CN112255608A - Radar clutter self-adaptive suppression method based on orthogonal projection - Google Patents

Radar clutter self-adaptive suppression method based on orthogonal projection Download PDF

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CN112255608A
CN112255608A CN202011090604.1A CN202011090604A CN112255608A CN 112255608 A CN112255608 A CN 112255608A CN 202011090604 A CN202011090604 A CN 202011090604A CN 112255608 A CN112255608 A CN 112255608A
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clutter
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pulse pressure
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王中宝
尹奎英
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CETC 14 Research Institute
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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
    • G01S7/414—Discriminating targets with respect to background clutter

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Abstract

The invention discloses a radar clutter self-adaptive suppression method based on orthogonal projection, which comprises the following steps of: acquiring original radar pulse pressure data; selecting pulse pressure data to obtain clutter sample data; estimating a clutter covariance matrix by utilizing clutter sample data; performing eigenvalue decomposition on the clutter covariance matrix; constructing clutter subspace of the pulse pressure data by the characteristic vector corresponding to the characteristic value; calculating an orthogonal projection matrix of the clutter subspace to obtain a noise subspace; and performing orthogonal projection on the original radar pulse pressure data on a noise subspace to obtain the pulse pressure data after wave elimination. The invention achieves the purpose of extracting the interested target and inhibiting clutter by means of orthogonal projection; the method can be adjusted according to the measured data, has strong self-adaptive capacity, can be widely applied to ground clutter and sea clutter scenes, effectively improves the accuracy of target detection, and reduces false alarms.

Description

Radar clutter self-adaptive suppression method based on orthogonal projection
Technical Field
The invention relates to the field of radars, in particular to a radar clutter self-adaptive suppression method based on orthogonal projection.
Background
Radar (Radio, Radio Detection and Ranging) is an abbreviation of "Radio Detection and Ranging", and the main working principle is that electromagnetic wave irradiation is carried out on an interested observation area through a transmitting antenna, when a target appears in a Radar beam irradiation area, a scattering echo can be captured by a Radar receiver, and then echo signals are processed and analyzed, so that information such as the distance, the direction, the speed and the like of the target is obtained. Compared with other sensors, the radar has the characteristics of long detection distance, wide action range, small influence of weather and the like, is widely applied to the fields of military and civil use and plays an important role.
The radar clutter is a general term for echo signals that interfere with normal operation of the radar, such as ground clutter and sea clutter, in addition to useful target echoes. Because the target that the radar surveyed can not isolate the appearance, always there is the clutter that multiple factor combined action produced around it, radar system if do not carry out effective processing to the clutter signal this moment, will lead to showing to be difficult to observe the target on the terminal, in addition, the existence of a large amount of clutter will bring very high calculation burden for data processing, and arouse a large amount of false alarms easily, the radar will normally work when serious. Therefore, clutter suppression has been a hot problem in the field of radar signal processing.
The clutter suppression is to perform mathematical model construction and parameter adjustment on the clutter in the current working environment of the radar in a signal processing mode on the basis of deep analysis of radar clutter characteristics, and design a reasonable clutter suppression method so as to achieve the purposes of suppressing the clutter and retaining a target. Essentially, the clutter suppression mainly aims to improve the power ratio (signal-to-clutter ratio) of the target relative to the clutter, ensure the detection accuracy of the radar to the target and reduce the generation of false alarms.
At present, the clutter suppression and target detection methods commonly used in the conventional radar mainly include a moving target display technology (MTI), a moving target detection technology (MTD), a clutter map, a Constant False Alarm Rate (CFAR), and the like. The methods have the advantages of simple engineering implementation, low calculation amount, stable performance and better stationary clutter suppression effect, and have the defects of no self-adaptive capability, and poor clutter suppression effect when the clutter has a certain speed or the speed is equal to the target speed.
Disclosure of Invention
In order to solve the above problems, the present invention provides an orthogonal projection-based radar clutter adaptive suppression method, which includes the following steps:
acquiring original radar pulse pressure data;
selecting pulse pressure data to obtain clutter sample data;
estimating a clutter covariance matrix by utilizing clutter sample data;
performing eigenvalue decomposition on the clutter covariance matrix;
constructing clutter subspace of the pulse pressure data by the characteristic vector corresponding to the characteristic value;
calculating an orthogonal projection matrix of the clutter subspace to obtain a noise subspace;
and performing orthogonal projection on the original radar pulse pressure data on a noise subspace to obtain the pulse pressure data after wave elimination.
Further, the clutter sample data simultaneously satisfies the following condition:
containing clutter signals;
does not contain a target signal;
the clutter statistical characteristics are the same or similar;
noise is not correlated with clutter statistics;
the noise is statistically uncorrelated with each other and has the same variance.
Further, the original radar pulse pressure data X specifically includes:
X＝S+C+W
wherein S is a target matrix, C is a clutter matrix, W is a noise matrix, and X, S, C and W are both M multiplied by N matrices; m is the number of sampling points of a single pulse, and N represents the number of pulses.
Further, the clutter sample data is specifically expressed as:
Figure BDA0002721915240000021
wherein the content of the first and second substances,
Figure BDA0002721915240000022
are all K multiplied by N matrixes, K is the number of samples,
Figure BDA0002721915240000023
is a matrix of sample clutter for the sample,
Figure BDA0002721915240000024
is a noise matrix of samples.
Further, the clutter sample data covariance matrix specifically is:
Figure BDA0002721915240000025
wherein the content of the first and second substances,
Figure BDA0002721915240000026
clutter covariance matrix, σ, of NxN2Is a noise variance, I is an identity matrix [ alpha ] having diagonal elements of 1 and the remaining elements of 0]HFor the conjugate transpose operator, R and I are both N matrices.
Further, the clutter sample data covariance matrix
Figure BDA0002721915240000027
The eigenvalue decomposition of (a) is expressed as:
Figure BDA0002721915240000028
where R is the rank of R, i.e. R ═ rank (R), U ═ U1 u2 … un … uN]TIs a matrix of NxN, sigma ═ diag (lambda)1 λ2 … λn … λN) Is an NxN matrix, scalar lambdanSum vector un(N is 1,2, …, N) is the eigenvalue of the clutter covariance matrix R and its corresponding eigenvector, vector unBeing an N × 1 matrix, diag () represents a diagonal matrix.
Further, the constructing of the clutter subspace of the pulse pressure data by the feature vectors corresponding to the feature values specifically includes:
order to
Figure BDA0002721915240000029
Then
Figure BDA00027219152400000210
Figure BDA00027219152400000211
Wherein the content of the first and second substances,
Figure BDA0002721915240000031
Figure BDA0002721915240000032
Figure BDA0002721915240000033
Figure BDA0002721915240000034
Figure BDA0002721915240000035
and
Figure BDA0002721915240000036
respectively, the first r large eigenvalues and the matrix formed by the eigenvectors corresponding to the (N-r) small eigenvalues, wherein,
Figure BDA0002721915240000037
is a matrix of N x r, and the matrix is a square matrix,
Figure BDA0002721915240000038
is a matrix of N (N-r),
Figure BDA0002721915240000039
are all in the form of a matrix of r x r,
Figure BDA00027219152400000310
is a matrix of (N-R) x (N-R), the big eigenvalue is the first R nonzero eigenvalues lambda 'selected by sorting the eigenvalues of the clutter covariance matrix R from big to small'iWherein i ═ 1,2, …, r, λ'1≥λ′2≥…≥λ′rThe remaining (N-r) eigenvalues are small eigenvalues;
the characteristic of characteristic value decomposition shows that U is unitary matrix and has the following properties
Figure BDA00027219152400000311
Clutter subspace of the structure
Figure BDA00027219152400000312
Is composed of
Figure BDA00027219152400000313
Wherein, the inner product of the matrix
Figure BDA00027219152400000314
Further, the specific calculation formula of the noise subspace is as follows:
Figure BDA00027219152400000315
further, the pulse pressure data after removing the noise specifically includes:
Figure BDA00027219152400000316
wherein, Y is an M multiplied by N matrix and represents the pulse pressure data after removing the noise.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention achieves the purpose of extracting the interested target and inhibiting clutter by means of orthogonal projection;
2. the phase coherence of the target component after orthogonal projection cannot be damaged, and coherent accumulation processing can be still performed to obtain a better target signal-to-noise ratio output result;
3. the method can be adjusted according to the measured data, has strong self-adaptive capacity, can be widely applied to ground clutter and sea clutter scenes, effectively improves the accuracy of target detection, and reduces false alarms.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a diagram of the real part of the transmitted signal.
Fig. 3 is a diagram of the time-frequency relationship of the transmitted signals.
Fig. 4 is a time domain power distribution diagram of pulse pressure data.
Figure 5 is a doppler power distribution at the location of the target.
FIG. 6 is a diagram of the wave covariance matrix eigenvalue energy distribution results.
Fig. 7 is a time domain power distribution diagram of pulse pressure data after orthogonal projection.
Fig. 8 is a doppler power distribution diagram of the position of the target after orthogonal projection.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Example 1:
the flow of the specific technical scheme of the invention is shown in figure 1, and comprises the following seven steps:
step one, acquiring original radar pulse pressure data
Suppose that the data vector formed by pulse pressure processing of the radar echo signal of a single pulse is
x＝[x1 x2 … xM]T (1)
Wherein x is a matrix of M × 1, and M is the number of sampling points of a single pulse, [ alpha ]]TFor the transpose operator, the pulse pressure data matrix of N pulses can be represented as
X＝[x1 x2 … xN]T (2)
Wherein X is an M × N matrix; it is known that there is inevitably a noise (or observation) error in the pulse pressure data matrix, and there may be equal components of target and clutter, so X can be expressed by the following equation
X＝S+C+W (3)
Where S is the target matrix, C is the clutter matrix, W is the noise matrix, S, C and W are both M N matrices. Mathematically, the main objective of the present invention is to extract the desired target signal S from the pulse data matrix X, while suppressing clutter C and noise W to the maximum.
In general, the radar receiver may obtain raw pulse pressure data after frequency mixing, sampling, pulse pressure processing, and the like, where the radar pulse pressure data is provided in a computer simulation manner, where a real part waveform of a transmitted signal is shown in fig. 2, a spectrum structure is shown in fig. 3, a time domain energy distribution of the radar pulse pressure data is shown in fig. 4, and a result of a doppler energy distribution of a target location is shown in fig. 5.
Step two, performing clutter sample data selection on the pulse pressure data
In order to extract the desired object from the pulse data matrix, a certain number of clutter sample data should be selected from the pulse data matrix, and the sample data should satisfy the following five conditions:
(1) containing clutter signals;
(2) does not contain a target signal;
(3) the clutter statistical characteristics are the same or similar;
(4) noise is not correlated with clutter statistics;
(5) the noise is statistically uncorrelated with each other and has the same variance.
Let the clutter sample data matrix be
Figure BDA0002721915240000051
Wherein the content of the first and second substances,
Figure BDA0002721915240000052
are all K multiplied by N matrixes, K is the number of samples,
Figure BDA0002721915240000053
is a matrix of sample clutter for the sample,
Figure BDA0002721915240000054
is a noise matrix of samples.
Considering the problem that energy leakage may exist in adjacent units at the position of the target, in order to meet the clutter sample data selection condition, 3 units should be left on the left and right of the unit to be detected for protection (or isolation) in the clutter sample data selection, and 24 units, namely K is 48, are selected on the left and right of the unit to be detected as the clutter sample data.
Thirdly, estimating a clutter covariance matrix by using the selected clutter sample data
The covariance matrix of clutter sample data obtained from the correlation matrix is
Figure BDA0002721915240000055
Wherein the content of the first and second substances,
Figure BDA0002721915240000056
covariance matrix of N × N clutter2Is a noise variance, I is an identity matrix [ alpha ] having diagonal elements of 1 and the remaining elements of 0]HFor the conjugate transpose operator, R and I are both N matrices.
Fourthly, eigenvalue decomposition is carried out on the covariance matrix
Let R be R, i.e. rank (R) ═ R, then the clutter sample data covariance matrix
Figure BDA0002721915240000057
The eigenvalue decomposition of (A) can be expressed as
Figure BDA0002721915240000058
Wherein U is [ U ]1 u2 … un … uN]TIs a matrix of NxN, sigma ═ diag (lambda)1 λ2 … λn … λN) Is an NxN matrix, scalar lambdanSum vector un(N is 1,2, …, N) is the eigenvalue of the matrix R and its corresponding eigenvector, vector unBeing an N × 1 matrix, diag () represents a diagonal matrix.
The result of the energy distribution of the characteristic spectrum of the whole sample data space is obtained by decomposition and is shown in fig. 6. As can be seen from fig. 6, there is only one large feature value in the feature spectrum, i.e., r ═ 1.
Step five, constructing clutter subspace of pulse pressure data by the characteristic vectors corresponding to the first r large characteristic values
Order to
Figure BDA0002721915240000059
The above formula can be abbreviated as
Figure BDA00027219152400000510
Further, sorting the eigenvalues of the clutter covariance matrix R from large to small, and selecting the first R nonzero eigenvalues as large eigenvalues lambda'i(i＝1,2,…,r)，λ′1≥λ′2≥…≥λ′rIt is clear that if the clutter echo power is large enough, i.e. λ'rRatio sigma2Obviously large, the remaining (N-r) eigenvalues are small eigenvalues, at this time
Figure BDA0002721915240000061
Can be expressed as
Figure BDA0002721915240000062
Wherein the content of the first and second substances,
Figure BDA0002721915240000063
Figure BDA0002721915240000064
Figure BDA0002721915240000065
Figure BDA0002721915240000066
namely, it is
Figure BDA0002721915240000067
And
Figure BDA0002721915240000068
respectively, r large eigenvalues and (N-r) small eigenvectors, wherein,
Figure BDA0002721915240000069
is a matrix of N x r, and the matrix is a square matrix,
Figure BDA00027219152400000610
is a matrix of N (N-r),
Figure BDA00027219152400000611
are all in the form of a matrix of r x r,
Figure BDA00027219152400000612
is a matrix of (N-r) × (N-r),
Figure BDA00027219152400000613
is an N × N matrix.
The characteristic of characteristic value decomposition shows that U is unitary matrix and has the following properties
Figure BDA00027219152400000614
Clutter subspace constructable by the above results
Figure BDA00027219152400000615
Is composed of
Figure BDA00027219152400000616
Wherein, the inner product of the matrix
Figure BDA00027219152400000617
Figure BDA00027219152400000618
Also known as projection matrices.
Step six, calculating an orthogonal projection matrix of a clutter subspace
Projection matrix from clutter subspace
Figure BDA00027219152400000619
Can be used forEasily obtain its orthogonal projection matrix
Figure BDA00027219152400000620
Is shown as
Figure BDA00027219152400000621
Wherein the content of the first and second substances,
Figure BDA00027219152400000622
for an N × N matrix, it is easy to find that the orthogonal projection matrix of the clutter subspace is the noise subspace, in other words, the clutter subspace and the noise subspace should be strictly orthogonal, one of them is known, and the other is uniquely determined.
Step seven, orthogonal projection is carried out on the pulse pressure data vector on a noise subspace to obtain the pulse pressure data after wave elimination
After the orthogonal projection matrix of the clutter subspace is calculated by the formula, multiplying the pulse pressure data vector by the orthogonal projection matrix, namely projecting the pulse pressure data into the noise subspace orthogonal to the clutter subspace, wherein the characteristics of orthogonal projection are utilized to know that the clutter component in the pulse pressure data vector can be eliminated, the target and the noise component are not influenced, and the mathematical relationship can be expressed as
Figure BDA00027219152400000623
Wherein, Y is an M multiplied by N matrix and represents the pulse pressure data after removing the noise.
FIG. 7 shows the range spectrum after clutter suppression, and FIG. 8 shows the Doppler spectrum of the target location after clutter suppression. Comparing the results of fig. 7 and fig. 4, the clutter suppressed targets are already very visible in the distance spectrum, indicating that the targets gain a signal-to-clutter ratio in the distance spectrum of more than 20dB after clutter removal. In comparison with the results of fig. 5, the energy of the target remains substantially constant in the doppler spectrum before and after the clutter is suppressed. The method achieves the purpose of extracting the target of interest and inhibiting the clutter by means of orthogonal projection, and is not difficult to see from the implementation operation.

Claims (9)

1. A radar clutter self-adaptive suppression method based on orthogonal projection is characterized by comprising the following steps:
acquiring original radar pulse pressure data;
selecting pulse pressure data to obtain clutter sample data;
estimating a clutter covariance matrix by utilizing clutter sample data;
performing eigenvalue decomposition on the clutter covariance matrix;
constructing clutter subspace of the pulse pressure data by the characteristic vector corresponding to the characteristic value;
calculating an orthogonal projection matrix of the clutter subspace to obtain a noise subspace;
and performing orthogonal projection on the original radar pulse pressure data on a noise subspace to obtain the pulse pressure data after wave elimination.
2. The adaptive suppression method for radar clutter based on orthogonal projection according to claim 1, wherein the clutter sample data simultaneously satisfies the following condition:
containing clutter signals;
does not contain a target signal;
the clutter statistical characteristics are the same or similar;
noise is not correlated with clutter statistics;
the noise is statistically uncorrelated with each other and has the same variance.
3. The method of claim 2, wherein the raw radar pulse pressure data X is specifically:
X＝S+C+W
wherein S is a target matrix, C is a clutter matrix, W is a noise matrix, and X, S, C and W are both M multiplied by N matrices; m is the number of sampling points of a single pulse, and N represents the number of pulses.
4. The method of claim 3, wherein the clutter sample data is specifically represented as:
Figure FDA0002721915230000011
wherein the content of the first and second substances,
Figure FDA0002721915230000012
are all K multiplied by N matrixes, K is the number of samples,
Figure FDA0002721915230000013
is a matrix of sample clutter for the sample,
Figure FDA0002721915230000014
is a noise matrix of samples.
5. The method of claim 4, wherein the clutter sample data covariance matrix is specifically:
Figure FDA0002721915230000015
wherein the content of the first and second substances,
Figure FDA0002721915230000016
clutter covariance matrix, σ, of NxN2Is a noise variance, I is an identity matrix [ alpha ] having diagonal elements of 1 and the remaining elements of 0]HFor the conjugate transpose operator, R and I are both N matrices.
6. The orthogonal projection-based radar clutter adaptive suppression method of claim 5, wherein the clutter sample data covariance matrix
Figure FDA00027219152300000218
The eigenvalue decomposition of (a) is expressed as:
Figure FDA00027219152300000219
where R is the rank of R, i.e. R ═ rank (R), U ═ U1 u2… un… uN]TIs a matrix of NxN, sigma ═ diag (lambda)1λ2… λn… λN) Is an NxN matrix, scalar lambdanSum vector un(N is 1,2, …, N) is the eigenvalue of the clutter covariance matrix R and its corresponding eigenvector, vector unBeing an N × 1 matrix, diag () represents a diagonal matrix.
7. The method of claim 6, wherein the constructing the clutter subspace of the pulse pressure data from the eigenvectors corresponding to the eigenvalues specifically comprises:
order to
Figure FDA0002721915230000021
Then
Figure FDA0002721915230000022
Figure FDA0002721915230000023
Wherein the content of the first and second substances,
Figure FDA0002721915230000024
Figure FDA0002721915230000025
Figure FDA0002721915230000026
Figure FDA0002721915230000027
Figure FDA0002721915230000028
and
Figure FDA0002721915230000029
respectively, the first r large eigenvalues and the matrix formed by the eigenvectors corresponding to the (N-r) small eigenvalues, wherein,
Figure FDA00027219152300000210
is a matrix of N x r, and the matrix is a square matrix,
Figure FDA00027219152300000211
is a matrix of N (N-r),
Figure FDA00027219152300000212
are all in the form of a matrix of r x r,
Figure FDA00027219152300000213
is a matrix of (N-R) x (N-R), the large eigenvalue is the first R nonzero eigenvalues selected by sorting the eigenvalues of the clutter covariance matrix R from large to smallCharacteristic value lambda'iWherein i ═ 1,2, …, r, λ'1≥λ′2≥…≥λ′rThe remaining (N-r) eigenvalues are small eigenvalues;
the characteristic of characteristic value decomposition shows that U is unitary matrix and has the following properties
Figure FDA00027219152300000214
Clutter subspace of the structure
Figure FDA00027219152300000215
Is composed of
Figure FDA00027219152300000216
Wherein, the inner product of the matrix
Figure FDA00027219152300000217
8. The adaptive suppression method for radar clutter based on orthogonal projection according to claim 7, wherein the specific calculation formula of the noise subspace is:
Figure FDA0002721915230000031
。
9. the method for adaptively suppressing radar clutter based on orthogonal projection according to claim 8, wherein the pulse pressure data after clutter removal is specifically:
Figure FDA0002721915230000032
wherein, Y is an M multiplied by N matrix and represents the pulse pressure data after removing the noise.
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