CN112149061A - Multi-class average maximization true and false target feature extraction method - Google Patents

Multi-class average maximization true and false target feature extraction method Download PDF

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CN112149061A
CN112149061A CN202011026888.8A CN202011026888A CN112149061A CN 112149061 A CN112149061 A CN 112149061A CN 202011026888 A CN202011026888 A CN 202011026888A CN 112149061 A CN112149061 A CN 112149061A
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周代英
张瑛
沈晓峰
梁菁
冯健
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Abstract

The invention belongs to the technical field of radar target identification, and particularly relates to a method for extracting characteristics of multiple types of average maximized true and false targets. The method adopts the multi-component Gaussian distribution to represent the likelihood function of the target data, can still accurately describe the distribution condition of the target data under the condition that target sample data is non-Gaussian distribution, and screens out the most effective classification and identification features from the characteristic elements of the one-dimensional distance image of the target. The defect that the conventional method is only suitable for Gaussian distribution of sample data is overcome, so that the target identification performance is improved, simulation experiments are carried out on the one-dimensional range profile data of four types of simulation targets, and the experimental result shows that the method is effective.

Description

Multi-class average maximization true and false target feature extraction method
Technical Field
The invention belongs to the technical field of radar target identification, and particularly relates to a method for extracting characteristics of multiple types of average maximized true and false targets.
Background
In radar target identification, feature extraction is a very critical step, so that on one hand, the dimension of an input vector can be reduced, the calculated amount can be reduced, and original classification information can be well maintained. The subspace is a commonly used feature extraction method, that is, a corresponding subspace is obtained under the condition that a certain criterion is satisfied. For example, the decision vector subspace method is a method in which a decision vector subspace is obtained using the fisher criterion, whereas the feature subspace method is a method in which a feature subspace is obtained under a condition in which a reconstruction error is minimum, and a good recognition effect can be obtained under a condition in which sample data is gaussian distributed.
However, the feature extraction method such as subspace is only suitable for the case where the sample data is gaussian distributed, and the distribution of the sample data may be non-gaussian in practice, and the recognition performance of the conventional feature extraction method is significantly reduced for the non-gaussian distribution case. There is room for further improvement in the recognition performance of the conventional feature extraction method.
Disclosure of Invention
Aiming at the problems, the invention provides a multi-class average maximization feature extraction method, which uses multi-component Gaussian distribution to represent likelihood functions of various target data, and can still accurately describe the distribution condition of the target data under the condition of non-Gaussian distribution, thereby screening out the most effective features for classification identification, overcoming the defects of the conventional feature extraction method, and effectively improving the classification performance of radar true and false targets.
The technical scheme of the invention is as follows:
a method for extracting features of multiple classes of average maximized true and false targets is characterized by comprising the following steps:
s1, setting n-dimension column vector xcjC 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 N for the jth training one-dimensional range profile of the c-th true and false targetc，
Figure BDA0002702382840000011
Wherein g is the number of classes, NcThe number of training one-dimensional range profile samples of a class c true and false target is shown, N is the total number of the training one-dimensional range profile samples, and a likelihood function of the class c is represented by a plurality of Gaussian components:
Figure BDA0002702382840000021
wherein
Figure BDA0002702382840000022
Is the weight coefficient of each gaussian component,
Figure BDA0002702382840000023
and is
Figure BDA0002702382840000024
McThe number of gaussian components for class c,
Figure BDA0002702382840000025
a parameter representing a gaussian component term, p (·) being a probability density function;
s2, assuming that each characteristic element in the one-dimensional image data is independent of each other, if the element is independent of the category, the element is not a valid characteristic, and the element obeys the distribution q (x)cjk/γck)，xcjkDenotes xcjThe k element of (a), γckDefining a two-valued variable psi for the distribution coefficients of the kth characteristic element of the class cckIf the feature element is valid, thenck1, otherwise psick0; make the probability of the feature element being valid as betak＝P(ψck1), modeling according to a likelihood function:
Figure BDA0002702382840000026
wherein r isckDistribution coefficients of kth characteristic elements of a type c;
s3, estimating the parameters of the model established in the step S2 by a mean value maximization method:
s31, initializing a mean vector, a covariance matrix and a weight of the Gaussian component;
s32, calculating the updated value of the model parameter:
Figure BDA0002702382840000027
Figure BDA0002702382840000028
Figure BDA0002702382840000029
Figure BDA0002702382840000031
Figure BDA0002702382840000032
Figure BDA0002702382840000033
Figure BDA0002702382840000034
Figure BDA0002702382840000035
Figure BDA0002702382840000036
wherein n isθAnd nγAre respectively a parameter set
Figure BDA0002702382840000037
And
Figure BDA0002702382840000038
the number of parameters in (1), E (-) and var (-) respectively represent a mean parameter and a variance parameter in a parameter set;
s33, calculating the error before and after each parameter updating, if the error is less than the specified threshold value or exceeds the specified iteration times, the updating process is terminated and the step S4 is carried out, otherwise, the step S32 is returned to continue the iterative updating;
s4, if
Figure BDA0002702382840000039
If the value is larger than the given threshold value, reserving the kth characteristic element, and forming a new characteristic vector by the reserved characteristic elements to be used as a characteristic extraction result.
After the feature vectors are obtained, the classification and identification of the targets can be finished by utilizing a classifier
The invention has the beneficial effects that: under the condition of non-Gaussian distribution, the distribution condition of target data can still be accurately described, so that the most effective characteristics for classification and identification are screened, the defects of the conventional characteristic extraction method are overcome, and the classification performance of radar true and false targets is effectively improved.
Detailed Description
The technical scheme of the invention is described in detail by combining simulation experiments as follows:
the method of the invention mainly comprises the following steps:
let xcjThe (N-dimensional column vector) is the jth training one-dimensional distance image of the c-th true and false target, c 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 Nc，
Figure BDA0002702382840000041
Wherein N iscThe number of training one-dimensional range image samples of class c true and false targets, and N is the number of training one-dimensional range image samplesAnd (4) total number. Class c likelihood function representation using polynomial gaussian components
Figure BDA0002702382840000042
Wherein
Figure BDA0002702382840000043
Is the weight coefficient of each gaussian component,
Figure BDA0002702382840000044
and is
Figure BDA0002702382840000045
McThe number of gaussian components for class c,
Figure BDA0002702382840000046
the parameter representing the gaussian component term, p (·) is a probability density function. Assuming that the feature elements in the one-dimensional image data are independent of each other, equation (1) may be changed to
Figure BDA0002702382840000047
If an element is independent of a class, then the element is not a valid feature, let it obey the distribution q (x)cjk/γck)，γckFor the distribution coefficient of the kth characteristic element of class c, equation (2) is transformed into
Figure BDA0002702382840000048
Wherein psickIs a two-valued variable, if the feature element is valid, phick1, otherwise psick0. Make the probability of the feature element being valid as
βk＝P(ψck＝1) (4)
The formula (3) can be represented as
Figure BDA0002702382840000051
The parameters in equation (5) are estimated by a mean maximization method, which comprises the following steps:
step 1: initializing a mean vector, a covariance matrix and a weight of the Gaussian component;
step 2: calculating updated values of model parameters
Figure BDA0002702382840000052
Figure BDA0002702382840000053
Figure BDA0002702382840000054
Figure BDA0002702382840000055
Figure BDA0002702382840000056
Figure BDA0002702382840000057
Figure BDA0002702382840000061
Figure BDA0002702382840000062
Figure BDA0002702382840000063
Wherein n isθAnd nγAre respectively a parameter set
Figure BDA0002702382840000064
And
Figure BDA0002702382840000065
e (-) and var (-) represent the mean parameter and variance parameter, respectively, in the parameter set. Since each mixture component is Gaussian distributed, nθ＝2，nγ＝2。
And step 3: and (3) calculating errors before and after the updating of each parameter, if the errors are smaller than a specified threshold value or exceed a specified iteration number, terminating the updating process, and otherwise, turning to the step 2 to continue the iterative updating.
If it is
Figure BDA0002702382840000066
And if the value is larger than the given threshold, reserving the kth characteristic element, grouping the reserved characteristic elements into a new characteristic vector, and then finishing the classification and identification of the target by utilizing a classifier.
To verify the effectiveness of the proposed method, the following simulation experiments were performed.
Four point targets were designed: 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 multi-class average maximization feature extraction method and the method based on judgment are utilizedThe identification experiment is carried out by the method for extracting the other vector subspace characteristics, and the result is shown in the table I. The experimental parameters were: mcThe iteration stop threshold value is taken to be 0.0001, and the threshold for selecting the characteristic element is taken to be 0.001. In addition, the mean and covariance matrices of the various types of samples are used as initial values of the mean and variance matrices of the gaussian components.
As can be seen from the results in table 1, for the true target, the recognition rate of the discriminant vector subspace feature extraction method is 83%, while the recognition rate of the multi-class average maximization feature extraction method of the present invention is 92%; for the fragments, the recognition rate of the discrimination vector subspace feature extraction method is 78%, while the recognition rate of the multi-class average maximization feature extraction method is 86%; for light baits, the recognition rate of the discrimination vector subspace feature extraction method is 80%, and the recognition rate of the multi-class average maximization feature extraction method is 85%; for heavy bait, the recognition rate of the discriminant vector subspace feature extraction method is 82%, while the recognition rate of the multi-class average maximization feature extraction method of the invention is 84%. On average, for four types of targets, the correct recognition rate of the multi-type average maximization feature extraction method is higher than that of a discrimination vector subspace feature extraction method, and the fact that the multi-type average maximization feature extraction method provided by the invention really improves the recognition performance of the multi-type targets is shown.
TABLE 1 identification results of the two methods
Figure BDA0002702382840000071

Claims (1)

1. A method for extracting features of multiple classes of average maximized true and false targets is characterized by comprising the following steps:
s1, setting n-dimension column vector xcjC 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 N for the jth training one-dimensional range profile of the c-th true and false targetc，
Figure FDA0002702382830000011
Wherein g is the number of classes, NcTraining for class c true and false targetsThe number of the one-dimensional range profile samples is N, the total number of the training one-dimensional range profile samples is N, and a likelihood function of a class c is represented by a plurality of Gaussian components:
Figure FDA0002702382830000012
wherein
Figure FDA0002702382830000013
Is the weight coefficient of each gaussian component,
Figure FDA0002702382830000014
and is
Figure FDA0002702382830000018
McThe number of gaussian components for class c,
Figure FDA0002702382830000015
a parameter representing a gaussian component term, p (·) being a probability density function;
s2, assuming that each characteristic element in the one-dimensional image data is independent of each other, if the element is independent of the category, the element is not a valid characteristic, and the element obeys the distribution q (x)cjk/γck)，xcjkDenotes xcjThe k element of (a), γckDefining a two-valued variable psi for the distribution coefficient of the kth characteristic element of the class c, determined by the distribution typeckIf the feature element is valid, thenck1, otherwise psick0; make the probability of the feature element being valid as betak＝P(ψck1), modeling according to a likelihood function:
Figure FDA0002702382830000016
wherein gamma isckDistribution coefficients of kth characteristic elements of a type c;
s3, estimating the parameters of the model established in the step S2 by a mean value maximization method:
s31, initializing a mean vector, a covariance matrix and a weight of the Gaussian component;
s32, calculating the updated value of the model parameter:
Figure FDA0002702382830000017
Figure FDA0002702382830000021
Figure FDA0002702382830000022
Figure FDA0002702382830000023
Figure FDA0002702382830000024
Figure FDA0002702382830000025
Figure FDA0002702382830000026
Figure FDA0002702382830000027
Figure FDA0002702382830000028
wherein n isθAnd nγAre respectively a parameter set
Figure FDA0002702382830000029
And
Figure FDA00027023828300000210
the number of parameters in (1), E (-) and var (-) respectively represent a mean parameter and a variance parameter in a parameter set;
s33, calculating the error before and after each parameter updating, if the error is less than the specified threshold value or exceeds the specified iteration times, the updating process is terminated and the step S4 is carried out, otherwise, the step S32 is returned to continue the iterative updating;
s4, if
Figure FDA0002702382830000031
If the value is larger than the given threshold value, reserving the kth characteristic element, and forming a new characteristic vector by the reserved characteristic elements to be used as a characteristic extraction result.
CN202011026888.8A 2020-09-25 2020-09-25 Multi-class average maximization true and false target feature extraction method Pending CN112149061A (en)

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CN113960553A (en) * 2021-10-14 2022-01-21 电子科技大学 Gaussian weight distribution feature extraction method in one-dimensional image recognition
CN115712867A (en) * 2022-11-03 2023-02-24 哈尔滨工程大学 Multi-component radar signal modulation identification method

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CN110007286A (en) * 2019-04-22 2019-07-12 电子科技大学 A kind of true and false target one-dimensional range profile feature extracting method of linear discriminant study
CN110658507A (en) * 2019-10-12 2020-01-07 电子科技大学 Multi-class average maximization true and false target feature extraction method for radar target identification
CN110687514A (en) * 2019-10-16 2020-01-14 电子科技大学 Nonlinear discrimination learning true and false target one-dimensional range profile feature extraction method

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CN108845302A (en) * 2018-08-23 2018-11-20 电子科技大学 A kind of true and false target's feature-extraction method of k nearest neighbor transformation
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