CN113406577B - Unmanned airborne radar main lobe interference suppression method, device and storage medium - Google Patents
Unmanned airborne radar main lobe interference suppression method, device and storage medium Download PDFInfo
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- G—PHYSICS
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The application discloses a main lobe interference suppression method, a main lobe interference suppression device and a storage medium for unmanned aerial vehicle radars, wherein the method comprises the steps of simultaneously detecting a space target by a plurality of distributed unmanned aerial vehicle radars to obtain echo data; sample data is selected from the echo data, wherein the sample data is the echo data of all distance units; calculating a first covariance matrix of the sample data; according to a first covariance matrix of the sample data, calculating a first generalized inner product value of each distance unit in 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 the singular samples, so that the target detection is more accurate; the use of generalized inner product results to detect the target can greatly reduce the amount of computation due to the adaptive processing. The method can be widely applied to the technical field of radar signal processing.
Description
Technical Field
The application relates to the technical field of radar signal processing, in particular to a method and a device for suppressing main lobe interference of an unmanned airborne radar and a storage medium.
Background
When the airborne radar detects targets, besides target objects of interest, unavoidable clouds, rain, ground objects and the like which are not of interest exist in a detection scene, radar signals can be reflected, and various disturbances released intentionally by enemy can exist in a combat scene, so that the accurate detection of the targets can be influenced. In order to successfully detect the target, the 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 identification of the target are finished by suppressing non-interested noise, clutter and interference or enhancing the information of the target in the echo. The distributed array can utilize the difference of targets and interference in space angle to carry out self-adaptive inhibition on interference in space domain, meanwhile, the enhancement of target information is completed by accumulating echo data of each array element in the array, meanwhile, as the distributed array can be considered to equivalently increase the length of a radar antenna base line, the interference in a main lobe range of a radar with a single node short base line can be changed into the interference in a side lobe of the distributed array, so that the main lobe interference of the radar in the traditional sense can be well inhibited, and the problems of main lobe distortion, target signal cancellation and the like when the main lobe interference is self-adaptively inhibited can be avoided.
Interference suppression algorithms mostly require knowledge of the interference to accomplish the suppression. The samples selected for estimating the information of the interference are called training samples. The training samples include data of moving objects, false object interference and the like which are not uniformly distributed on the echo distance unit, and the data are called singular samples. The presence of singular samples may cause errors in the information estimates of the interference (where the interference refers to active noise suppression interference that is relatively uniformly distributed over the range bin, and these interference may raise the noise floor of the echo, which is the primary interference affecting target detection), and may not achieve the desired interference suppression effect, or may even result in cancellation of the target signal.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a method and a device for suppressing main lobe interference of an unmanned aerial vehicle radar and a storage medium.
The technical scheme adopted by the application is as follows:
in one aspect, an embodiment of the present application includes a method for suppressing main lobe interference of an unmanned airborne radar, including:
a plurality of distributed unmanned aerial vehicle radar are used for detecting a space target at the same time, so that echo data are obtained;
sample data is selected from the echo data, wherein the sample data is the 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 a 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 in which interference targets 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 plurality of distributed unmanned aerial vehicle-mounted radars detect the space target at the same time and obtain the echo data, the method further comprises:
the echo data is preprocessed, the preprocessing including at least one of pulse compression processing and pulse doppler processing.
Further, the calculating the first covariance matrix of the sample data is performed by:
R X ＝E[XX H ]；
wherein R is X A first covariance matrix representing sample data E]Representing the mean operation, H represents the conjugate transpose of the first covariance matrix, and X represents the sample data matrix after preprocessing.
Further, the calculating the 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 i A first generalized inner product value, X, representing an ith range bin i A data vector representing an ith distance element, H representing the conjugate transpose of the first covariance matrix,first party representing sample dataDifference matrix R X Is the inverse of (1).
Further, the step of rejecting the singular sample data includes:
setting a first rejection threshold value;
and eliminating all sample data with the first generalized inner product value larger than the first eliminating 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 samples removed;
sorting the first generalized inner product value from large to small; obtaining a sequencing list;
and eliminating sample data corresponding to the first generalized inner product value of the first numerical value in the sorting list.
Further, after the rejecting the singular sample data, the method further comprises:
taking the sample data from which the singular sample data is removed as a training sample;
calculating a second covariance matrix of the training sample;
calculating a second generalized inner product value of each distance unit in the training sample according to the second covariance matrix;
and performing 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 with the first generalized inner product value larger than the rejection 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 rejection threshold;
removing all the data with the third generalized inner product value larger than the second removing threshold value from 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 performing target detection according to the fourth generalized inner product value.
On the other hand, the embodiment of the application also comprises a main lobe interference suppression device of the unmanned aerial vehicle radar, which comprises the following components:
at least one processor;
at least one memory for storing at least one program;
and when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the main lobe interference suppression method of the unmanned airborne radar.
In another aspect, an embodiment of the present application further includes a computer readable storage medium having stored thereon a program executable by a processor, where the program executable by the processor is configured to implement the method for suppressing main lobe interference of an unmanned airborne radar when executed by the processor.
The beneficial effects of the application are as follows:
according to the method, the first generalized inner product value of the sample data is calculated, and singular sample data in the sample data are removed, so that a better suppression effect on uniform suppression interference is achieved; the removed sample data is used as a training sample to estimate the interference information, and the target detection is directly carried out on the generalized inner product value, so that the complexity and the calculated amount of the processing can be reduced.
Additional aspects and advantages of the application 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 application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1 is a flowchart of steps of a main lobe interference suppression method of an unmanned airborne radar in the present embodiment;
FIG. 2 is a flowchart illustrating steps of a method for suppressing interference to a dense decoy according to an embodiment of the present application;
fig. 3 is a flowchart of a main lobe interference suppression method of an unmanned airborne radar according to an embodiment of the present application;
fig. 4 is a flowchart of a singular sample rejection method based on a threshold according to an embodiment of the present application;
fig. 5 is a flowchart of an iterative singular sample rejection method based on a specific rejection number according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a distributed array layout used in a simulation experiment according to an embodiment of the present application;
FIG. 7 is a diagram of a target detection result using a generalized inner product result when there is no dense false target interference according to an embodiment of the present application;
FIG. 8 is a generalized inner product result diagram before singular sample rejection according to an embodiment of the present application;
FIG. 9 is a generalized inner product result diagram after singular samples are removed by using a threshold removal method according to an embodiment of the present application;
FIG. 10 is a generalized inner product result diagram obtained by using an iterative method of eliminating a fixed number of samples according to an embodiment of the present application;
FIG. 11 is a diagram of the result of the rejection and detection of the rejected singular samples again in the embodiment of the application;
fig. 12 is a schematic structural diagram of a main lobe interference suppression device for an unmanned airborne radar according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
In the description of the present application, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present application and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application.
In the description of the present application, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed 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 application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.
Embodiments of the present application will be further described below with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present application provides a method for suppressing main lobe interference of an unmanned airborne radar, including:
s1, a plurality of distributed unmanned aerial vehicle radar are used for detecting a space target at the same time, and echo data are obtained;
s2, selecting sample data from the echo data, wherein the sample data are 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 a 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 are data in which interference targets are not uniformly distributed on an echo distance unit;
s6, eliminating the singular sample data;
s7, acquiring the removed sample data as a training sample to estimate the interference information.
Generalized inner product (Generalized Inner Product, GIP) is one type of non-uniform detector that is currently being used primarily. The basic idea is that firstly, a covariance matrix of training sample data is calculated, the obtained covariance matrix mainly reflects the characteristics of noise or interference which are distributed uniformly in the data, the covariance value is used for detecting the generalized inner product detection quantity of each distance unit, the detection quantity of a unit without singular samples is far smaller than that of a unit with singular samples, the singular samples can be detected by setting a proper threshold, and then, the samples are removed, so that the interference information can be estimated more accurately.
In this embodiment, the radar is a pulse system, the radar is disposed on an unmanned aerial vehicle, the radar may be an array radar formed by a plurality of array elements, or may be a radar with a single array element, and each unmanned aerial vehicle and the radar carried by the unmanned aerial vehicle form a distributed node. And a plurality of distributed nodes detect the space target at the same time to obtain echo data.
In this embodiment, after the echo data is obtained, the echo data is further preprocessed, where preprocessing includes but is not limited to pulse compression, pulse doppler and other processing techniques, noise, clutter and the like in the preprocessed data are suppressed, and the target information is enhanced. Preprocessing the data obtained at any observation time to form a data matrix, and marking the data matrix as X= (X) i,j ) N×L Wherein N is the number of array elements (the number of channels after synthesis if the data is subjected to subarray synthesis or dimension reduction), L is the number of distance units corresponding to pulses, and x i,j And the data of the jth distance unit received by the ith array element after pretreatment is represented.
In this embodiment, all possible singular samples are removed, and after the removed samples are used as training samples to estimate the interference information, whether the interference that would form a dense false target exists is further determined according to the set threshold value and the generalized inner product value. Specifically, referring to fig. 2, if the determination result is that there is interference that may form a dense decoy, 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 with the first generalized inner product value larger than the rejection 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 the 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;
s15, performing target detection according to the fourth generalized inner product value.
According to the method, the generalized inner product value of the sample data is calculated, singular samples in the training samples are removed, so that a better suppression effect on uniform suppression interference is achieved, the removed interference and the moving target are subjected to removing and screening again by utilizing the generalized inner product, interference which can form false targets 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 conducted on the generalized inner product value, and complexity and calculation amount of processing are reduced.
Referring to fig. 3, in this embodiment, the specific implementation steps of the main lobe interference suppression method of the unmanned airborne radar are as follows:
(1) Preprocessing echo data received by a radar, preprocessing data obtained at any observation time to form a data matrix, and marking the data matrix as X= (X) i,j ) N×L ；
In the step, in order to combat adverse factors such as noise, clutter, interference and the like in echo, which affect target detection, pulse compression enables radar waveforms to have a certain bandwidth by carrying out specific modulation on transmitting signals, and the signal to noise ratio of a target is improved by utilizing correlation through carrying out matching receiving on echo signals; pulse Doppler refers to the process of converting data into frequency domain by utilizing the difference of Doppler characteristics caused by the motion characteristics of different objects, and a series of Doppler filters can be designed to filter the data in the time domain so as to separate clutter information and target information. In addition, the data can be processed by processing methods such as pulse cancellation and pulse accumulation. The dimension of the echo data matrix is not changed by the pretreatment, but the signal-to-noise ratio of the treated data is improved, clutter is basically inhibited, and the main factors influencing target detection in the pretreated data are interference and noise.
(2) The covariance matrix of X is calculated, and the calculation method is as follows: r is R X ＝E[XX H ]Wherein R is X Covariance matrix representing X, E [ []Representing mean operation, H representing the conjugate transpose of the covariance matrix;
in this step, the echo data contains interference and noise data in addition to the target information, and it is assumed that the echo data contains P targets and N J Information of individual interferences and noises, wherein the interferences are not only pressed interferences with distribution of each distance unit in echo, but also dense decoy interferences with denser distribution in partial distance window or deception interferences forwarded by multiple slices, and also isolated decoy interferences, and a mathematical model of the echo can be expressed asWherein t represents the reception time, a i (θ i )、a j (θ j ) Steering vectors, θ, representing target and interference data, respectively i 、θ j For the space angle information of a target or an interference source, the guiding vector represents the space phase difference of the current array configuration to incoming wave signals, 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 theta/>When (where azimuth defines the angle between azimuth and positive x-axis half axis in three dimensional Cartesian coordinate system, pitch angle is defined as the angle between pitch and positive y-axis half axis), space is located in [ x ] k y k z k ]The array element of the position takes the origin of coordinates as a reference array element, and the incoming wave signal is expressed as the current array element guiding vectorWhen the distance between the target and the radar does not meet the far-field condition, the steering vector can be directly represented by the wave path difference of different array elements; s is(s) i (t)、s j (t) represents complex envelope information, and n (t) represents observation noise. Echo data of all distance units are selected as sample data, and covariance matrix of the sample data is estimated>
(3) Calculating matrix R X Is the inverse of (1) usingThe generalized inner product value of each distance unit is calculated by the following steps:in the formula, GIP i A first generalized inner product value, X, representing an ith range bin i Data vector representing the ith distance element of N array elements, H representing the conjugate transpose of the first covariance matrix, ">First covariance matrix R representing sample data X Is calculated by the inverse of (1);
(4) And eliminating all possible singular samples in the sample data, and estimating interference information by using the eliminated samples as training samples. The following two rejection modes can be selected: firstly, dividing data according to a set threshold value and a generalized inner product value, and taking data with the generalized inner product value lower than the threshold value as a sample meterCovariance matrix R for calculating uniform interference j1 The method comprises the steps of carrying out a first treatment on the surface of the Secondly, a certain number of removed samples is set, the samples with the set number are removed, the rest data are taken as training samples to calculate covariance matrix R of uniform interference j1 ；
In this embodiment, the generalized inner product can be understood as using a matrix according to step (3)Energy of vector after whitening vector, thus if vector X i Distribution and calculation covariance matrix R of (2) X When the distribution phase difference of the training samples X is larger, the calculated generalized inner product value is larger, and when the vector X is i Distribution and calculation covariance matrix R of (2) X When the distribution of the training samples X is close, the calculated generalized inner product value is smaller. If vector X i Distribution and calculation covariance matrix R of (2) X The distribution of training samples X used is independent and equal (independent and identically distributed, i.i.d), then the calculated generalized inner product value converges to E [ GIP ] i ]I.e. vector X i Is a length N of (c). Since the singular samples are distributed only in a part of the distance units in the training samples, R X And (3) reflecting more noise and suppression interference, and because the distribution of the singular samples is relatively non-dispersive compared with noise and suppression, the value of the singular samples is larger in the generalized inner product result obtained in the step (3) under the condition that the signal-to-noise ratio of the target and the interference-to-noise ratio of the false target are not particularly low, and the singular samples can be proposed by utilizing the difference of the generalized inner product values to obtain purer suppression interference information estimation. The culling can be accomplished in two ways, threshold-based culling and fixed number iterative culling. The two methods are described in detail below with reference to fig. 4 and 5.
(5) For the threshold elimination method, covariance matrix R is calculated j1 Is the inverse of (2)Use->Calculating the generalized inner product value of each distance unit; for a specific number of methods of culling, a covariance matrix R is calculated j1 Is>Use->Calculating generalized inner product values of each distance unit (the process of eliminating a specific number of samples, calculating covariance and generalized inner product values can be iterated, the number of general iterations is 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 method for eliminating the singular value is based on the threshold, as previously described, the physical meaning of the generalized inner product is the energy of the whitening vector, if the distribution of the current sample and the distribution of the sample X are independent and identical, the generalized inner product value converges to N, so that the generalized inner product value of the singular sample unit does not theoretically exceed N, but considering that the training sample contains the singular sample, in practice, a suitable threshold can be selected according to the data situation, if the singular sample is obviously more, the threshold can be lowered, if the threshold is set to be even slightly smaller than N, and if the singular sample is less, the threshold can be raised, for example, 1.2-1.5N.
As shown in fig. 5, the method for iteratively removing based on the fixed number of removing is a method for removing based on the threshold, which depends on the selection of the removing threshold, and a proper threshold needs to be selected according to the actual situation, if the removing threshold is fixed, the situations that the singular samples are not removed cleanly or the residual samples are too few after removing may occur. 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 with the suppression interference only, and therefore the singular sample rejection can be finished 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, and as the number of singular samples is reduced, the generalized inner product value of the singular samples is increased until the situation of the residual samples is converged to the actual suppression interference distribution situation. According to practical application, 1/3-1/2 of samples are generally selected and removed, and the iteration times are about 3 times, so that the interference distribution of the rest samples is close to the actual suppression interference distribution condition.
In this embodiment, the removed data is used as a sample of interference estimation to estimate the covariance matrix R of the interference j1 And use R j1 Is the inverse of (2)Recalculating the generalized inner product value according to the calculation mode of the generalized inner product->Can be regarded as use->As a weight vector, beam forming is carried out on echo data, 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 space position time-varying target guide vector of each distributed node cannot be obtained, the generalized inner product can be used as a non-coherent adaptive interference suppression method, and after the generalized inner product operation is completed, target detection can be directly carried out on the generalized inner product result without carrying out adaptive processing on the echo.
(6) And judging whether interference which can form a dense false target exists or not according to the set threshold value and the generalized inner product value. Considering the distance units with generalized inner product values higher than the threshold value as the distance units with false target interference and targets; if the distance units above the threshold are obviously larger than the target number, the interference which can form dense decoys is considered to exist, the interference can be formed by dense decoys released by the enemy, and can also be deception interference formed by repeated slicing and forwarding of radar signals by the enemy, and the covariance matrix R 'of the distance unit data is calculated' X The method comprises the steps of carrying out a first treatment on the surface of the If the number of distance units higher than the threshold is less, judging that no interference which can form a dense false target exists in the echo, wherein the distance units higher than the threshold are possibly targets or isolated interference, directly detecting the target by the generalized inner product result calculated at the moment, and executing the step (10);
in this embodiment, the purpose of rejection is to avoid the influence of the odd samples on the suppression interference covariance estimation, so that the suppression interference is better suppressed, but the rejected data is not processed, no matter how the data is adaptively processed, false targets are formed, or as described in step (5), generalized inner product results are directly detected, if dense interference exists, a large number of false alarms are formed by the dense interference, if the dry-to-noise ratio is greater than the target signal-to-noise ratio, and if the interference is distributed in a distance unit near the target, the interference can submerge the target, causing missed detection. The best approach is therefore to further process the dense decoy interference in these rejected singular samples.
To deal with dense decoy interference, first, a determination is made as to whether dense decoy interference exists, and a generalized inner product may still be used for the determination. When dense interference exists, samples with large generalized inner product values are more than when only a small number of targets and isolated interference exist in the step (5), a higher threshold, such as 3 times or more times of data length, can be set as the threshold, and whether dense decoys exist or not can be judged according to the number of samples higher than the threshold. If the sample cell above the threshold is significantly greater than the number of target and isolated disturbances, then dense disturbances may be considered to be present. If dense decoy interference exists, continuing to process the interference, and if the dense decoy interference does not exist, performing target detection on the result obtained in the step (5).
For dense decoy interference, whether the interference comes from a large amount of interference sources released by an adversary or repeated slice forwarding of radar signals by the adversary, the distribution characteristics of the interference are similar and the number is usually far greater than the number of targets, the covariance matrix of the eliminated data is used for calculating the generalized inner product value of the sample units, the generalized inner product value of the distance units existing in the targets is larger because the distribution characteristics of the targets are different from the distribution characteristics of the interference, 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 previous singular sample elimination process' X . It should be noted that if the number of array elements is large, for example, the number of array elements is larger than the number of samples remaining after the rejection, the obtained R 'is larger than the number of samples remaining after the rejection' X Rather than full rank, subsequent processing using a singular matrix of non-full rank certainly does not achieve the desired processing result. However, if the subarray synthesis is performed on the echo, R 'can be obtained when the number of synthesis channels is smaller than the number of samples after rejection' X Full rank estimation of (c) is required. The suppression of the interference can be accomplished as long as the degree of freedom of the synthesized array is greater than the number of interference.
(7) Calculating matrix R' X Is the inverse (R' X ) -1 Using (R' X ) -1 Calculating a 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 similarly to step (3)' X Is the inverse (R' X ) -1 And recalculating and eliminating the generalized inner product value of the distance unit where the sample is located.
(8) Similar to the step (4), the method of threshold rejection or specific sample number rejection is adopted to reject the possible moving target or isolated interference and other data, and obtain covariance matrix R which can form denser false target 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), as described in step (6), where the singular sample corresponds to a target or isolated interference. Taking the samples removed at this time as estimated samples of dense false target interference, and estimating covariance matrix R of the estimated samples j2 。
(9) Calculating matrix R j2 Is the inverse of (2)Use->Calculating a generalized inner product value of the distance unit which is higher than the threshold and is obtained in the step (6);
in this example, the inverse of that obtained in step (8) is usedAnd (3) recalculating a generalized inner product value of a distance unit where the singular sample which is higher than the detection threshold and is obtained in the step (5) is located, wherein the dense false target interference is suppressed.
(10) And detecting the calculated generalized inner product result.
In this embodiment, the method for suppressing main lobe interference of the unmanned airborne radar according to the embodiment of the present application is further verified through the following simulation experiment:
simulation setting: in order to illustrate the effect of the method provided by the application on the rejection of the singular samples, the following experiment is designed. The distributed system transmits and receives signals as linear frequency modulation signals, the central 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, the system comprises a large array consisting of 100 array elements and five small arrays consisting of 20 array elements, echo data are subjected to subarray synthesis, the main array synthesizes 5 channels, each auxiliary array synthesizes one channel, the distribution array is a non-uniform sparse planar array, and the distribution array is positioned on an xoy plane as shown in figure 6. The signal-to-noise ratio of the original single channel of the target is-20 dB, the distance of the target is 300km, and the azimuth and the pitching are (0 DEG, 20 DEG).
In experiment 1), two groups of suppression interference are arranged, the distance between an interference source and a target is the same, the azimuth and the pitching are respectively (0.078 degrees, 20 degrees) and (-0.078 degrees, 20 degrees), and are all positioned in a main lobe of a main radar, the bandwidth is 5MHz, and the original interference-to-noise ratio of a single channel is 30dB.
Experiment 2) echo contains two sets of slice-forwarded spoofing disturbances. Each set of slice sample fragments is repeated 16 times with a interference to interference dry-to-noise ratio of 30dB (referring to the original dry-to-noise ratio of the single channel prior to preprocessing).
1) Experiment content 1: simulation verification of main lobe suppression interference suppression by the method when no dense false target exists:
the experimental contents are as follows: since 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 samples of the target can be easily removed no matter what removing method is used, and the difference between the two removing methods for obtaining the correct estimation of the suppression interference is further explained in the 2 nd group of experiments. Here, the result of calculating the generalized inner product after removing the singular samples and performing the detection is given as shown in fig. 7.
Analysis of results: it can be seen that using the generalized inner product results, sufficient array synthesis gain can be obtained to allow the target to be detected. Although the generalized inner product is a non-coherent array synthesis accumulation mode in principle, when the array configuration cannot obtain ideal coherent accumulation gain, detection of a target based on the generalized inner product is completely feasible, and compared with a method of eliminating singular samples first and estimating a covariance matrix and then performing self-adaptive processing, the method does not need to perform self-adaptive processing operation, and the processing complexity and the calculation amount can be reduced.
2) Experiment content 2: simulation verification of the method when suppression interference and dense false target interference exist simultaneously:
simulation verification of 2-1 singular sample rejection method:
the experimental contents are as follows: in addition to the targets, the group of experiments adds slice forwarding spoofing type interference which can cause a large number of false targets and is a singular sample for echo data. Under the condition of more singular samples, the singular sample eliminating effect provided by the application is verified. Fig. 8 shows generalized inner product values before culling obtained by estimating covariance matrix using all echo data as training samples. Fig. 9 is a result of calculating the generalized inner product value after culling using a multiple of 1 length as a threshold. Fig. 10 shows the result when 1/3 sample is removed and the number of iterations is 3, where fig. 10 (a) shows the result of the generalized inner product calculated after the first removal and fig. 10 (b) shows the result of the generalized inner product calculated after the iteration.
Analysis of results: it can be seen that both methods can find the singular samples corresponding to the two groups of interference, but the rejection effect in the iterative rejection result is superior to that of the threshold rejection, because the interference-to-interference ratio of the group with a smaller distance is small, the generalized inner product value is also smaller, the threshold rejection can not reject the singular samples completely, the iterative rejection can ensure that the size of each rejection is small, and after multiple iterations, all possible singular samples can be rejected as much as possible. When the interference and noise ratio is large enough, the two methods can reject the singular samples better, and when the interference and noise ratio is small, the iterative rejection-based method can ensure the good rejection effect more easily.
2-2 suppression of decoy interference:
the experimental contents are as follows: as described above, if the result obtained in 2-1 is directly detected, a large number of decoys will appear, and these decoys are formed by the interference forwarded by the removed slice, so that in order to ensure the suppression effect on the suppression interference, these interferences are removed, and these interference will form decoys after removal. Therefore, the application proposes to reject the rejected singular samples again to complete the suppression of false target interference. The results obtained if the set of experiments adds, in addition to the target, dense decoy disturbances and slice forwarding spoofing disturbances that can cause a significant number of decoys that are singular samples for the echo data. Under the condition of more singular samples, the singular sample eliminating effect provided by the application is verified. Fig. 11 is a diagram of the detection result after the singular samples are removed again.
Analysis of results: it can be seen that by performing rejection and generalized inner product calculation on the rejected singular samples again, the generalized inner product value of the decoy is gradually suppressed due to the difference of interference distribution of the target and the decoy, and the interference forming the decoy is suppressed, so that the target covered by the interference in fig. 9 and 10 is gradually enlarged and can be detected, and it can be seen that the method can process the slice forwarding type deception interference forming the intensive decoy or the passive intensive decoy interference released by the enemy, and complete correct detection of the target.
According to experimental verification, the main lobe interference suppression method for the unmanned aerial vehicle radar provided by the embodiment of the application has the following technical effects:
(1) The method solves the problem of interference which can cause dense false targets, and the existing method based on generalized inner products only eliminates the interference, avoids the influence of the method on uniform suppression of interference, and does not solve the interference which can form the dense false targets in the eliminated data;
(2) The method provided by the embodiment of the application is suitable for the problem that the target guide vector cannot be detected in cooperation with the distributed airborne radar under the time-varying distribution condition, and the physical characteristics of non-coherent accumulation of the generalized inner product are utilized while non-uniform data are removed by using the generalized inner product, so that interference suppression and non-coherent accumulation of target data are completed;
(3) The method provided by the embodiment of the application directly uses the generalized inner product result to detect the target, thereby greatly reducing the processing complexity and the calculated amount of detecting the filtering result obtained by self-adaptive processing after the interference information is obtained.
Referring to fig. 12, the embodiment of the present application further provides an apparatus 200 for suppressing main lobe interference of an unmanned airborne radar, which specifically includes:
at least one processor 210;
at least one memory 220 for storing at least one program;
the 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 is used as a non-transitory computer readable storage medium for storing non-transitory software programs and non-transitory computer executable programs. 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 implementations, memory 220 may optionally include remote memory located remotely from processor 210, which 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 appreciated that the device structure shown in fig. 12 is not limiting of the device 200 and may include more or fewer components than shown, or may be combined with certain components, 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 perform, but is not limited to, the steps of the embodiments shown in fig. 1 and 2.
The above-described embodiment of the apparatus 200 is merely illustrative, in which the units illustrated as separate components may or may not be physically separate, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment.
The embodiment of the present application also provides a computer-readable storage medium storing a processor-executable program for implementing the method shown in fig. 1 and 2 when executed by a processor.
Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the methods shown in fig. 1 and 2.
It is to be understood that all or some of the steps, systems, and methods disclosed above may be implemented in 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 both 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 known to those skilled 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 be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, 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.
The embodiments of the present application have been described in detail with reference to the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of one of ordinary skill in the art without departing from the spirit of the present application.
Claims (7)
1. The main lobe interference suppression method for the unmanned airborne radar is characterized by comprising the following steps of:
a plurality of distributed unmanned aerial vehicle radar are used for detecting a space target at the same time, so that echo data are obtained;
sample data is selected from the echo data, wherein the sample data is the 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 a 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 in which interference targets are not uniformly distributed on an echo distance unit;
removing the singular sample data;
acquiring the removed sample data as a training sample to estimate interference information;
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 with the first generalized inner product value larger than a rejection 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 rejection threshold;
removing all the data with the third generalized inner product value larger than the second removing threshold value from 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;
performing target detection according to the fourth generalized inner product value;
the step of removing the singular sample data includes:
setting a first numerical value, wherein the first numerical value is the number of samples removed;
sorting the first generalized inner product value from large to small; obtaining a sequencing list;
removing sample data corresponding to a first generalized inner product value of a first numerical value in the sorting list;
after the singular sample data are removed, the main lobe interference suppression method of the unmanned aerial vehicle radar further comprises the following steps:
taking the sample data from which the singular sample data is removed as a training sample;
calculating a second covariance matrix of the training sample;
calculating a second generalized inner product value of each distance unit in the training sample according to the second covariance matrix;
and performing target detection according to the second generalized inner product value.
2. The method for main lobe interference suppression of unmanned aerial vehicle radar according to claim 1, wherein after a plurality of distributed unmanned aerial vehicle radars detect a space target at the same time and echo data is obtained, the method further comprises:
the echo data is preprocessed, the preprocessing including at least one of pulse compression processing and pulse doppler processing.
3. The method of claim 1, wherein said calculating a first covariance matrix of the sample data is performed by:
R X ＝E[XX H ]；
wherein R is X A first covariance matrix representing sample data E]Representing the mean operation, H represents the conjugate transpose of the first covariance matrix, and X represents the sample data matrix after preprocessing.
4. The method of claim 1, wherein the calculating a first generalized inner product value for each range bin in the sample data according to a first covariance matrix of the sample data is performed by:
in the formula, GIP i A first generalized inner product value, X, representing an ith range bin i A data vector representing an ith distance element, H representing the conjugate transpose of the first covariance matrix,first covariance matrix R representing sample data X Is the inverse of (1).
5. The method for suppressing main lobe interference of unmanned airborne radar according to claim 1, wherein said step of rejecting said singular sample data comprises:
setting a first rejection threshold value;
and eliminating all sample data with the first generalized inner product value larger than the first eliminating threshold value.
6. An unmanned airborne radar main lobe interference suppression device, comprising:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any of claims 1-5.
7. Computer readable storage medium, characterized in that it has stored thereon a processor executable program for implementing the method according to any of claims 1-5 when being executed by a processor.
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