CN107144812B - Single-station mobile automatic positioning method - Google Patents
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- CN107144812B CN107144812B CN201710519069.9A CN201710519069A CN107144812B CN 107144812 B CN107144812 B CN 107144812B CN 201710519069 A CN201710519069 A CN 201710519069A CN 107144812 B CN107144812 B CN 107144812B
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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
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
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Abstract
The invention relates to a single-station mobile automatic positioning method, which comprises the following steps: acquiring a set of azimuth angles of the emission source, and storing the set of azimuth angles into a mobile station set; and analyzing by adopting an improved K-Means clustering algorithm, namely selecting three points as a clustering basis, storing the circle center and the radius of an inscribed circle of a triangle with the three points as vertexes into an inscribed circle set in an object mode, and generating acquired data into a thermodynamic diagram layer to be superposed on a map for displaying, namely acquiring the interference source position. The invention selects high-quality direction-finding data, makes a large amount of direction-finding data more centralized and accurate, removes divergent direction-finding data, and uses an improved K-means clustering method to perform clustering analysis on the intersection points of direction-finding lines, so that the positioning result is more reliable and accurate, and the invention is beneficial to fast positioning illegal emission such as radio interference sources and the like by users.
Description
Technical Field
The invention relates to the field of radio monitoring and application of innovative direction-finding positioning algorithm, in particular to a single-station mobile automatic positioning method.
Background
Most of the existing positioning technologies for interference sources in radio monitoring still use direction-finding and cross-plotting positioning methods, the direction-finding data volume is large, but the actually used data volume is not too large, and a large part of data is discarded. Most of the data are discarded, which brings great uncertainty to the positioning result, and the user cannot quickly position the interference source, which brings serious loss to the frequency management.
Disclosure of Invention
The invention aims to provide a single-station mobile automatic positioning method, which is used for selecting high-quality direction-finding data in order to eliminate inaccurate data, so that a large amount of direction-finding data is more concentrated and accurate, scattered direction-finding data is eliminated, and an improved K-means clustering method is used for carrying out clustering analysis on intersection points of direction-finding lines obtained by direction finding, so that a positioning result is more reliable and accurate, and the method is favorable for a user to quickly position illegal emission such as a radio interference source and the like.
The invention is realized by the following steps:
a single-station mobile automatic positioning method comprises the following steps:
s1, acquiring a set of azimuth angles of the emission source through the direction-finding machine, and storing the set of azimuth angles into a mobile monitoring station set;
filtering the data which are separated from the core value by adopting a threshold quality screening method, and then storing all intersection points of the indicator line which is subjected to sequencing dynamic insertion screening into an intersection point set;
s3, analyzing the data obtained in the step S2 by adopting an improved K-Means clustering algorithm, selecting three points as a clustering basis, calculating the circle center and the radius of an inscribed circle of a triangle with the three points as vertexes, and storing the circle center and the radius of the inscribed circle into an inscribed circle set in an object mode;
s4, calculating the stacking times of the inscribed circles, performing statistical operation, taking the stacking times as the color assignment weight of the inscribed circles, and taking the weight as the scale of the color depth during rendering;
and S5, adding the inscribed circle set data overlapped in the step S4 into a thermodynamic diagram generation method, generating a thermodynamic diagram layer, overlapping the thermodynamic diagram layer on a map for displaying, wherein the area with deep colors according to the thermodynamic diagram is the position of the interference source.
Further, in step S1, a set of azimuth angles at which the transmission sources are located is obtained by the direction-finding machine and stored in the mobile monitoring station set, which specifically includes:
the direction finder obtains the incoming wave direction of the emission source in the travelling process of a single mobile monitoring station, obtains the true north direction degree after the compass marks on the map, and then obtains the included angle with the true north direction and stores the included angle in the mobile monitoring station set.
Further, in step S2, storing all the intersections of the filtered direction lines in the intersection set by using the sorted dynamic insertion, specifically including:
setting the straight line inclination angle to
Setting the direction finding degree of the mobile monitoring station as azimuth, and converting into: bearing = azimuth-90 °;
setting the coordinates of the mobile monitoring station at the position 1 as: (X1, Y1), direction finding Angle
And the coordinates of the mobile monitoring station at the position 2 are as follows: (X2, Y2), direction finding Angle
The coordinates of the intersection point are: (X, Y);
the following algorithm is adopted to carry out circular traversal on the three direction indicating lines, so that the intersection points of all two direction indicating lines can be obtained and stored in an intersection point set, and the algorithm is as follows:
further, in step S3, the K-Means clustering algorithm specifically includes:
s301, specifying the number K value of clusters needing to be divided;
s302, randomly selecting K initial data object points as initial clustering centers;
s303, calculating the distance from each of the rest data objects to the K initial clustering centers, classifying the data objects into the cluster class of the center closest to the data objects, and recording the serial numbers of the data objects;
s304, calculating the center of each cluster, updating, and judging whether the center of the new class and the center of the original class meet the specified precision convergence function, if so, finishing the adjustment of the data object, otherwise, returning to the step S303 to readjust.
Further, in the K-Means clustering algorithm, the data are clustered into n clusters, then the n clusters are clustered into K clusters, and are gradually classified into 3 clusters, wherein n is greater than 3, and 3< K < n.
Further, in step S3, the circle center and the radius of the inscribed circle of the triangle with the three points as the vertices are calculated, and the circle center and the radius of the inscribed circle are stored in the inscribed circle set in an object manner, specifically:
setting the coordinates of three vertexes of the triangle as A (Xa, Ya), B (Xb, Yb) and C (Xc, Yc), and obtaining the three-side lengths a, B and C of the triangle;
setting p = (a + b + c)/2, then the triangle area can be obtained:
the coordinates of the center of the inscribed circle are set as (centerX, centerY), and the radius of the inscribed circle is set as:
then, there are:
namely, the center and radius of the inscribed circle are obtained.
Furthermore, when the intersection point of the direction indicating line exceeds the transmission range of the radio wave of the emission source, drawing of an inscribed circle is not carried out;
and setting intersection points on a reverse extension line of a direction indicating line of the mobile monitoring station, namely setting a vector included angle between a unit vector of a direction-finding angle of the mobile monitoring station and a connecting line of the mobile monitoring station and a certain intersection point to be 180 degrees, and eliminating the direction indicating line in the system without participating in calculation, wherein the direction indicating line is considered as a false direction.
Further, in step S4, the method further includes calculating the occurrence frequency of the direction indicating degree according to the position relationship and the radius of all the obtained inscribed circles, and finally normalizing the occurrence frequency of the direction indicating degree to obtain the final color assignment weight data, wherein the occurrence frequency of the direction indicating degree is calculated according to the position relationship and the radius, and the position relationship includes only three cases of intersection, inclusion, and inscribed.
Further, the method further comprises the step of obtaining the position of the interference source by a confidence ellipse algorithm, specifically:
and (4) storing all the inscribed circles obtained in the step (S3) in a confidence ellipse calculation set without counting the radius, calculating the confidence range of the overall data, setting the confidence probability, and drawing an ellipse according to the confidence range of the overall data, namely obtaining the position of the interference source.
Further, it is set that when new data is added into the mobile monitoring station set, the steps S1 to S5 are executed circularly
The invention has the beneficial effects that:
(1) selecting high-quality direction-finding data to enable a large amount of direction-finding data to be more concentrated and accurate, removing divergent direction-finding data, and performing cluster analysis on intersection points of direction-finding obtained direction-finding lines by using an improved K-means clustering method to enable a positioning result to be more reliable and accurate, so that a user can quickly position illegal emission such as a radio interference source and the like;
(2) the method is suitable for positioning the interference source in a complex environment, the overall algorithm design is novel and strong in practicability, and accurate interference source positioning can be quickly obtained;
(3) by adopting the thermodynamic diagram display method, the display effect is more visual and elegant, and a user can obtain a positioning result more efficiently.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of an automatic positioning method of the present invention;
FIG. 2 is a schematic diagram of cellular matrix addressing in accordance with the present invention;
FIG. 3 is a schematic diagram of hexagonal cell addressing in accordance with the present invention;
FIG. 4 is a progressive color band diagram of the present invention;
FIG. 5 is a thermodynamic diagram illustrating the effect of the present invention;
fig. 6 is an actually measured screenshot of the cluster superposition thermodynamic diagram of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In an embodiment, referring to fig. 1, a single-station mobile automatic positioning method includes the following steps:
s1, acquiring a set of azimuth angles of the emission source through the direction-finding machine, and storing the set of azimuth angles into a mobile monitoring station set;
filtering the data which are separated from the core value by adopting a threshold quality screening method, and then storing all intersection points of the indicator line which is subjected to sequencing dynamic insertion screening into an intersection point set;
s3, analyzing the data obtained in the step S2 by adopting an improved K-Means clustering algorithm, selecting three points as a clustering basis, calculating the circle center and the radius of an inscribed circle of a triangle with the three points as vertexes, and storing the circle center and the radius of the inscribed circle into an inscribed circle set in an object mode;
s4, calculating the stacking times of the inscribed circles, performing statistical operation, taking the stacking times as the color assignment weight of the inscribed circles, and taking the weight as the scale of the color depth during rendering;
and S5, adding the inscribed circle set data overlapped in the step S4 into a thermodynamic diagram generation method, generating a thermodynamic diagram layer, overlapping the thermodynamic diagram layer on a map for displaying, wherein the area with deep colors according to the thermodynamic diagram is the position of the interference source.
In this embodiment, referring to fig. 2 and fig. 3, in step S2, the data out of the core value is filtered by using a filtering method, which includes a method of cellular matrix addressing.
In this embodiment, since the direction-finding angle obtained by the monitoring station is an angle with the due north direction, which is different from the slope angle of the straight line in the rectangular coordinate system, to obtain the correct calculation result, the angle needs to be converted, and the slope angle of the straight line is set
Considering the periodicity of the slope function of the straight line, setting the direction finding degree of the mobile monitoring station as azimuth, and converting into: bearing = azimuth-90 °; and calculating by taking bearing as the final angle.
Since the line of the direction line of the monitoring station passes through a fixed point, i.e. the coordinates of the monitoring station, which is known, the equation of the line is determined, assuming the coordinates of the monitoring station 1 as (X1, Y1), the direction finding angle
The coordinates of the monitoring station 2 are: (X2, Y2), direction finding Angle
The coordinates of the intersection point are: (X, Y);
the following algorithm is adopted to carry out circular traversal on the three direction indicating lines, so that the intersection points of all two direction indicating lines can be obtained and stored in an intersection point set, and the algorithm is as follows:
in this embodiment, the coordinates of three vertices of the triangle are set as a (Xa, Ya), B (Xb, Yb), and C (Xc, Yc), i.e. the three-side lengths a, B, and C of the triangle are obtained;
setting p = (a + b + c)/2, then the triangle area can be obtained:
the coordinates of the center of the inscribed circle are set as (centerX, centerY), and the radius of the inscribed circle is set as:
then, there are:
namely, the center and radius of the inscribed circle are obtained.
In this embodiment, the K-Means clustering algorithm specifically includes:
s301, specifying the number K value of clusters needing to be divided;
s302, randomly selecting K initial data object points as initial clustering centers;
s303, calculating the distance from each of the rest data objects to the K initial clustering centers, classifying the data objects into the cluster class of the center closest to the data objects, and recording the serial numbers of the data objects;
s304, calculating the center of each cluster, updating, judging whether the center of the new class and the center of the original class meet the specified precision convergence function, if so, finishing the adjustment of the data object, otherwise, returning to the step S303 to readjust.
The K-Means clustering algorithm is a common classification-based clustering analysis method, and the final objective of the clustering algorithm is to divide data objects into K clusters according to an input parameter K. The K-Means clustering algorithm belongs to a dynamic clustering algorithm, also called a gradual clustering method, and has the obvious characteristic of an iterative process, wherein whether the classification of each sample data is correct or not is examined every time, and if the classification is incorrect, readjustment is carried out. And after all the data objects are adjusted, modifying the center and entering the next iteration. If all data objects are correctly classified in an iteration, no new adjustment is made, the clustering center is not changed, the clustering criterion function also indicates convergence, and the algorithm is successfully ended. In actual calculation, the number of designed iterations is large, the iteration times can be limited under the condition of ensuring certain precision, and excessive memory consumption is avoided.
The specific process of the K-Means clustering algorithm is as follows:
in clustering, a given sample set is { x (1), … x (m) }, each
；
The K-Means algorithm is used for clustering sample data into K clusters, and the specific algorithm is described as follows:
a. randomly selecting K clustering centroid points as
；
b. The following process is repeated until convergence:
for each sample i, calculate the class to which it should belong
；
c. For each class j, the centroid of the class is recalculated:
the first problem faced by K-Means is how to guarantee convergence, and the previous algorithm emphasizes that the termination condition is convergence, which proves that K-Means can guarantee convergence completely. We describe qualitatively below the convergence, we define the distortion function as follows:
the J function represents the sum of the squares of the distances of each sample point to its centroid. K-Means is to minimize J. Assuming that the current J does not reach a minimum, then the centroid of each class may be fixed first
Adjusting the category to which each sample belongs
To let the J function decrease, and likewise, fix
Adjusting the center of mass of each class
J may also be reduced. These two processes are the processes of monotonically decreasing J in the inner loop. When the J is decreased to the minimum value,
and
but also converges at the same time.
Since the distortion function J is a non-convex function, we cannot guarantee that the minimum value obtained is a global minimum value, that is, K-Means is sensitive to the selection of the centroid initial position, but generally, the local optimization achieved by K-Means already meets the requirement. If the result is worried to be converged to the local optimum, different initial values can be selected to operate the K-Means clustering algorithm for multiple times, and then the value corresponding to the minimum J value is selected
And
and (6) outputting. Meanwhile, the operation efficiency is reduced, the operation time is increased, and the practical compromise is neededAnd (4) filtering.
In this K-Means algorithm:
inputting: the initial data set shows the number K of line intersection sets and clusters;
and (3) outputting: k clusters, satisfying the root mean square error criterion function convergence.
Because the inscribed circle algorithm is used later, the subsequent inscribed circle algorithm can be performed only by clustering 3 data points, or clustering data into n (n is more than 3) clusters, then clustering n into K (K is more than 3 and less than n) clusters, and recursively clustering into 3 clusters.
In radio monitoring direction finding positioning, the clustering analysis algorithm can avoid small probability errors, namely occasional abnormal direction line data can not cause serious influence on the global clustering result, so that the stability and the robustness of the inscribed circle algorithm are further ensured. Meanwhile, with the continuous updating of direction-finding data, the clustering center fluctuates, and the position of the center of the inscribed circle changes correspondingly, so that after an inscribed circle algorithm is discussed, a superimposed thermodynamic diagram algorithm of the inscribed circle is added.
In the thermodynamic diagram algorithm of the present embodiment, the superimposed inscribed circle set obtained in step S3 is transmitted to a thermodynamic diagram layer drawing method to obtain a final thermodynamic diagram effect, please refer to fig. 4, and finally, the final localization result and the display effect can be obtained by superimposing the superimposed inscribed circle set on a map.
In this embodiment, for processing a large amount of direction finding data, please refer to fig. 5, in order to remove inaccurate data, high-quality direction finding data is selected, so that a large amount of direction finding data is more concentrated and accurate, and divergent direction finding data is removed, a screening method of direction finding quality and a level threshold is adopted, and an improved K-Means clustering method is used to perform cluster analysis on intersection points of direction finding lines obtained by direction finding to obtain thermodynamic diagram basic data. On the basis of the previous clustering algorithm, a brand-new thermodynamic diagram buffering algorithm is adopted to show a thermodynamic diagram with a better effect, and the actual measurement effect is shown in figure 6. The buffer algorithm is a kind of neighbor analysis, and combines with a part of methods of nuclear density analysis, in order to identify the influence degree of a certain geographic entity or spatial object on its surrounding ground objects, a region with a certain radius range is established around it, the value in the region has core representativeness, and the value of the region has gradient.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A single-station mobile automatic positioning method is characterized by comprising the following steps:
s1, acquiring a set of azimuth angles of the emission source through the direction-finding machine, and storing the set of azimuth angles into a mobile monitoring station set;
s2, filtering the data which are separated from the core value by adopting a threshold quality screening method, and then storing all the intersection points of the indicator line which is subjected to sequencing dynamic insertion screening into an intersection point set;
s3, analyzing the data obtained in the step S2 by adopting an improved K-Means clustering algorithm, selecting three points as a clustering basis, calculating the circle center and the radius of an inscribed circle of a triangle with the three points as vertexes, and storing the circle center and the radius of the inscribed circle into an inscribed circle set in an object mode;
s4, calculating the stacking times of the inscribed circles, performing statistical operation, taking the stacking times as the color assignment weight of the inscribed circles, and taking the weight as the scale of the color depth during rendering;
and S5, adding the inscribed circle set data overlapped in the step S4 into a thermodynamic diagram generation method, generating a thermodynamic diagram layer, overlapping the thermodynamic diagram layer on a map for displaying, wherein the area with deep colors according to the thermodynamic diagram is the position of the interference source.
2. The single-station mobile automatic positioning method according to claim 1, wherein in step S1, a set of azimuth angles at which the transmission sources are located is obtained by the direction-finding machine and stored in the mobile monitoring station set, which specifically comprises:
the direction finder obtains the incoming wave direction of the emission source in the travelling process of a single mobile monitoring station, obtains the true north direction degree after the compass marks on the map, and then obtains the included angle with the true north direction and stores the included angle in the mobile monitoring station set.
3. The single-station mobile automatic positioning method according to claim 1 or 2, wherein in step S2, storing all the intersections of the filtered direction-indicating lines by using sorted dynamic insertion into an intersection set, specifically comprises:
under a plane rectangular coordinate system, setting a straight line inclination angle as theta, setting the direction-finding degree of the mobile monitoring station as azimuth, and converting the direction-finding degree of the mobile monitoring station into that under the rectangular coordinate system: azimuth +90 °, then θ +90 °;
setting the coordinates of the mobile monitoring station at the position 1 as: (X1, Y1), direction finding Angle θ1And the coordinates of the mobile monitoring station at the position 2 are as follows: (X2, Y2), direction finding Angle θ2The coordinates of the intersection point are: (X, Y);
the following algorithm is adopted to carry out circular traversal on the three direction indicating lines, so that the intersection points of all two direction indicating lines can be obtained and stored in an intersection point set, and the algorithm is as follows:
4. the single-station mobile automatic positioning method according to claim 1, wherein in step S3, the K-Means clustering algorithm specifically includes:
s301, specifying the number K value of clusters needing to be divided;
s302, randomly selecting K initial data object points as initial clustering centers;
s303, calculating the distance from each of the rest data objects to the K initial clustering centers, classifying the data objects into the cluster class of the center closest to the data objects, and recording the serial numbers of the data objects;
s304, calculating the center of each cluster, updating, and judging whether the center of the new class and the center of the original class meet the specified precision convergence function, if so, finishing the adjustment of the data object, otherwise, returning to the step S303 to readjust.
5. The single-station mobile automatic positioning method according to claim 1 or 4, characterized in that in the K-Means clustering algorithm, the data are clustered into n clusters, then the n clusters are clustered into K clusters, and are gradually classified into 3 clusters, wherein n is greater than 3, and 3< K < n.
6. The single-station mobile automatic positioning method of claim 1, wherein in step S3, the circle center and radius of the inscribed circle of the triangle with the three points as the vertices are calculated, and the circle center and radius of the inscribed circle are stored in the inscribed circle set in an object-wise manner, specifically:
setting the coordinates of three vertexes of the triangle as A (Xa, Ya), B (Xb, Yb) and C (Xc, Yc), and obtaining the three-side lengths a, B and C of the triangle;
setting p to (a + b + c)/2, the triangle area can be obtained:
the coordinates of the center of the inscribed circle are set as (centerX, centerY), and the radius of the inscribed circle is set as: r is S/p, then:
namely, the center and radius of the inscribed circle are obtained.
7. The single-station mobile automatic positioning method according to claim 1 or 6,
when the intersection point of the direction indicating lines exceeds the transmission range of the radio wave of the emission source, drawing of an inscribed circle is not performed;
and setting intersection points on a reverse extension line of a direction indicating line of the mobile monitoring station, namely setting a vector included angle between a unit vector of a direction-finding angle of the mobile monitoring station and a connecting line of the mobile monitoring station and a certain intersection point to be 180 degrees, and eliminating the direction indicating line in the system without participating in calculation, wherein the direction indicating line is considered as a false direction.
8. The single-station mobile automatic positioning method according to claim 1, wherein in step S4, the method further includes calculating the number of occurrences of the directivity according to the position relationship and the radius of all the obtained inscribed circles, and finally normalizing the number of occurrences of the directivity to obtain the final color assignment weight data, wherein the number of occurrences of the directivity is calculated according to the position relationship and the radius, and the position relationship includes only three cases of intersection, inclusion and inscribed.
9. The single-station mobile automatic positioning method according to claim 1, further comprising obtaining the position of the interference source by a confidence ellipse algorithm, specifically:
and (4) storing all the inscribed circles obtained in the step (S3) in a confidence ellipse calculation set without counting the radius, calculating the confidence range of the overall data, setting the confidence probability, and drawing an ellipse according to the confidence range of the overall data, namely obtaining the position of the interference source.
10. The method as claimed in claim 1, wherein steps S1 to S5 are executed in a loop when new data is added to the mobile monitoring station set.
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| CN111669698B (en) * | 2019-03-07 | 2022-06-21 | 广州慧睿思通科技股份有限公司 | Positioning method, device, system and storage medium |
| CN110349261B (en) * | 2019-07-15 | 2023-05-09 | 泰华智慧产业集团股份有限公司 | Method for generating three-dimensional thermodynamic diagram based on GIS |
| CN110677865B (en) * | 2019-09-25 | 2020-08-04 | 北京邮电大学 | Method for positioning external interference source of mobile communication network |
| CN110988787B (en) * | 2019-12-20 | 2023-11-17 | 上海创远仪器技术股份有限公司 | Method for realizing optimal direction finding angle measurement based on cluster analysis in wireless signal direction finding monitoring |
| CN113009414B (en) * | 2019-12-20 | 2024-03-19 | 中移(成都)信息通信科技有限公司 | Signal source position determining method and device, electronic equipment and computer storage medium |
| CN112415471B (en) * | 2020-12-11 | 2023-10-31 | 成都大公博创信息技术有限公司 | Single-station mobile direction-finding positioning method |
| CN115598670B (en) * | 2022-09-29 | 2024-03-05 | 西安邮电大学 | Method for monitoring and positioning interference source by combining triangular network and ant colony algorithm |
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| 基于测向的短波组网定位技术研究;赵二超;《中国优秀硕士学位论文全文数据库 信息科技辑》;20160315;I136-1982 * |
| 测向交叉定位***中的K_means聚类融合算法;孙鹏 等;《电光与控制》;20161015;36-40 * |
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