CN108490414A - A kind of radar target identification method based on time-frequency distributions instantaneous frequency edge feature - Google Patents

A kind of radar target identification method based on time-frequency distributions instantaneous frequency edge feature Download PDF

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CN108490414A
CN108490414A CN201810429367.3A CN201810429367A CN108490414A CN 108490414 A CN108490414 A CN 108490414A CN 201810429367 A CN201810429367 A CN 201810429367A CN 108490414 A CN108490414 A CN 108490414A
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frequency
time
distributions
matrix
feature
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CN108490414B (en
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于雪莲
顾成玲
赵林森
唐永昊
曲学超
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University of Electronic Science and Technology of China
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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Abstract

The invention discloses a kind of radar target identification methods based on time-frequency distributions instantaneous frequency edge feature, belong to Technology of Radar Target Identification field, are related to Radar High Range Resolution time-frequency distributions characteristic, the technology of instantaneous frequency local edge.This method first, pre-processes radar range profile, eliminates it and translates sensibility, amplitude sensitive；Then, the Wigner Ville distributions of Range Profile are found out, then the cross-interference terms in Wigner Ville distributions are inhibited by time-frequency reordering technique；Next, seeking the instantaneous frequency edge feature of the time-frequency distributions after time-frequency is reset, it is used in combination and Principal Component Analysis Algorithm obtains the eigenmatrix of instantaneous frequency edge feature；Finally, classified to test sample using k nearest neighbor grader.The information of the time and frequency variation of Range Profile had both been utilized in this method simultaneously in this way, in turn avoided directly improving object recognition rate using the time-frequency distributions problem larger as information redundancy, calculation amount caused by identification feature.

Description

A kind of radar target identification method based on time-frequency distributions instantaneous frequency edge feature
Technical field
The invention belongs to Technology of Radar Target Identification field, it is related to Radar High Range Resolution time-frequency distributions characteristic, instantaneous The technology of frequency edges characteristic.
Background technology
The frequency of stationary signal is single and constant, distributed constant or distribution law not changed letter at any time Number.Fourier transformation is carried out to stationary signal, its frequency domain information can be analyzed, but also frequency can be incited somebody to action by inverse Fourier transform Domain signal transforms to time domain.With that can change over time, frequency is meeting for unstable signal (transient signal) its frequency and statistical property Variation.Fourier transformation gives signal frequency distribution situation on frequency spectrum, and provides the temporal information of frequency, i.e. transformation results It only shows without some frequency content, but does not provide the information that corresponding frequencies change over time, and this information is exactly Reflect most basic and most critical the property of non-stationary signal.This also means that it is necessary to study non-stationary signal m- frequencies at any time The information that rate changes simultaneously.
The energy of non-stationary signal is the function of T/F.Numerous Time-Frequency Distribution Algorithms have studied non-stationary signal Energy and T/F physical relationship, more profoundly describe the variation characteristic of signal.With simple time domain or frequency domain Change information is compared, and time-frequency distributions give the change information of signal energy m- frequency at any time simultaneously, is provided more comprehensive Recognize the information of non-stationary signal.
Wigner-Ville distribution gives the time-frequency distributions of signal, in numerous research signal Time-Frequency Distribution Algorithms The time-frequency locality highest of Wigner-Ville distributions, but there are cross-interference terms for the algorithm.Cross-interference terms are seriously done The analysis to signal time-frequency distributions has been disturbed, error analysis results are frequently can lead to.It is special using Wigner-Ville distribution as identification Sign, will certainly influence recognition effect.By carrying out time-frequency rearrangement to Wigner-Ville distribution, can effectively inhibit therein Time-frequency cross-interference terms, and then the analysis of the time-frequency distributions conducive to signal, the Wigner-Ville distribution after being reset with time-frequency are made For identification feature, it will improve discrimination.
Different target time-frequency distributions than it is more similar on the whole when, using the time-frequency distributions of suppressing crossterms as identification Feature recognition not rate will not be too ideal, therefore considers certain features of extraction time-frequency distributions, and time-frequency distributions have abundant wink Shi Tezheng.Comparing from data structure angle, temporal characteristics are often one-dimensional vector, and time-frequency distributions are two-dimensional matrixes, When thus the former is as identification, calculation amount will greatly reduce.Although contained data volume reduces, due to different target Certain temporal characteristics difference from time-frequency distributions it is obvious that individually extract, and as the foundation of identification, often obtains preferably Recognition effect.
Radar High Range Resolution (HRRP) is typical non-stationary signal, traditional based on HRRP recognition methods, is mostly Using the simple time domain or frequency domain of HRRP as identification feature, although also there is the time-frequency distributions feature based on HRRP to be identified , but it is HRRP time-frequency distributions features redundancy, computationally intensive, recognition effect needs to be further increased.
Invention content
The invention discloses a kind of radar HRRP target identification methods based on time-frequency distributions instantaneous frequency edge feature, it Using the instantaneous frequency edge feature of time-frequency distributions as identification feature, therefore the time of Range Profile had both been utilized in this method simultaneously With the information of frequency variation, in turn avoid directly larger using time-frequency distributions as information redundancy, calculation amount caused by identification feature Problem, improve object recognition rate.
The invention discloses a kind of radar target identification methods based on time-frequency distributions instantaneous frequency edge feature：Its feature It is, this approach includes the following steps：
Step 1, training Range Profile sample Y is pre-processed, eliminates translation sensibility and amplitude sensitive：
Training Range Profile sample matrix Y=[y1,y2,…,yi,…,yN]∈Rn×N, yi∈RnIt is n dimensional vectors apart from decent This, i=1,2 ..., N, Rn×NIndicate n × N-dimensional matrix；
In formula, τ is time delay, and f is frequency；
Step 3, time-frequency rearrangement is carried out to sample Wigner-Ville distribution, inhibits cross-interference terms in distribution：
WithThe time after Wigner-Ville distribution carries out time-frequency rearrangement and frequency, t and f are indicated respectively Indicate that the time of Wigner-Ville distribution and frequency, t ', f ' respectively represent time, frequency respectively；Time-frequency distributions after rearrangement It is denoted as Wy′；
Step 4, the instantaneous frequency edge feature of the time-frequency distributions after resetting is sought：
Step 5, the nonlinear characteristic contained in instantaneous frequency edge spy is linearized：
Using nonlinear function φ by instantaneous frequency edge featureIt is mapped to higher dimensional space, mapping result is And then the nonlinear characteristic contained in instantaneous frequency edge spy is linearized；For i-th of training sample Range Profile instantaneous frequency Edge feature, i=[1,2 ..., N]；
Step 6, according to kernel function principle, the nuclear matrix K of frequency edges Feature Mapping result is constructed：
By kernel function principle, frequency edges Feature Mapping resultInner product withKernel function κ value is equal, i.e.,
Build kernel matrix K：
Step 7, by the nuclear matrix K of training sample, projective transformation matrix X is trained：
Using two-dimensional principal component analysis algorithm, dimensionality reduction is carried out to nuclear matrix K, obtains projective transformation matrix X；X=[x1, x2,…,xd]∈RN×d, x1,x2,…,xd∈RNFor the feature vector corresponding to kernel matrix K characteristic values, d is feature vector Number；
Step 8, test sample is sought according to the projective transformation matrix X acquiredProjection properties matrix
Wherein,
Step 9：Using k nearest neighbor grader, classify to test sample, identifies target.
The two-dimentional time-frequency distributions of different target are more similar on the whole, and as identification feature, not only discrimination is relatively low, but also Operand is bigger, and the present invention uses the frequency edges feature of time-frequency distributions as identification feature, more due to frequency edges feature It can reflect the geometry and physical characteristic of different target, and it is one-dimensional vector, not only obtains higher discrimination in this way, And reduce calculation amount.
Description of the drawings
Fig. 1 is implementation steps flow chart of the present invention；
Fig. 2 be " 260 width Range Profiles of amp- 26 ", abscissa indicate per range from range cell, ordinate indicate distance The number of picture；
Fig. 3 be " diploma " 260 width Range Profiles, abscissa indicate per range from range cell, ordinate indicate distance The number of picture；
Fig. 4 be " Ya Ke -42 " 260 width Range Profiles, abscissa indicate range from range cell, ordinate indicate often away from Number from picture.
Specific implementation mode
Step 1：Training sample Y is built, and carries out eliminating translation sensibility and amplitude sensitive processing.
The HRRP data that the present invention uses are to use high-resolution wideband radar outfield measured data, packet by certain domestic research institute Include " Ya Ke -42 " medium-sized jet airplane, " diploma " miniature jet machine, " totally three kinds of targets such as amp- 26 " light propeller airplane HRRP data.The HRRP data of each target are the matrixes of 520 rows 256 row.Wherein, the representative of line number 520 has 520 ranges From picture, columns 256, which represents every width Range Profile, 256 range cells.
In order to probe into the ratio (instruction surveys ratio) of number of training and test sample number to the influence degree of recognition effect, difference It is surveyed in instruction than being 1:3、1:2、1:1、2:1、3:Experiment is identified under the conditions of 1.Ratio 1 is surveyed with instruction:1, i.e., each target is by 260 width Range Profile is for training, training sample Y=[y1,y2,…,y780], 260 width Range Profiles for testing for following the steps below.
The translation sensibility that Range Profile y is eliminated using gravity model appoach calculates the center of gravity of Range Profile first：
Then, by the central region of Range Profile to center of gravity W.
(2) l is used2Norm normalized cumulant eliminates the amplitude sensitive of Range Profile as y.
Step 2, the Wigner-Ville distribution W of training sample Y is calculatedy(t,f)：
In formula, τ is time delay, and f is frequency.
Step 3, time-frequency rearrangement is carried out to sample Wigner-Ville distribution, inhibits cross-interference terms in distribution：
WithFor the center of gravity of Wigner-Ville distribution, t and the center that f is Wigner-Ville distribution, t ', f ' are respectively Time, frequency are represented, the time-frequency distributions after rearrangement are denoted as Wy′。
Step 4, the instantaneous frequency edge feature of the time-frequency distributions after resetting is sought：
Step 5, the nonlinear characteristic contained in instantaneous frequency edge spy is linearized：
Using nonlinear function φ by instantaneous frequency edge featureIt is mapped to higher dimensional space, mapping result is And then the nonlinear characteristic contained in instantaneous frequency edge spy is linearized.For i-th of training sample Range Profile instantaneous frequency Edge feature, i=[1,2 ..., 780].
Step 6, according to kernel function principle, the nuclear matrix K of frequency edges Feature Mapping result is constructed：
By kernel function principle, frequency edges Feature Mapping resultInner product withCertain kernel function κ value is equal, i.e.,
Build kernel matrix K：
Step 7, by the nuclear matrix K of training sample, projective transformation matrix X is trained：
Using two-dimensional principal component analysis algorithm, dimensionality reduction is carried out to nuclear matrix K, obtains projective transformation matrix X.X=[x1, x2,…,x20]∈R780×20For projective transformation matrix, x1,x2,…,x20∈R780For the spy corresponding to kernel matrix K characteristic values Sign vector, the number of feature vector take 20；
Step 8, test sample is soughtProjection properties matrix
Wherein,Neighbour's number parameter of KNN graders is set as 3.
Amp- 26 that the present invention uses, the diploma, Ya Ke -42 three kinds of Aircraft Targets are set forth in Fig. 2, Fig. 3, Fig. 4 HRRP.As can be seen from the figure its different width HRRP of target even of the same race, difference is also bigger, this, which is also meaned that, directly makes Use HRRP as identification feature, recognition effect will not be too ideal.It is special it is therefore desirable to further excavate the time-frequency wherein contained Sign is more favorable for identification classification because time-frequency characteristics give the time domain and frequency domain information of HRRP simultaneously.Three width figures are compared may be used also To find, range cell number that every width HRRP of amp- 26 and Ya Ke -42 includes to be more than that the every width HRRP of the diploma include away from From number of unit.
Table 1 is based on Range Profile initial data object recognition rate；The table first row is meant that respectively with HRRP, HRRP Wigner-Ville is distributed, the instantaneous frequency edge feature of the Wigner-Ville distribution of HRRP is identification feature.In order to make pair Convincingness is had more than effect, projective transformation is carried out using the highest pivot analysis algorithm of each feature recognition rate is made.Wherein, feature HRRP and instantaneous frequency edge feature carry out projection properties transformation using KPCA algorithms, and Wigner-Ville distribution uses bilateral two It ties up principal component analysis and carries out projection properties transformation.The first row represents instruction and surveys ratio.It is discrimination (%) in table.
Table -1

Claims (1)

1. a kind of radar target identification method based on time-frequency distributions instantaneous frequency edge feature：It is characterized in that, this method packet Include following steps：
Step 1, training Range Profile sample Y is pre-processed, eliminates translation sensibility and amplitude sensitive：
Training Range Profile sample matrix Y=[y1,y2,…,yi,…,yN]∈Rn×N, yi∈RnFor n dimensional vector Range Profile samples, i =1,2 ..., N, Rn×NIndicate n × N-dimensional matrix；
In formula, τ is time delay, and f is frequency；
Step 3, time-frequency rearrangement is carried out to sample Wigner-Ville distribution, inhibits cross-interference terms in distribution：
WithThe time after Wigner-Ville distribution carries out time-frequency rearrangement and frequency, t and f difference are indicated respectively The time and frequency, t ', f ' for indicating Wigner-Ville distribution respectively represent time, frequency；Time-frequency distributions after rearrangement are denoted as Wy′；
Step 4, the instantaneous frequency edge feature of the time-frequency distributions after resetting is sought：
Step 5, the nonlinear characteristic contained in instantaneous frequency edge spy is linearized：
Using nonlinear function φ by instantaneous frequency edge featureIt is mapped to higher dimensional space, mapping result isIn turn The nonlinear characteristic contained in instantaneous frequency edge spy is linearized；For i-th of training sample Range Profile instantaneous frequency edge Feature, i=[1,2 ..., N]；
Step 6, according to kernel function principle, the nuclear matrix K of frequency edges Feature Mapping result is constructed：
By kernel function principle, frequency edges Feature Mapping resultInner product withKernel function κ value is equal,
I.e.
Build kernel matrix K：
Step 7, by the nuclear matrix K of training sample, projective transformation matrix X is trained：
Using two-dimensional principal component analysis algorithm, dimensionality reduction is carried out to nuclear matrix K, obtains projective transformation matrix X；X=[x1,x2,…, xd]∈RN×d, x1,x2,…,xd∈RNFor the feature vector corresponding to kernel matrix K characteristic values, d is the number of feature vector；
Step 8, test sample is sought according to the projective transformation matrix X acquiredProjection properties matrix
Wherein,
Step 9：Using k nearest neighbor grader, classify to test sample, identifies target.
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