CN116540185A - Sorting identification method based on density clustering - Google Patents
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- CN116540185A CN116540185A CN202310817968.2A CN202310817968A CN116540185A CN 116540185 A CN116540185 A CN 116540185A CN 202310817968 A CN202310817968 A CN 202310817968A CN 116540185 A CN116540185 A CN 116540185A
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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
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Abstract
The invention provides a sorting and identifying method based on density clustering, which comprises the following steps: step 1, acquiring a pulse positioning information data set D and marking core elements in the pulse positioning information data set D; step 2, marking all core elements with reachable density or density connection as the same cluster serial number; step 3, traversing all clusters, and enabling core elements in each cluster to be-the intra-neighborhood elements are marked with the same cluster number as the core element; step 4, carrying out square statistics on the elements in each cluster according to the frequency and the pulse width; step 5, judging whether the elements in each cluster are effective according to the frequency and pulse width square statistics; and 6, considering the elements with effective frequency and pulse width square statistics as elements passing through sorting, and obtaining parameters of corresponding targets according to the elements passing through sorting. The invention can remove impurities from complex electromagnetic environment without priori informationAnd (3) scattering signals, successfully sorting and identifying target radar/radiation source signals, and obtaining accurate electromagnetic parameters and accurate positioning information of the target.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a sorting identification method based on density clustering.
Background
Along with the development of electronic countermeasure, various radars in the future battlefield are various in types and complex in system, meanwhile, dense background signals such as various communication signals, civil navigation signals and the like exist, and the sorting difficulty of target signals is extremely high.
The prior signal processing technology generally needs prior information (frequency, repetition frequency, pulse width and the like) of the target to perform pre-sorting, and then uses algorithms such as angle sorting and the like to perform target sorting identification. The method has the following defects:
(1) The prior information can not be acquired, the authenticity and the real-time performance of the prior information can not be ensured, and the prior information can not adapt to the battlefield situation of future instantaneous change;
(2) Algorithms such as angle stacking and the like cannot effectively restrict the relation among elements meeting the requirements, and meanwhile, the resolution capability of a long-distance adjacent target is poor;
(3) The impurity removing capability is weak, and the influence of stray signals with similar electromagnetic parameters is easy to occur.
Disclosure of Invention
The invention aims to provide a sorting and identifying method based on density clustering, which aims to solve the problems of the prior signal processing technology.
The invention provides a sorting and identifying method based on density clustering, which comprises the following steps:
step 1, acquiring a pulse positioning information data set D and marking core elements in the pulse positioning information data set D;
step 2, marking all core elements with reachable density or density connection as the same cluster serial number;
step 3, traversing all clusters, and enabling core elements in each cluster to be-intra-neighborhood element taggingIs the same cluster number as the core element;
step 4, carrying out square statistics on the elements in each cluster according to the frequency and the pulse width;
step 5, judging whether the elements in each cluster are effective according to the frequency and pulse width square statistics;
and 6, considering the elements with effective frequency and pulse width square statistics as elements passing through sorting, and obtaining parameters of corresponding targets according to the elements passing through sorting.
Further, step 1 includes:
all pulse positioning information data sets D are obtained through calculation by utilizing the angle measurement information of each pulse and combining the attitude and position information of the current platform;
traversing the elements in the pulse positioning information data set D in sequence, if one element is-marking the element as a core element if the number Np of elements in the neighborhood is equal to or greater than Mpts; wherein, mpts is the density threshold value that sets for.
Further, the density can be up to:
if the element p reaches the element q through a plurality of intermediate elements with reachable direct densities, the element p is called as the element p with reachable relative density to the element q; wherein, the direct density can be reached as follows: if element p is in element qIn the neighborhood, and the element q is a core element, the element p is called as the direct density of the element q can be reached.
Further, the density connection in step 2 means:
if the element p and the element q are reachable by means of the element o density, the element p and the element q are said to be connected in density.
Further, in step 5, determining whether the elements in each cluster are valid according to the frequency and pulse width square statistics includes:
and traversing the frequency and pulse width square statistical result, if the ratio of the number of elements contained in a square to the number of elements contained in the maximum square is not smaller than a set ratio, considering the elements in the square to be effective, and otherwise, invalidating the elements.
Preferably, the set ratio is 25%.
Further, the obtaining the parameters of the corresponding target according to the sorted elements in the step 6 includes:
counting electromagnetic parameters of elements passing through sorting in the clusters to obtain electromagnetic parameters of corresponding targets;
and calculating the position gravity center of the elements passing through sorting in the cluster to obtain the accurate positioning information of the corresponding target.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the method can remove stray signals from a complex electromagnetic environment under the condition of no priori information, successfully sort and identify the target radar/radiation source signals, and obtain accurate electromagnetic parameters and accurate positioning information of the target.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly describe the drawings in the embodiments, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a typical radar/radiation source signal localization profile.
FIG. 2a is a graph showing electromagnetic parameters and position distribution before clustering in an embodiment of the present invention.
FIG. 2b is a graph showing electromagnetic parameters and position distribution after clustering in an embodiment of the present invention.
FIG. 2c is a graph showing electromagnetic parameters and position distribution after square statistics according to an embodiment of the present invention.
Fig. 3 is a flowchart of a sorting identification method based on density clustering in an embodiment of the invention.
Fig. 4a is a frequency square statistic chart of clustering targets 1 in an embodiment of the invention.
Fig. 4b is a pulse width square statistical chart of the clustered targets 1 in the embodiment of the invention.
Fig. 4c is a frequency square statistic chart of clustering targets 2 in an embodiment of the invention.
Fig. 4d is a pulse width square statistical chart of the clustered targets 2 in the embodiment of the invention.
Fig. 4e is a frequency square statistic chart of clustering targets 3 in an embodiment of the invention.
Fig. 4f is a pulse width square statistical chart of the clustered targets 3 in the embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
The principle of the embodiment is as follows:
the method adopts a position clustering algorithm to sort signals, is influenced by factors such as power fluctuation, phase noise and the like, radar/radiation source signal positioning information can be randomly distributed in any irregular shape, the typical distribution situation is shown in figure 1, and meanwhile, the clustering algorithm cannot predict the clustering target number without prior information, and a large amount of stray signals are doped in target signals, so that the selected clustering algorithm has good adaptability and impurity removing capability, and the DBSCAN (sensitivity-based spatial clustering of applications with noise) clustering algorithm is selected in the method for analyzing in summary.
Position clustering is carried out on single-pulse positioning information based on a DBSCAN clustering algorithm, position clusters of different targets are selected, direct statistics is carried out on electromagnetic parameters of pulse signals in the clusters, secondary sorting is carried out according to direct statistics results, accurate electromagnetic parameters of different target clusters are finally obtained, and meanwhile, the position gravity center of the target clusters after secondary sorting is calculated, so that accurate positioning information of each target can be obtained.
The definition involved in this embodiment is as follows:
-a neighborhood: in the sample data, for any elementX i Distance from it is not more than the set radius +.>Is referred to as an elementX i Is->-a neighborhood;
core elements: if a certain element-the number of elements in the neighborhood is not smaller than the density threshold Mpts, then the element is referred to as core element;
the direct density can be achieved: if element p is in element qIn the neighborhood, and the element q is a core element, the element p is called as the direct density of the element p relative to the element q is reachable;
the density can be achieved: if the element p can reach the element q through a plurality of intermediate elements with reachable direct densities, the element p is called as the element p with reachable relative density to the element q;
density connection: if the element p and the element q are reachable by means of the element o density, the element p and the element q are said to be connected in density.
In the embodiment, three radar signals in a complex electromagnetic environment are sorted by adopting a sorting and identifying method based on density clustering, and finally accurate electromagnetic parameters and accurate positioning results of the three radars are obtained. The simulation conditions are as follows:
(1) Simulating a complex electromagnetic environment, wherein three radar signals and spurious signals exist in the time domain at the same time, electromagnetic parameters of the three radar signals and the spurious signals are mutually overlapped, and the distribution situation is shown in figure 2 a;
(2) The first radar frequency agility range is 3000 MHz-4000 MHz, the pulse width jitter range is 5μs～15μsThe second radar frequency agility range is 3100 MHz-3500 MHz, the pulse width jitter range is 10μs～20μsThe third radar frequency is fixed 3200MHz + -1 MHz, and the pulse width is fixed 8μs±1μsSpurious signal frequency agility range 2900 MHz-4100 MHz, pulse width jitter range 0.1μs～25μs；
(3) According to the simulation data distribution, setting-radius of neighborhood->35 m->-the density threshold MPts in the neighborhood is 25.
Based on the design principle, as shown in fig. 3, the specific steps of sorting three radar signals in a complex electromagnetic environment by adopting the sorting identification method based on density clustering include:
step 1, calculating to obtain all pulse positioning information data sets D by utilizing the angle measurement information of each pulse and combining the attitude and position information of the current three radars; traversing the elements in the pulse positioning information data set D in sequence, if one element is-marking the element as a core element if the number Np of elements in the neighborhood is equal to or greater than Mpts;
step 2, marking all core elements with reachable density or density connection as the same cluster serial number;
step 3, traversing all clusters, and enabling core elements in each cluster to be-neighborhoodThe intra-domain element is marked with the same cluster number as the core element; three clusters with cluster numbers of 1-3 are finally obtained, and as the spurious signals are spread over the area, the position clusters can not completely eliminate the spurious signals, and the 3 clustering targets all contain a small amount of spurious signals, as shown in fig. 2 b.
Step 4, performing square statistics on elements in each cluster according to the frequency and the pulse width to obtain frequency and pulse width histograms of 3 clustering targets, as shown in fig. 4a, 4b, 4c, 4d, 4e and 4 f;
step 5, traversing the frequency and pulse width square statistical result, if the ratio of the number of elements contained in a square to the number of elements contained in the maximum square is not smaller than a set ratio (preferably 25%), considering the elements in the square to be effective, otherwise, invalidating the elements;
step 6, considering the elements with effective frequency and pulse width square statistics as elements passing sorting, and further removing stray signals with similar positions by secondary sorting of the square statistics, as shown in fig. 2 c; parameters of the corresponding target are then obtained from the elements that pass the sorting:
counting electromagnetic parameters of the elements in the clusters through sorting to obtain electromagnetic parameters of 3 targets, wherein the result is basically consistent with the preset electromagnetic parameters;
and calculating the position gravity centers of the elements passing through sorting in the clusters to obtain the accurate positioning information of 3 targets.
The verification shows that the method can remove stray signals from a complex electromagnetic environment under the condition of no priori information, successfully sort and identify target radar/radiation source signals, and obtain accurate electromagnetic parameters and accurate positioning information of the target.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The sorting and identifying method based on density clustering is characterized by comprising the following steps:
step 1, acquiring a pulse positioning information data set D and marking core elements in the pulse positioning information data set D;
step 2, marking all core elements with reachable density or density connection as the same cluster serial number;
step 3, traversing all clusters, and enabling core elements in each cluster to be-the intra-neighborhood elements are marked with the same cluster number as the core element;
step 4, carrying out square statistics on the elements in each cluster according to the frequency and the pulse width;
step 5, judging whether the elements in each cluster are effective according to the frequency and pulse width square statistics;
and 6, considering the elements with effective frequency and pulse width square statistics as elements passing through sorting, and obtaining parameters of corresponding targets according to the elements passing through sorting.
2. The density cluster-based sort and identification method of claim 1, wherein step 1 comprises:
all pulse positioning information data sets D are obtained through calculation by utilizing the angle measurement information of each pulse and combining the attitude and position information of the current platform;
traversing the elements in the pulse positioning information data set D in sequence, if one element is-marking the element as a core element if the number Np of elements in the neighborhood is equal to or greater than Mpts; wherein, mpts is the density threshold value that sets for.
3. The sorting and identifying method based on density clustering according to claim 1, wherein the density reachable in step 2 means:
if the element p reaches the element q through a plurality of intermediate elements with reachable direct densities, the element p is called as the element p with reachable relative density to the element q; wherein, the direct density can be reached as follows: if element pIs at element qIn the neighborhood, and the element q is a core element, the element p is called as the direct density of the element q can be reached.
4. A sorting and identifying method based on density clustering according to claim 1 or 3, wherein in step 2, the density linkage means:
if the element p and the element q are reachable by means of the element o density, the element p and the element q are said to be connected in density.
5. The density cluster-based sort and recognition method of claim 1, wherein determining whether the elements in each cluster are valid based on frequency and pulse width square statistics in step 5 comprises:
and traversing the frequency and pulse width square statistical result, if the ratio of the number of elements contained in a square to the number of elements contained in the maximum square is not smaller than a set ratio, considering the elements in the square to be effective, and otherwise, invalidating the elements.
6. The density-cluster-based sort and identification method of claim 5, wherein the set ratio is 25%.
7. The density-cluster-based sort recognition method according to claim 1, wherein the obtaining of the parameters of the corresponding target from the elements passing through the sort in step 6 includes:
counting electromagnetic parameters of elements passing through sorting in the clusters to obtain electromagnetic parameters of corresponding targets;
and calculating the position gravity center of the elements passing through sorting in the cluster to obtain the accurate positioning information of the corresponding target.
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| CN117310452A (en) * | 2023-11-29 | 2023-12-29 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method, device, computer equipment and storage medium for determining electromagnetic signal leakage |
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| CN117310452A (en) * | 2023-11-29 | 2023-12-29 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method, device, computer equipment and storage medium for determining electromagnetic signal leakage |
| CN117310452B (en) * | 2023-11-29 | 2024-03-26 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Method, device, computer equipment and storage medium for determining electromagnetic signal leakage |
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