CN103983959A - SAR system movement target radial speed estimation method based on data reconstruction - Google Patents
SAR system movement target radial speed estimation method based on data reconstruction Download PDFInfo
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9029—SAR image post-processing techniques specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
-
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/60—Velocity or trajectory determination systems; Sense-of-movement determination systems wherein the transmitter and receiver are mounted on the moving object, e.g. for determining ground speed, drift angle, ground track
- G01S13/605—Velocity or trajectory determination systems; Sense-of-movement determination systems wherein the transmitter and receiver are mounted on the moving object, e.g. for determining ground speed, drift angle, ground track using a pattern, backscattered from the ground, to determine speed or drift by measuring the time required to cover a fixed distance
-
- 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
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
The invention discloses an SAR system movement target radial speed estimation method based on data reconstruction. The SAR system movement target radial speed estimation method based on data reconstruction mainly resolves the problems in the prior art that training samples are not enough, and the clutter energy of the training samples and the clutter energy of a unit to be detected are not matched. The SAR system movement target radial speed estimation method based on data reconstruction comprises the steps that 1, radar image data are obtained through radar imaging processing; 2, the data of the unit to be detected are extracted from the radar image data, and the power spectrum of the unit to be detected is estimated through an IAA algorithm; 3, the energy of clutter and noise is determined according to the power spectrum to calculate a clutter-noise ratio; 4, the range where the clutter is located is determined according to the clutter-noise ratio, and a clutter plus noise covariance matrix is constructed according to the range and the power spectrum; 5, the radial speed of a movement target is estimated through the clutter plus noise covariance matrix. The SAR system movement target radial speed estimation method based on data reconstruction does not need training samples, when the clutter energy of the training samples and the clutter energy of the unit to be detected are not matched, the radial speed of the movement target can be accurately estimated, and the SAR system movement target radial speed estimation method based on data reconstruction can be applied to the fields of radar, communication and the like to achieve estimation of radial speed parameters of the movement target.
Description
Technical field
The present invention relates to radar signal processing field, the particularly method of moving target radial velocity parameter estimation in SAR system, can be for the estimation of moving target radial velocity in SAR system.
Background technology
Along with the development of Radar Technology, there is the SAR-GMTI system that synthetic-aperture radar SAR combines with ground moving target display technique GMTI.SAR-GMTI system is conducive to obtain observation scene Static and dynamic two kinds of information, just progressively becomes the study hotspot that airborne or spaceborne radar is realized GMTI function.SAR-GMTI, by scene echoes phase history matching treatment is obtained to higher azimuth discrimination ability, is conducive to estimation and the reorientation of moving target parameter.But, in moving-target radial velocity is estimated, because ground static target is that clutter and moving target data exist simultaneously, and there is competitive relation, can cause the decline of moving target radial velocity estimated performance.In actual treatment, usually need clutter to suppress, to accurately estimate the kinematic parameter of moving target.
F.C.Robey, D.R.Fuhrmann, E.J.Kelly and R.Nitzberg propose adaptive matched filter method in " A CFAR adaptive matched filter detector " (IEEE Trans.Aerosp.Electron.Syst, the 28th phase 208-216 page in 1992) literary composition.The method utilizes near clutter data estimation unit to be detected to go out the clutter covariance matrix of unit to be detected, forms adaptive matched filter weight vector, then treats detecting unit data weighting clutter reduction, estimates the kinematic parameter of moving target.The shortcoming of the method is when the energy of the clutter of lack of training samples or training sample and unit to be detected clutter exists unmatched situation, can cause the estimated performance of moving target parameter to decline.
Summary of the invention
The object of the invention is to for above-mentioned the deficiencies in the prior art, a kind of SAR system motion target radial speed method of estimation based on data reconstruction is proposed, while there is unmatched situation with the energy of the clutter at lack of training samples or training sample and unit to be detected clutter, improve the estimated performance of moving target parameter.
The basic ideas of the object of the invention are: first utilize IAA algorithm to estimate the power spectrum of unit to be detected, according to spectra calculation, go out unit to be detected miscellaneous noise ratio, utilize miscellaneous noise ratio to determine the interferometric phase scope at clutter place.Then according to the in-scope of power spectrum and clutter, construct the clutter plus noise covariance matrix of unit to be detected, finally carry out adaptive matched filter estimating target motion parameter.
For achieving the above object, technical scheme of the present invention comprises the steps:
(1) L of radar passage echo data carried out to imaging processing and obtain L width image, L >=2;
(2) according to L width view data, an interferometric phase without fuzzy interval in, utilize IAA algorithm to estimate that moving-target place pixel is the power spectrum of unit to be detected
(3) construct unit to be detected clutter plus noise covariance matrix
(3a) according to the interferometric phase of Clutter, be the power spectrum of 0 ° and unit to be detected
obtain the clutter energy of unit to be detected
and using the minimum value of cell power spectrum to be detected as noise energy
by clutter energy and noise energy, obtain the miscellaneous noise ratio of unit to be detected
(3b) according to be detected, treat that the miscellaneous noise ratio CNR of unit determines related coefficient
calculate the probability density function of clutter and noise profile:
Wherein, Γ represents Euler integral of the second kind,
represent Gaussian hypergeometric function, n represents to look number more, is that 1, θ represents clutter interferometric phase center herein, is 0 herein,
the interferometric phase that represents clutter;
(3c) according to probability density function
calculate the variances sigma 2 that clutter distributes:
(3d) according to the interferometric phase of Clutter, be 0 ° and variances sigma
2, obtain clutter range of energy distribution and be: Θ ∈ (0-σ
2, 0+ σ
2);
(3e), according to the distribution range Θ of clutter energy, calculate clutter plus noise covariance matrix:
Wherein,
represent cell power spectrum to be detected
energy in corresponding clutter range of energy distribution Θ, a
c(θ) spatial domain steering vector when expression interferometric phase is θ, I representation unit matrix;
(4) according to clutter plus noise correlation matrix
estimate moving target radial velocity:
In formula
spatial domain steering vector for moving target.
The present invention compared with prior art has the following advantages:
The present invention is owing to utilizing cell data to be detected to construct the clutter plus noise covariance matrix of unit to be detected
training sample clutter energy and the unmatched problem of unit clutter energy to be detected have been avoided; Simultaneously due to structure unit to be detected clutter plus noise covariance matrix
process do not need training sample, avoided the problem of lack of training samples.
Accompanying drawing explanation
Fig. 1 is realization flow figure of the present invention;
Fig. 2 is existing synthetic-aperture radar ground moving target display system SAR-GMTI schematic diagram;
Fig. 3 is the sub-process figure of iterative estimate power spectrum in the present invention;
Fig. 4 is with after traditional adaptive matched filter method AMF and the inventive method clutter reduction, the assorted correlation curve of making an uproar of output letter;
Fig. 5 is by traditional adaptive matched filter method AMF and the inventive method, estimates the correlation curve of movement parameter of moving target precision;
Fig. 6 is that the Parameter Estimation Precision of traditional adaptive matched filter method and the inventive method is with the change curve of training sample clutter energy.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in detail:
The present invention is a kind of SAR system motion target radial speed method of estimation based on data reconstruction, the estimation of moving target radial velocity parameter is the pith that blended space Radar Moving Target shows, during parameter estimation, only be concerned about the echoed signal of moving target, but the data that receive are inevitably mixed with the clutter of ground static scene, there is competitive relation in the signal of clutter and moving target, causes the estimated accuracy severe exacerbation of moving target radial velocity parameter.In order accurately to estimate moving target radial velocity parameter, need to suppress unit to be detected clutter, still, it is separated that unit to be detected clutter and Moving Target Return are difficult to, and uses a large amount of training samples to estimate unit to be detected clutter in practical application.This just there will be number of training not enough, training sample clutter energy and the unmatched problem of unit clutter energy to be detected, and these problems can cause the estimated performance of moving target radial velocity parameter to decline.The present invention is directed to the problems referred to above, in the situation that not using training sample, utilize cell data to be detected to construct the clutter plus noise covariance matrix of unit to be detected, can effectively suppress unit to be detected noise signal, estimate the radial velocity parameter of moving target.
With reference to Fig. 1, specific embodiment of the invention step is as follows:
Step 1, carries out imaging processing to the L of radar passage echo data, obtains L width image, L >=2.
The synthetic-aperture radar ground moving target display system SAR-GMTI that the present invention uses, as shown in Figure 2, wherein Texas tower is comprised of L evenly distributed array element, and the operation wavelength of radar is λ, and the translational speed of platform is v
a, the radial velocity of moving target is v
r.The mode of operation of first passage is the send out/collection of letters number, and other passage is the responsible signal that receives only; Each passage echo data is carried out to imaging processing and obtain L width image.
Step 2, obtains the vector form of L width radar image data.
View data by corresponding same pixel in L width image, is sequentially arranged in vector form by the passage shown in Fig. 2, and the spatial information (si) that this data vector has comprised moving target can be used for estimating the radial velocity parameter of moving target.
Due in the ideal case, each pixel of radar image meets independent same distribution, and ground moving target is in a pixel, and the complex data form that L width view data is arranged in vector can be expressed as following form:
H
0:X
0(m,n)＝γ
c(m,n)⊙a
c+N(m,n)
H
1:X
1(m,n)＝γ
c(m,n)⊙a
c+N(m,n)+γ
s(m,n)⊙a
s(v
r)
Wherein m and n represent respectively radar image middle distance to orientation to coordinate; H
0represent that unit to be detected does not comprise moving target; H
1represent that unit to be detected comprises moving target; ;
represent L * 1 dimension additive white Gaussian noise data vector;
represent the clutter equivalence back scattering of given resolution element;
the moving-target equivalence that represents resolution element is backward loose;
clutter spatial domain steering vector after compensation;
spatial domain steering vector for moving target; []
Τthe computing of table transposition.
Step 3, utilizes the vector data of pixel to be detected, an interferometric phase without fuzzy interval in, in this example, be from-π to π, use IAA algorithm to estimate the power spectrum of pixel to be detected
With reference to Fig. 3, the specific implementation step of this step is as follows:
(3a) in-π to π, data to be tested are carried out to matched filtering, the energy in obtain-π to π under different interferometric phases, the power spectrum of unit to be detected is:
Wherein, z represents to revise number of times, z=0 here, X
cUTrepresent elementary echo data vector to be detected;
represent different interferometric phases
under spatial domain steering vector; K represents the umber that 2 π are on average divided, and gets 3600 in this example; []
Ηrepresent conjugate transpose computing.
(3b) in-π to π, according to cell power spectrum structure to be detected cell data covariance matrix to be detected:
(3c) according to covariance matrix R
z, revise the power spectrum of unit to be detected:
(3d) order
convergence threshold ε is set, and ε value is slightly larger than 0, judges whether cell power spectrum to be detected restrains: if
set up, cell power spectrum to be detected convergence, obtains accurate power spectrum to be detected
otherwise cell power spectrum to be detected does not restrain, and makes z=z+1, returns to step (3b) and circulates successively.
Step 4, calculates the miscellaneous noise ratio of unit to be detected.
It according to the interferometric phase of Clutter, is the power spectrum of 0 ° and unit to be detected
the energy that obtains corresponding interferometric phase is the clutter energy of unit to be detected
and using the minimum value of cell power spectrum to be detected as noise energy
By clutter energy and noise energy, obtain the miscellaneous noise ratio of unit to be detected:
Step 5, determines the distribution range Θ of clutter according to miscellaneous noise ratio.
(5a) according to be detected, treat that the miscellaneous noise ratio CNR of unit determines related coefficient
calculate the probability density function of clutter and noise profile:
Wherein, Γ represents Euler integral of the second kind,
represent Gaussian hypergeometric function, n represents to look number more, is that 1, θ represents clutter interferometric phase center herein, is 0 herein,
the interferometric phase that represents clutter.
(5b) according to probability density function
calculate the variances sigma that clutter distributes
2:
(5c) variances sigma distributing according to clutter
2interferometric phase in conjunction with Clutter is 0 °, obtains clutter range of energy distribution: Θ ∈ (0-σ
2, 0+ σ
2).
Step 6, according to the cell power spectrum to be detected estimating and the distribution range Θ of clutter, constructs unit to be detected clutter plus noise covariance matrix
Wherein,
represent cell power spectrum to be detected
energy in corresponding clutter range of energy distribution Θ, a
c(θ) spatial domain steering vector when expression interferometric phase is θ, I representation unit matrix.
Step 7, according to clutter plus noise correlation matrix
estimate moving target radial velocity:
In formula
spatial domain steering vector for moving target.
Effect of the present invention can be illustrated by following emulation experiment:
1. simulated conditions:
Radar system in this example is used the existing Moving Target with Airborne Synthetic Aperture Radar shown in Fig. 2 to show SAR-GMTI system, and it is comprised of 3 equally distributed linear arrays, and array element distance is 0.5m, and the platform speed of mobile system is v
a=100m/s, the operation wavelength of radar is λ=0.03m, and ground scene is that training sample size is 50 * 50, and unit to be detected miscellaneous noise ratio is 30dB, and signal to noise ratio (S/N ratio) is 20dB.
2. emulation content and result:
Emulation 1: be respectively 10dB at ground scene clutter energy, 30dB, in the situation of 50dB, the performance comparison of carrying out clutter inhibition by the inventive method and traditional adaptive matched filter method, result is as Fig. 4, and wherein Fig. 4 (a) is the situation that ground scene clutter energy is 10dB; The situation that Fig. 4 (b) is 30dB for ground scene clutter energy; The situation that Fig. 4 (c) is 50dB for ground scene clutter energy.
From as Fig. 4 (a), output letter miscellaneous noise ratio of the present invention is higher than traditional adaptive matched filter method, and the better effects if of the inventive method to clutter inhibition be described; From 4 (b), Fig. 4 (c) is visible, and output letter miscellaneous noise ratio of the present invention and traditional adaptive matched filter method are basic identical, illustrates that the inventive method is basic identical to effect and traditional adaptive matched filter method of clutter inhibition; The inventive method is in training sample clutter energy and the unmatched situation of unit clutter energy to be detected, can accurately suppress unit to be detected clutter energy, reduce the competition of clutter and moving target, and the present invention do not need training sample, overcome the problem of number of training deficiency.
Emulation 2: be respectively 10dB at ground scene clutter energy, 30dB, in the situation of 50dB, by the inventive method and traditional adaptive matched filter method, estimate the performance comparison of movement parameter of moving target, result is as Fig. 5, and wherein Fig. 5 (a) is the situation that ground scene clutter energy is 10dB; The situation that Fig. 5 (b) is 30dB for ground scene clutter energy; The situation that Fig. 5 (c) is 50dB for ground scene clutter energy.
From Fig. 5 (a), the ratio of precision of the inventive method estimating speed tradition adaptive matched filter method is high; From Fig. 5 (b) and 5 (c), the precision of the inventive method estimating speed and traditional adaptive matched filter method are basic identical; By experimental result, show that the inventive method is in training sample clutter energy and the unmatched situation of unit clutter energy to be detected, can accurately estimate the radial velocity parameter of moving target, and the present invention does not need training sample, overcome the problem of number of training deficiency.
Emulation 3: in the situation that moving target radial velocity is 1m/s, by the inventive method and traditional adaptive matched filter method, simulation parameter estimated accuracy is with the variation relation of ground scene clutter energy, and its result as shown in Figure 6.
As seen from Figure 6, when ground scene clutter energy is during lower than unit to be detected clutter energy, the ratio of precision tradition adaptive matched filter method of the estimating speed of the inventive method is high; When ground scene clutter energy equals or during higher than unit to be detected clutter energy, the precision of the estimating speed of the inventive method and traditional adaptive matched filter method are basic identical; By experimental result, show that the inventive method is in training sample clutter energy and the unmatched situation of unit clutter energy to be detected, can accurately estimate the radial velocity parameter of moving target, and the present invention does not need training sample, overcome the problem of number of training deficiency.
To sum up, the SAR system motion target radial speed method of estimation based on data reconstruction of the present invention mainly solves two problems of prior art: the first, and the problem of number of training deficiency; The second, training sample and the unmatched problem of unit clutter energy to be detected, these two problems all can cause the estimated performance of moving target radial velocity to decline.
Claims (2)
1. the SAR system motion target radial speed method of estimation based on data reconstruction, comprises the steps:
(1) L of radar passage echo data carried out to imaging processing and obtain L width image, L >=2;
(2) according to L width view data, an interferometric phase without fuzzy interval in, utilize IAA algorithm to estimate that moving-target place pixel is the power spectrum of unit to be detected
(3) construct unit to be detected clutter plus noise covariance matrix
(3a) according to the interferometric phase of Clutter, be the power spectrum of 0 ° and unit to be detected
obtain the clutter energy of unit to be detected
and using the minimum value of cell power spectrum to be detected as noise energy
by clutter energy and noise energy, obtain the miscellaneous noise ratio of unit to be detected
(3b) according to be detected, treat that the miscellaneous noise ratio CNR of unit determines related coefficient
calculate the probability density function of clutter and noise profile:
Wherein, Γ represents Euler integral of the second kind,
represent Gaussian hypergeometric function, n represents to look number more, is that 1, θ represents clutter interferometric phase center herein, is 0 herein,
the interferometric phase that represents clutter;
(3c) according to probability density function
calculate the variances sigma that clutter distributes
2:
(3d) according to the interferometric phase of Clutter, be 0 ° and variances sigma
2, obtain clutter range of energy distribution and be: Θ ∈ (0-σ
2, 0+ σ
2);
(3e), according to the distribution range Θ of clutter energy, calculate clutter plus noise covariance matrix:
Wherein,
represent cell power spectrum to be detected
energy in corresponding clutter range of energy distribution Θ, a
c(θ) spatial domain steering vector when expression interferometric phase is θ, I representation unit matrix;
(4) according to clutter plus noise correlation matrix
estimate moving target radial velocity:
In formula
spatial domain steering vector for moving target.
2. method according to claim 1, the IAA algorithm that utilizes that wherein step (2) is described estimates that moving-target place pixel is the power spectrum of unit to be detected
carry out as follows:
(2a) utilize beamforming algorithm at an interferometric phase, to estimate the power spectrum of unit to be detected in without fuzzy interval, the power spectrum according to a preliminary estimate that obtains unit to be detected is:
Wherein, z=0, X
cUTrepresent elementary echo data vector to be detected;
represent different interferometric phases
under spatial domain steering vector; K represents the umber that an interferometric phase is on average divided without fuzzy interval, []
Ηrepresent conjugate transpose computing;
(2b) according to the cell power spectrum to be detected of estimating, construct cell data covariance matrix to be detected:
(2c) according to covariance matrix R
zright, treat the power spectrum of detecting unit and revise, obtain revised result:
(2d) order
convergence threshold ε is set, and ε value is slightly larger than 0, judges whether cell power spectrum to be detected restrains: if
set up, cell power spectrum to be detected convergence, obtains accurate power spectrum to be detected
otherwise cell power spectrum to be detected does not restrain, and makes z=z+1, returns to step (2b) and circulates successively.
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| CN108318879A (en) * | 2017-12-29 | 2018-07-24 | 哈尔滨工业大学 | ISAR image transverse direction calibrating methods based on IAA Power estimation technologies |
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| CN108318879A (en) * | 2017-12-29 | 2018-07-24 | 哈尔滨工业大学 | ISAR image transverse direction calibrating methods based on IAA Power estimation technologies |
| CN108318879B (en) * | 2017-12-29 | 2021-08-31 | 哈尔滨工业大学 | ISAR image transverse calibration method based on IAA spectrum estimation technology |
| CN109655793A (en) * | 2018-12-07 | 2019-04-19 | 南京邮电大学 | The method that higher-frequency radar extracts echo from glitch |
| CN113534149A (en) * | 2021-06-04 | 2021-10-22 | 西安电子科技大学 | Distributed radar imaging method based on MIAA |
| CN113466861A (en) * | 2021-06-18 | 2021-10-01 | 上海交通大学 | Multichannel SAR-GMTI image registration method and system based on IAA processing |
| CN113466861B (en) * | 2021-06-18 | 2022-04-26 | 上海交通大学 | Multichannel SAR-GMTI image registration method and system based on IAA processing |
| CN115856892A (en) * | 2023-03-03 | 2023-03-28 | 西安电子科技大学 | RPCA moving target detection method based on data reconstruction |
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