CN108845302B - K-nearest neighbor transformation true and false target feature extraction method - Google Patents

K-nearest neighbor transformation true and false target feature extraction method Download PDF

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CN108845302B
CN108845302B CN201810964240.1A CN201810964240A CN108845302B CN 108845302 B CN108845302 B CN 108845302B CN 201810964240 A CN201810964240 A CN 201810964240A CN 108845302 B CN108845302 B CN 108845302B
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周代英
沈晓峰
冯健
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University of Electronic Science and Technology of China
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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 discloses a K neighbor transformation true and false target feature extraction method, which belongs to the technical neighborhood of radar target identification.

Description

K-nearest neighbor transformation true and false target feature extraction method
Technical Field
The invention belongs to the field of radar target identification technology, and particularly relates to a K nearest neighbor transformation true and false target feature extraction method.
Background
In radar target identification, the discrimination vector transformation method can increase the difference between different target features and reduce the difference between the same target features, so that the features with obvious difference are extracted, and therefore, the discrimination vector transformation method obtains good classification performance.
However, the discriminant vector transform method is only suitable for the case where the sample data is gaussian distributed, but the distribution of the sample data may be non-gaussian distributed in practice, and the recognition performance of the discriminant vector transform method is significantly degraded in the case of the non-gaussian distribution. There is room for further improvement in the recognition performance of the conventional discrimination vector conversion method.
Disclosure of Invention
The invention aims to: aiming at the existing problems, a K neighbor transformation feature extraction method is provided to overcome the defects of a conventional discrimination vector transformation method and effectively improve the classification performance of radar true and false targets.
The technical scheme of the K nearest neighbor transformation true and false target feature extraction method specifically comprises the following steps:
step 1: inputting a training sample set of one-dimensional range profile of radar target by xijRepresenting training samples, wherein subscript i is a class identifier, subscript j is a training sample identifier, i is greater than or equal to 1 and less than or equal to g, j is greater than or equal to 1 and less than or equal to NiG denotes the number of categories, NiA number of samples representing a corresponding category;
step 2: calculating an estimated value of a K nearest neighbor transformation matrix A
Figure BDA0001774542280000011
Figure BDA0001774542280000012
Wherein the sample matrix
Figure BDA0001774542280000013
Constraint coefficient matrix of similar K nearest neighbor rule
Figure BDA0001774542280000014
Matrix array
Figure BDA0001774542280000015
Constraint coefficient of similar K nearest neighbor rule
Figure BDA0001774542280000021
Is set as follows: if it is
Figure BDA0001774542280000022
Or
Figure BDA0001774542280000023
Then
Figure BDA0001774542280000024
Otherwise
Figure BDA0001774542280000025
Where the subscript k is a training sample discriminator of some kind, σ2The value of the gaussian parameter is represented,
Figure BDA0001774542280000026
k representing a vector of the same kind1Set of neighbor vectors, k1Is a preset neighbor number;
constraint coefficient matrix of heterogeneous K neighbor rule
Figure BDA0001774542280000027
Matrix array
Figure BDA0001774542280000028
Constraint coefficient of heterogeneous K neighbor rule
Figure BDA0001774542280000029
Is set as follows: if it is
Figure BDA00017745422800000210
Or alternatively
Figure BDA00017745422800000211
Then
Figure BDA00017745422800000212
Otherwise
Figure BDA00017745422800000213
Wherein the subscript l is a category specifier,
Figure BDA00017745422800000214
k representing a vector of a different class2Set of neighbor vectors, k2Is a preset neighbor number;
step (ii) of3: inputting radar true and false target one-dimensional range profile x of sub-image features to be extractedtAccording to
Figure BDA00017745422800000215
Obtaining a one-dimensional range profile xtCharacteristic vector y oft。
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
according to the method, the difference between the same-class sample characteristics is reduced based on the K neighbor constraint rule, the difference between different-class sample characteristics is increased for weighting, and the influence of other samples on the construction of the transformation matrix is reduced, so that the characteristics of non-Gaussian distribution sample data can be extracted, the defects of a conventional discrimination vector transformation method are overcome, and the classification performance of radar true and false targets is effectively improved.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments.
The K nearest neighbor transformation true and false target feature extraction method reduces the difference between the same type sample features based on the K nearest neighbor constraint rule, increases the weighting of the difference between the different type sample features, and reduces the influence of other samples on the construction of a transformation matrix, so that the features of non-Gaussian distribution sample data can be extracted, and the specific implementation process is as follows:
by xij(n-dimensional column vector) represents the iththJ-th of class true and false targetthI is more than or equal to 1 and less than or equal to g, j is more than or equal to 1 and less than or equal to Ni，
Figure BDA0001774542280000031
Wherein N isiIs the iththThe number of training one-dimensional distance image samples of the true and false-like targets is N, and N is the total number of the training one-dimensional distance image samples. Will train a one-dimensional range profile xijThe following transformations are performed:
yij＝ATxij (1)
where A is the transformation matrix, yijIs xijThe corresponding feature vector, T, represents the matrix transpose.
Calculating the sum of squares of differences between any two sample feature vectors of the same type in a feature spaceC：
Figure BDA0001774542280000032
Wherein
Figure BDA0001774542280000033
Constraint coefficients for similar K nearest neighbor rules:
Figure BDA0001774542280000034
wherein the Gaussian parameter σ2Is an empirical value, and is determined through experiments under the condition of meeting the requirement of processing precision,
Figure BDA0001774542280000035
k representing a vector of the same kind1A set of neighbor vectors. Equation (3) shows that when two samples belonging to the same target class are k each other1When the samples are adjacent to each other, the constraint coefficient of the difference between the similar samples is not equal to zero, and the constraint coefficient of the difference between the other similar samples is zero.
Using the equation of the matrix trace, equation (2) can be converted into:
Figure BDA0001774542280000036
substitution of formula (1) for formula (4) gives:
Figure BDA0001774542280000037
by simplifying the formula (5), the following can be obtained:
SC＝ATX(DC-WC)XTA (6)
wherein
Figure BDA0001774542280000041
Figure BDA0001774542280000042
Figure BDA0001774542280000043
Similarly, calculating the weighted distance square sum S between the heterogeneous target sample characteristics in the characteristic spaceB：
Figure BDA0001774542280000044
Wherein wij,lkConstraint coefficients based on heterogeneous K nearest neighbor rules:
Figure BDA0001774542280000045
wherein
Figure BDA0001774542280000046
K representing a vector of a different class2A set of neighbor vectors.
Using the formula of the matrix trace, equation (10) can be converted into
Figure BDA0001774542280000047
Substituting formula (1) into formula (12)
Figure BDA0001774542280000048
The formula (13) is simplified to obtain
SB＝ATX(DB-WB)XTA (14)
Wherein
Figure BDA0001774542280000051
Figure BDA0001774542280000052
The estimation value of the K nearest neighbor transformation matrix can be obtained by solving the following minimization problem
Figure BDA0001774542280000053
Figure BDA0001774542280000054
The right side of the formula (17) calculates the partial derivative of A and makes it equal to zero, and the estimated value of the K neighbor transformation matrix can be obtained
Figure BDA0001774542280000055
Is composed of a matrix (X (D)B-WB)XT)-1(X(DC-WC)XT) Is used for determining the characteristic vector corresponding to the M (n) maximum characteristic values.
Obtaining the estimated value of K adjacent transformation matrix
Figure BDA0001774542280000056
Then, an arbitrary sample x can be obtained by using the formula (1)tCharacteristic vector y oftI.e. by
Figure BDA0001774542280000057
And then radar true and false target identification processing is carried out based on the extracted feature vector, so that the classification performance of the radar true and false targets is effectively improved.
To verify the effectiveness of the proposed method, the following simulation experiments were performed.
Four point targets are set: true objects, debris, light baits, and heavy baits. The bandwidth of radar emission pulse is 1000MHZ (the range resolution is 0.15m, the radar radial sampling interval is 0.075m), the target is set as a uniform scattering point target, the scattering point of a true target is 7, and the number of the scattering points of the other three targets is 11. In the one-dimensional distance images of every 1 degree within the range of the target attitude angle of 0-80 degrees, the one-dimensional distance images of the target attitude angle of 0 degree, 2 degrees, 4 degrees, 6 degrees, and 90 degrees are taken for training, and the one-dimensional distance images of the rest attitude angles are taken as test data, so that each category of targets has 45 test samples.
For four targets (true target, fragment, light bait and heavy bait), in the range of 0-90 degrees of attitude angle, the K nearest neighbor transformation feature extraction method and the discrimination vector transformation-based feature extraction method are utilized to carry out recognition experiment, and the result is shown in the table I. Experimental nearest neighbor parameter k1＝20，k210, gaussian parameter σ2＝6.25。
From the results in table one, it can be seen that for the true target, the recognition rate of the discriminant vector transform feature extraction method is 83%, while the recognition rate of the K neighbor transform feature extraction method of the present invention is 90%; for the fragments, the recognition rate of the vector transformation feature extraction method is judged to be 78%, and the recognition rate of the K neighbor transformation feature extraction method is 85%; for light baits, the recognition rate of the discrimination vector transformation feature extraction method is 80 percent, while the recognition rate of the K neighbor transformation feature extraction method is 86 percent; the discrimination vector transform feature extraction method has a recognition rate of 82% for heavy baits, and the K-nearest neighbor transform feature extraction method of the present invention has a recognition rate of 83%. On average, for four types of targets, the correct recognition rate of the K neighbor transformation feature extraction method is higher than that of a discrimination vector transformation feature extraction method, and the K neighbor transformation feature extraction method provided by the invention is proved to improve the recognition performance of multiple types of targets.
Table one two methods of identification results
Figure BDA0001774542280000061
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.

Claims (4)

1. A K nearest neighbor transformation true and false target feature extraction method is characterized by comprising the following steps:
step 1: inputting a training sample set of one-dimensional range profile of radar target by xijRepresenting training samples, wherein subscript i is a class identifier, subscript j is a training sample identifier, i is more than or equal to 1 and less than or equal to g, j is more than or equal to 1 and less than or equal to NiG denotes the number of categories, NiA number of samples representing a corresponding category;
step 2: calculating the estimated value of K nearest neighbor transformation matrix A
Figure FDA0001774542270000011
Figure FDA0001774542270000012
Wherein the sample matrix
Figure FDA0001774542270000013
Constraint coefficient matrix of similar K nearest neighbor rule
Figure FDA0001774542270000014
Matrix array
Figure FDA0001774542270000015
Constraint coefficient of similar K nearest neighbor rule
Figure FDA0001774542270000016
Is set as follows: if it is
Figure FDA0001774542270000017
Or
Figure FDA0001774542270000018
Then
Figure FDA0001774542270000019
Otherwise
Figure FDA00017745422700000110
Where the subscript k is the training sample specifier, σ2The value of the gaussian parameter is represented,
Figure FDA00017745422700000111
k representing a vector of the same kind1Set of neighbor vectors, k1Is a preset neighbor number;
constraint coefficient matrix of heterogeneous K neighbor rule
Figure FDA00017745422700000112
Matrix array
Figure FDA00017745422700000113
Constraint coefficient of heterogeneous K neighbor rule
Figure FDA0001774542270000021
Is set as follows: if it is
Figure FDA0001774542270000022
Or
Figure FDA0001774542270000023
l is not equal to i, then
Figure FDA0001774542270000024
Otherwise
Figure FDA0001774542270000025
Wherein the subscript l is a category specifier,
Figure FDA0001774542270000026
k representing a vector of a different class2Set of neighbor vectors, k2Is a preset neighbor number;
and step 3: inputting radar true and false target one-dimensional range profile x of sub-image features to be extractedtAccording to
Figure FDA0001774542270000027
Obtaining a one-dimensional range profile xtCharacteristic vector y oft。
2. The method of claim 1, wherein, in step 2,
estimated value
Figure FDA0001774542270000028
Is a matrix (X (D)B-WB)XT)-1(X(DC-WC)XT) M, where M < n, n denotes the training sample xijThe number of dimensions of (a).
3. Method according to claim 1 or 2, characterized in that the number of neighbors k1、k2Is preferably taken to be k1＝20，k2＝10。
4. Method according to claim 1 or 2, characterized in that the preferred value of the gaussian parameter is σ2＝6.25。
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