etc., to obtain a covariance matrix R that will result in denser decoy interference_j2；

In this embodiment, similar to step (4), singular sample data is removed according to the generalized inner product result calculated in step (7), and as described in step (6), the singular sample at this time corresponds to a target or isolated interference. Taking the samples after the elimination as estimation samples of the dense false target interference, and estimating a covariance matrix R of the samples_j2。

(9) Calculating the matrix R_j2Contrary to (2)Use ofCalculating the generalized inner product value of the distance unit which is higher than the threshold and is obtained in the step (6);

in the present example, the inverse obtained in step (8) was usedAnd (4) recalculating the generalized inner product value of the distance unit where the singular samples higher than the detection threshold obtained in the step (5) are located, wherein the dense false target interference is already suppressed.

(10) And detecting the calculated generalized inner product result.

In this embodiment, the method for suppressing interference of the main lobe of the unmanned airborne radar in the embodiment of the present invention is further verified through the following simulation experiment:

simulation setting: in order to illustrate the removing effect of the method provided by the invention on the singular samples, the following experiment is designed. The distributed system transmits and receives signals which are linear frequency modulation signals, the center frequency of carrier frequency is 300MHz, the frequency modulation bandwidth is 5MHz, the pulse width is 20us, the pulse repetition period is 200us, the system consists of 6 detection nodes and comprises a large array consisting of 100 array elements and five small arrays consisting of 20 array elements, echo data are subjected to sub-array synthesis, a main array is synthesized into 5 channels, each auxiliary array is synthesized into one channel, the arrangement position is shown in figure 6, the distributed array is a non-uniform sparse planar array and is positioned on an xoy plane. The original single-channel signal-to-noise ratio of the target is-20 dB, the target distance is 300km, and the azimuth and pitch are (0 degrees, 20 degrees).

Experiment 1) is provided with two groups of interference suppression, the distance of an interference source is the same as the distance of a target, the azimuth and the pitching are respectively (0.078 degrees, 20 degrees) and (-0.078 degrees and 20 degrees), the two groups of interference suppression are all positioned in a main lobe of a main radar, the bandwidth is 5MHz, and the original interference-noise ratio of a single channel is 30 dB.

Experiment 2) the echo contains two sets of slice-forwarded deceptive interferences. The repeated forwarding times of each group of slice sampling segments are 16 times, and the interference ratio is 30dB (referring to the original interference noise ratio of a single channel before preprocessing).

1) Content of experiment 1: the method is used for simulating and verifying the suppression interference suppression of the main lobe when no dense false target exists:

the experimental contents are as follows: dense false target interference is not added in the experiment set, because the number of singular samples in the samples is only the target, the generalized inner product value of the distance unit where the target is located is obviously larger than that of other distance units, the singular sample of the target can be easily removed by any removing method, and the difference of the two removing methods for obtaining the correct estimation of the suppressed interference is further explained in the experiment set 2. Here, the result of calculating the generalized inner product and detecting after removing the singular samples is given, as shown in fig. 7.

And (4) analyzing results: it can be seen that using the generalized inner product results, sufficient array synthesis gain can be obtained for the target to be detected. Although the generalized inner product is a non-coherent array synthesis accumulation mode in principle, when an array configuration cannot obtain an ideal coherent accumulation gain, the detection of a target based on the generalized inner product is completely feasible, and compared with a method of removing a singular sample estimation covariance matrix and then performing adaptive processing, the method does not need to perform adaptive processing operation, and the processing complexity and the calculation amount can be reduced.

2) Experiment content 2: the simulation verification of the method is carried out when the suppression interference and the dense false target interference exist at the same time:

2-1 simulation verification of the singular sample elimination method:

the experimental contents are as follows: in addition to targets, slice forwarding deception interference which can cause a large number of false targets and is a singular sample for echo data is added in the experiment. And under the condition of more singular samples, verifying the singular sample eliminating effect provided by the invention. Fig. 8 shows the generalized inner product values before culling obtained by estimating the covariance matrix using all echo data as training samples. Fig. 9 shows the result of calculating the generalized inner product value after using 1 times of data length as the threshold culling. Fig. 10 shows the result obtained when 1/3 samples are removed and the number of iterations is 3, where fig. 10(a) shows the generalized inner product result calculated after the first removal and fig. 10(b) shows the generalized inner product result calculated after the iterations.

And (4) analyzing results: the two methods can find singular samples corresponding to two groups of interferences, but the elimination effect in the result of iterative elimination is better than that of threshold elimination, because the interference ratio of a group with a short distance is small, the influence on the generalized inner product value is small, the threshold elimination cannot eliminate the interference completely, the iterative elimination can ensure that the size of each elimination is kept small, and after multiple iterations, all possible singular samples can be eliminated as much as possible. When the interference dry-to-noise ratio is large enough, singular samples can be better eliminated by the two methods, and when the interference dry-to-noise ratio is small, a good elimination effect can be more easily ensured by the iterative elimination-based method.

2-2 suppression of decoy interference:

the experimental contents are as follows: as described above, if the result obtained in step 2-1 is directly detected, a large number of false targets are generated, which are formed by the rejected slice forwarding interference, and in order to ensure the suppression effect on the suppression interference, the interference is rejected, and after rejection, the false targets are generated. Therefore, the invention proposes to remove the removed singular samples again to complete the suppression of the interference which can form false targets. The results obtained if the set of experiments adds, in addition to the targets, a large amount of dense decoy interference and slice-forward deceptive interference that can cause decoys, which are singular samples to the echo data. And under the condition of more singular samples, verifying the singular sample eliminating effect provided by the invention. Fig. 11 is a diagram showing the detection result of removing the singular samples again.

And (4) analyzing results: it can be seen that by performing the elimination and the generalized inner product calculation again on the eliminated singular samples, the generalized inner product value of the decoy is gradually suppressed due to the difference in the interference distribution between the target and the decoy, and the target covered by the interference in fig. 9 and 10 becomes gradually larger and can be detected due to the suppression of the interference forming the decoy, so that the method can process the slice forwarding type deception interference which can form the dense decoy or the passive dense decoy interference released by the enemy, and complete the correct detection of the target.

According to experimental verification, the method for suppressing the interference of the main lobe of the unmanned airborne radar has the following technical effects:

(1) the method of the embodiment of the invention solves the problem of interference which can cause dense false targets, the existing method based on the generalized inner product only eliminates the interference, avoids the influence of the interference on uniform suppression interference suppression, and does not solve the problem of interference which can form the dense false targets in the eliminated data;

(2) the method is suitable for the problem that the target guide vector cannot be obtained when the distributed airborne radar is cooperatively detected under the time-varying distribution condition, the physical characteristics of non-coherent accumulation of the generalized inner product are utilized while the non-uniform data is eliminated by using the generalized inner product, and meanwhile, interference suppression and the non-coherent accumulation of the target data are completed;

(3) the method of the embodiment of the invention directly uses the generalized inner product result to carry out target detection, thereby greatly reducing the processing complexity and the calculated amount of carrying out self-adaptive processing after obtaining the interference information to obtain the filtering result and then carrying out detection.

Referring to fig. 12, an embodiment of the present invention further provides an unmanned airborne radar main lobe interference suppression apparatus 200, which specifically includes:

at least one processor 210;

at least one memory 220 for storing at least one program;

when executed by the at least one processor 210, causes the at least one processor 210 to implement the method as shown in fig. 1 and 2.

The memory 220, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs and non-transitory computer-executable programs. The memory 220 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 220 may optionally include remote memory located remotely from processor 210, and such remote memory may be connected to processor 210 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

It will be understood that the device configuration shown in fig. 12 is not intended to be limiting of device 200, and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.

In the apparatus 200 shown in fig. 12, the processor 210 may retrieve the program stored in the memory 220 and execute, but is not limited to, the steps of the embodiments shown in fig. 1 and fig. 2.

The above-described embodiments of the apparatus 200 are merely illustrative, and the units illustrated as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purposes of the embodiments.

Embodiments of the present invention also provide a computer-readable storage medium storing a program executable by a processor, where the program executable by the processor is used to implement the method shown in fig. 1 and 2 when being executed by the processor.

The embodiment of the application also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the methods illustrated in fig. 1 and 2.

It will be understood that all or some of the steps, systems of methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.