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CN108535704B - Signal pre-sorting method based on self-adaptive two-dimensional clustering - Google Patents

Signal pre-sorting method based on self-adaptive two-dimensional clustering Download PDF

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CN108535704B
CN108535704B CN201810313620.9A CN201810313620A CN108535704B CN 108535704 B CN108535704 B CN 108535704B CN 201810313620 A CN201810313620 A CN 201810313620A CN 108535704 B CN108535704 B CN 108535704B
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王俐
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Guizhou Institute of Technology
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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
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Abstract

本发明公开了一种基于自适应二维聚类的信号预分选方法，可用于复杂环境下新体制雷达的信号预识别，该方法包括以下步骤：利用天线阵列对目标二维定位，获得距离和方位信息；设置距离和方位的阈值初始值；计算目标距离维和方位维的相似度；根据计算的相似度和阈值，得到距离和方位的二维聚类；自适应调整距离和方位的阈值，统计聚类后距离维和方位维的相似度均值，直至均值小于距离或方位的阈值时，完成信号预分选。本发明采用先定位后预分选的方法，突破了传统雷达信号分选的局限，利用距离和方位的二维聚类处理，有效地解决了信号分选中日益严重的增批和漏批现象，提高了雷达信号分选的有效性。

Figure 201810313620

The invention discloses a signal pre-selection method based on self-adaptive two-dimensional clustering, which can be used for signal pre-identification of a new system radar in a complex environment. The method includes the following steps: using an antenna array to locate a target two-dimensionally to obtain a distance and orientation information; set the threshold initial value of distance and orientation; calculate the similarity of the target distance dimension and orientation dimension; obtain the two-dimensional clustering of distance and orientation according to the calculated similarity and threshold; adaptively adjust the threshold of distance and orientation, After the clustering, the average similarity of the distance dimension and the azimuth dimension is counted, and the signal pre-selection is completed until the average value is less than the threshold value of the distance or azimuth. The invention adopts the method of positioning first and then pre-sorting, which breaks through the limitation of traditional radar signal sorting, and uses two-dimensional clustering processing of distance and azimuth to effectively solve the increasingly serious phenomenon of increasing batches and missing batches in signal sorting. The effectiveness of radar signal sorting is improved.

Figure 201810313620

Description

Signal pre-sorting method based on self-adaptive two-dimensional clustering
Technical Field
The invention relates to the technical field of radar signal processing in electronic countermeasure, in particular to a signal pre-sorting method based on self-adaptive two-dimensional clustering.
Background
Electronic countermeasure is an important combat means for attack and defense in modern war, and radar signal sorting is to sort radar radiation signals with unknown parameters under a complex electromagnetic environment, and is an important component of an electronic countermeasure system.
Modern radars have developed in the direction of multifunctional and multipurpose new systems and new technologies, one radar may have multiple working states and multiple systems, and various complex waveforms are often designed, so that regularity of signal sorting and identification is destroyed, a radar reconnaissance system is greatly challenged, the number of various electronic countermeasure equipment is increased, the radar reconnaissance system is in a complex and intensive electromagnetic signal environment, complexity and operation of signal processing are mainly concentrated on signal sorting, and the level of signal sorting is an important index for measuring the advancement of electronic countermeasure equipment.
For a complex electromagnetic environment, radar radiation source signals are often overlapped in a time domain, a space domain and a frequency domain, the traditional radar signal sorting method has serious batch increasing and batch missing phenomena, the high density of the signal environment also causes the traditional sorting processing to have large calculation amount, and the phenomena can cause the failure of signal sorting, so that a new signal sorting method needs to be explored urgently. The invention adopts the method of positioning first and then pre-sorting, reduces the operation amount of subsequent signal sorting processing, and can achieve the purpose of correctly sorting signals.
Disclosure of Invention
The invention aims to: in view of the above problems, a method is provided which can improve the accuracy of sorting with a small amount of calculation.
The invention provides a signal pre-sorting method based on self-adaptive two-dimensional clustering, which comprises the following steps:
1) two-dimensionally positioning a target by using an antenna array to obtain a distance and direction (R, theta) information sequence;
2) setting initial values of threshold values (delta R, delta theta) of distance and azimuth;
3) calculating the similarity (d) between the target distance dimension and the orientation dimensionR,dθ)；
4) According to the calculated similarity and the threshold value, two-dimensional clustering of the distance and the direction is obtained;
5) self-adaptively adjusting the threshold values of the distance and the orientation, and counting the similarity mean value of the distance dimension and the orientation dimension after clustering
Figure BDA0001623152340000021
And when the average value is smaller than the threshold value of the distance or the direction, finishing signal pre-sorting.
The method comprises the following steps: the two-dimensional positioning of the array antenna on the target specifically comprises the following steps: by using a large-caliber sparse array, when R is less than or equal to 2D2The target processing array near field area at lambda is used for simultaneously obtaining target distance and azimuth information based on the array spherical wave model, wherein R is the target distance, D is the array caliber, and lambda is the target wavelength; positioning is firstly carried out on unsorted signals of the broadband radar, scanning is carried out on different distances R and azimuth angles theta, and a space spectrum is calculated for each stepping frequency point k:
Figure BDA0001623152340000022
wherein N is a covariance matrix of the noise,
Figure BDA0001623152340000023
the array flow vector under different frequency points is obtained, and the space spectrum of the broadband radar signal is as follows:
Figure BDA0001623152340000024
in the above formula, K is the number of the stepping frequency points; and searching a peak value of the radar signal space spectrum, wherein the scanning distance and the direction corresponding to the peak value are the distance and direction information of the target.
In the step 2), initial values of distance and orientation thresholds are set according to the precision of the sparse array for target distance measurement and direction finding.
More specifically, in step 3), the similarity between the distance dimension and the orientation dimension is calculated by using the euclidean distance:
Figure BDA0001623152340000025
wherein d isREuclidean distance in the distance dimension, M is the number of pulse sequences, RrefTaking the distance measured for the first time as a reference distance;
Figure BDA0001623152340000026
wherein d isθEuclidean distance, θ, in the azimuthal dimensionrefFor the reference orientation, the first measured orientation is typically taken as the reference orientation.
More specifically, in the step 4), when the pulse sequence satisfies d at the same time in the distance dimension and the azimuth dimensionRΔ R and d ≤θWhen the pulse sequence is less than or equal to delta theta, the pulse sequences are grouped into one class, otherwise, a new class is reconstructed, and the like is performed until all the pulse sequences complete two-dimensional clustering.
More specifically, in the step 5), an average value of the pulse sequence distance and the azimuth dimension similarity is calculated by using the following formula:
Figure BDA0001623152340000031
and
Figure BDA0001623152340000032
while at the same timeSatisfy the requirement of
Figure BDA0001623152340000033
And
Figure BDA0001623152340000034
and (3) adjusting the threshold values of the distance and the direction, and repeating the steps 3), 4) and 5) until the threshold values do not need to be adjusted again, and finishing signal pre-sorting.
The method has the advantages that the method of positioning firstly and then pre-sorting is adopted, the limitation of traditional radar signal sorting is broken through, the phenomena of increasing batches and missing batches which are increasingly serious in signal sorting are effectively solved by utilizing two-dimensional clustering processing of distance and direction, and the effectiveness of radar signal sorting is improved.
Drawings
FIG. 1 is a flow chart of a signal pre-sorting method based on adaptive two-dimensional clustering according to the present invention;
FIG. 2 is a schematic diagram of the frequency pre-sorting result in the simulation experiment of the present invention;
FIG. 3 is a schematic diagram of a distance pre-sorting result in a simulation experiment according to the present invention;
FIG. 4 is a schematic diagram of the pre-sorting result of the orientation in the simulation experiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings. All other methods, which can be obtained by a person skilled in the art without any inventive step based on the method of the present invention, are within the scope of the present invention.
Fig. 1 is a flowchart of a signal pre-sorting method based on adaptive two-dimensional clustering according to the present invention, and as shown in the figure, the method includes the following steps:
step S1, two-dimensionally positioning the target by using the antenna array to obtain a distance and direction (R, theta) information sequence;
the two-dimensional positioning of the target utilizes a large-caliber sparse array, and when R is less than or equal to 2D2Target processing array at/lambda timeIn the near field region, target distance and azimuth information can be simultaneously obtained based on an array spherical wave model, wherein R is the target distance, D is the array caliber, and lambda is the target wavelength. Positioning is firstly carried out on unsorted signals of the broadband radar, scanning is carried out on different distances R and azimuth angles theta, and a space spectrum is calculated for each stepping frequency point k:
Figure BDA0001623152340000035
where N is the covariance matrix of the noise, αfk(R, theta) is an array flow vector under different frequency points, and then the space spectrum of the broadband radar signal is as follows:
Figure BDA0001623152340000041
in the above formula, K is the number of the stepped frequency points. And searching a peak value of the radar signal space spectrum, wherein the scanning distance and the direction corresponding to the peak value are the distance and direction information of the target.
Step S2, setting initial values of threshold values (delta R, delta theta) of distance and orientation;
the threshold is an initial value of a distance threshold and an orientation threshold which are set according to the precision of the sparse array for measuring distance and direction of the target.
Step S3, calculating the similarity of the target distance dimension and the orientation dimension;
and calculating the similarity between the distance dimension and the orientation dimension by adopting the Euclidean distance:
Figure BDA0001623152340000042
wherein d isREuclidean distance in the distance dimension, M is the number of pulse sequences, RrefFor the reference distance, the first measured distance is generally taken as the reference distance.
Figure BDA0001623152340000043
Wherein d isθEuclidean distance, θ, in the azimuthal dimensionrefFor reference orientation, takeThe first measured orientation is the reference orientation.
Step S4, according to the calculated similarity and threshold, obtaining two-dimensional clustering of distance and orientation;
the two-dimensional clustering is to utilize the pulse sequence to simultaneously satisfy d in the distance dimension and the azimuth dimensionRΔ R and d ≤θWhen the pulse sequence is less than or equal to delta theta, the pulse sequences are grouped into one class, otherwise, a new class is reconstructed, and the like is performed until all the pulse sequences complete two-dimensional clustering.
Step S5, self-adaptively adjusting the threshold values of the distance and the orientation, and counting the similarity mean value of the distance dimension and the orientation dimension after clustering
Figure BDA0001623152340000044
And finishing signal pre-sorting until the mean value is smaller than the threshold value of the distance or the direction.
Calculating the mean value of the pulse sequence distance dimension and the azimuth dimension similarity by the following formula:
Figure BDA0001623152340000045
and
Figure BDA0001623152340000046
when simultaneously satisfying
Figure BDA0001623152340000047
And
Figure BDA0001623152340000048
and adjusting the threshold values of the distance and the orientation, and repeating the steps S3, S4 and S5 until the threshold values do not need to be adjusted again, and finishing the signal pre-sorting.
The technical effects achieved by the present invention are illustrated by simulation experiments.
The distance-direction information of the four broadband frequency agile radar radiation sources simultaneously existing in an airspace is respectively (10200m,1 degree), (10400m,2 degrees), (10600m,3 degrees) and (10800m,4 degrees), a first target (3260MHz,3290MHz) is separated by 10MHz frequency agile, a second target (3300MHz,3340MHz) is separated by 40MHz frequency agile, a third target (3340MHz,3370MHz) is separated by 10MHz frequency agile, and a fourth target (3220MHz,3250MHz) is separated by 10MHz frequency agile and 20MHz frequency agile. The number of pulses M is 100, the initial threshold values of the distance and the direction are 100M and delta theta is 0.5 degrees, and the results of frequency pre-sorting, distance pre-sorting and direction pre-sorting in two-dimensional clustering are respectively shown in fig. 2, fig. 3 and fig. 4, so that the purpose of correctly pre-sorting multiple radar signals with mutually overlapped airspace, time domain and frequency domain in a complex electromagnetic environment is achieved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (4)

1.一种基于自适应二维聚类的信号预分选方法，其特征在于，该方法包括：1. a signal pre-sorting method based on adaptive two-dimensional clustering, is characterized in that, the method comprises: 1)利用天线阵列对目标二维定位，获得距离和方位信息(R,θ)序列；1) Use the antenna array to locate the target two-dimensionally, and obtain the sequence of distance and azimuth information (R, θ); 2)设置距离和方位的阈值(ΔR,Δθ)初始值；2) Set the initial value of the threshold (ΔR, Δθ) of distance and orientation; 3)计算目标距离维和方位维的相似度(dR,dθ)；3) Calculate the similarity (d R , d θ ) of the target distance dimension and azimuth dimension; 4)根据计算的相似度和阈值，得到距离和方位的二维聚类；4) According to the calculated similarity and threshold, two-dimensional clusters of distance and orientation are obtained; 5)自适应调整距离和方位的阈值，统计聚类后距离维和方位维的相似度均值
Figure FDA0003299127000000011
直至均值小于距离或方位的阈值时，完成信号预分选；5) Adaptively adjust the thresholds of distance and orientation, and count the average similarity of distance dimension and orientation dimension after clustering
Figure FDA0003299127000000011
Until the mean value is less than the distance or azimuth threshold, the signal pre-selection is completed; 所阵列天线对目标二维定位具体为：The two-dimensional positioning of the target by the array antenna is as follows: 利用大口径稀疏阵列，当R≤2D2/λ时目标处理阵列近场区域，基于阵列球面波模型可同时获得目标距离和方位信息，R为目标距离，D为阵列口径，λ为目标波长；对于宽带雷达未分选信号先实施定位，在不同的距离R和方位角θ上扫描，对各步进频率点k计算空间谱：Using a large aperture sparse array, when R≤2D 2 /λ, the target deals with the near-field area of the array. Based on the array spherical wave model, the target distance and azimuth information can be obtained at the same time, where R is the target distance, D is the array aperture, and λ is the target wavelength; For the unsorted signal of the broadband radar, first perform positioning, scan at different distances R and azimuth angles θ, and calculate the spatial spectrum for each step frequency point k:
Figure FDA0003299127000000012
Figure FDA0003299127000000012
 其中，N为噪声的协方差矩阵，
Figure FDA0003299127000000013
为不同频率点下的阵列流矢量，
Figure FDA0003299127000000014
为不同频段时的空间扫描距离，则宽带雷达信号空间谱为：where N is the covariance matrix of noise,
Figure FDA0003299127000000013
is the array flow vector at different frequency points,
Figure FDA0003299127000000014
is the spatial scanning distance in different frequency bands, then the spatial spectrum of the broadband radar signal is:
Figure FDA0003299127000000015
Figure FDA0003299127000000015
 上式中K为步进频点个数；搜索雷达信号空间谱的峰值，此峰值对应的扫描距离和方位就是目标的距离和方位信息；In the above formula, K is the number of step frequency points; search for the peak of the radar signal spatial spectrum, and the scanning distance and azimuth corresponding to this peak are the distance and azimuth information of the target; 所述步骤(3)中，采用欧氏距离计算距离维和方位维的相似度：In the step (3), the Euclidean distance is used to calculate the similarity of the distance dimension and the azimuth dimension:
Figure FDA0003299127000000016
其中dR为距离维的欧氏距离，M为脉冲序列个数，Rref为参考距离，一般取第一次测得的距离为参考距离；
Figure FDA0003299127000000016
Among them, d R is the Euclidean distance of the distance dimension, M is the number of pulse sequences, and R ref is the reference distance. Generally, the distance measured for the first time is taken as the reference distance;
Figure FDA0003299127000000017
其中dθ为方位维的欧氏距离，θref为参考方位，取第一次测得的方位为参考方位。
Figure FDA0003299127000000017
Among them, d θ is the Euclidean distance of the azimuth dimension, θ ref is the reference azimuth, and the azimuth measured for the first time is taken as the reference azimuth. 2.根据权利要求1所述的方法，其特征在于，所述步骤(2)中，依据稀疏阵列对目标测距和测向的精度，设置距离和方位阈值的初始值。2 . The method according to claim 1 , wherein, in the step (2), the initial values of the distance and orientation thresholds are set according to the accuracy of the sparse array in ranging and direction finding of the target. 3 . 3.根据权利要求1所述的方法，其特征在于，所述步骤(4)中，当脉冲序列在距离维和方位维相似度同时满足dR≤ΔR和dθ≤Δθ时，序列被聚为一类，否则重新构建一个新类，以此类推，直到所有脉冲序列完成二维聚类。3. The method according to claim 1, wherein, in the step (4), when the pulse sequence satisfies d R ≤ ΔR and d θ ≤ Δθ simultaneously in the distance dimension and azimuth dimension similarity, the sequence is aggregated into One class, otherwise rebuild a new class, and so on, until all pulse sequences complete two-dimensional clustering. 4.根据权利要求3所述的方法，其特征在于，所述步骤(5)中，4. method according to claim 3, is characterized in that, in described step (5), 计算脉冲序列距离维和方位维相似度的均值，用如下公式：Calculate the mean value of the similarity between the distance dimension and the azimuth dimension of the pulse sequence, using the following formula:
Figure FDA0003299127000000021
和
Figure FDA0003299127000000022
Figure FDA0003299127000000021
and
Figure FDA0003299127000000022
 当同时满足
Figure FDA0003299127000000023
和
Figure FDA0003299127000000024
时，调整距离和方位的阈值，重复所述步骤(3)、(4)和(5)直至无需再调整阈值，则完成信号预分选。while satisfying
Figure FDA0003299127000000023
and
Figure FDA0003299127000000024
, adjust the thresholds of distance and azimuth, repeat the steps (3), (4) and (5) until the thresholds do not need to be adjusted any more, then the signal pre-selection is completed.
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