CN111273237A - Strong interference suppression method based on spatial matrix filtering and interference cancellation - Google Patents

Strong interference suppression method based on spatial matrix filtering and interference cancellation Download PDF

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CN111273237A
CN111273237A CN201910457468.6A CN201910457468A CN111273237A CN 111273237 A CN111273237 A CN 111273237A CN 201910457468 A CN201910457468 A CN 201910457468A CN 111273237 A CN111273237 A CN 111273237A
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CN111273237B (en
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邹男
柳国龙
付进
梁国龙
齐滨
邱龙皓
王逸林
孙思博
李晨牧
李雄辉
张文琪
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Harbin Engineering University
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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 strong interference suppression method based on spatial domain matrix filtering and interference cancellation. The invention is mainly used for inhibiting strong interference outside and in the observation fan plane. Through subspace matrix filtering, the interference influence outside the observation sector is reduced, the strong interference direction in the observation sector is further obtained, the strong interference direction is applied to the design of a blocking matrix, and a new blocking matrix which does not reduce the data dimension is constructed. And processing the array received data through a blocking matrix and a spatial matrix filter, and finally performing azimuth estimation by adopting an MUSIC spectrum. The invention keeps the weak target information of the adjacent direction while inhibiting the strong interference in the observation sector, and realizes the estimation of the weak target direction under the condition of strong interference. The invention belongs to an underwater acoustic array signal processing method which can be applied to the fields of array signal processing, weak target azimuth detection and the like.

Description

Strong interference suppression method based on spatial matrix filtering and interference cancellation
Technical Field
The invention relates to the technical field of underwater acoustic engineering, in particular to a strong interference suppression method based on spatial domain matrix filtering and interference cancellation.
Background
The DOA estimation method based on array signal processing is widely applied to the fields of sonar, radar and the like, wherein the target azimuth estimation under the background of strong interference firstly suppresses the interference.
The spatial domain matrix filter is applied to DOA estimation, so that the interference and noise of a stop band region can be suppressed, and the azimuth estimation precision under a low signal-to-noise ratio is improved. However, when a strong disturbance is located within the observation sector relatively close to the target azimuth, the matrix spatial filter fails. Conventional blocking matrices may also be used to remove the effects of strong interference, but this technique requires that the orientation of the strong interference be known in advance and the blocking matrix dimensions be reduced.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a strong interference suppression method based on spatial domain matrix filtering and interference cancellation, and the invention provides the following technical scheme:
a strong interference suppression method based on spatial domain matrix filtering and interference cancellation comprises the following steps:
the method comprises the following steps: establishing an array element signal receiving model, setting a matrix filter pass band, and determining a strong interference direction in the pass band;
step two: constructing a blocking matrix according to the estimated strong interference direction, processing the data received by the blocking matrix, and removing the strong interference in a passband;
step three: removing the influence of the blocking matrix on the covariance matrix;
step four: and performing spatial filtering processing on the covariance matrix to obtain an MUSIC spatial spectrum.
Preferably, the first step is specifically:
the first step is as follows: establishing an array element signal receiving model, wherein the model is a uniform linear array consisting of N array elements, P far-field narrow-band plane wave signals are incident to the model from different directions, the spacing between the array elements is half wavelength of signal frequency, and the array element receiving signal is obtained by calculation and is represented by the following formula:
Figure BDA0002077041870000011
n(t)＝[n1(t),n2(t),...,nN(t)]T(2)
wherein x (t) is array element receiving signal si(t) is a source signal, P is the number of far-field narrow-band plane wave signals, a (theta)i) Is an array manifold vector of the ith source signal, n (t) is a noise vector [ ·]TFor transposition, N is the number of array elements;
the second step is that: setting incoherent signals and noise of the source signals, wherein the background noise of each array element is zero mean Gaussian white noise which is independent from each other, and establishing a covariance matrix of array received data, wherein the covariance matrix is represented by the following formula:
Figure BDA0002077041870000021
wherein R isxCovariance matrix for data received for the array, K representing total number of snapshots, K representing number of snapshots, piRepresenting the energy of the ith source signal, I being an identity matrix [ ·]HWhich represents the transpose of the conjugate,
Figure BDA0002077041870000022
representing the noise energy, λiAs a characteristic value, ηiRepresents the ith characteristic value and satisfies η1＞η2＞···＞ηN，uiIs ηiA corresponding feature vector;
the third step: setting the pass band of matrix filter as theta and stop band as
Figure BDA0002077041870000023
Filtering the received array signal data by adopting a spatial matrix filter to the strong interference outside the passband Θ so that the signal in the passband Θ passes through without distortion, wherein the expected response of the spatial matrix filter is represented by the following formula:
Figure BDA0002077041870000024
wherein T is a spatial matrix filter, a (theta) is a guide vector of a specified angle domain,
Figure BDA0002077041870000025
is an expected response;
the fourth step: setting a spatial matrix filter, carrying out constraint optimization on the passband response to minimize the maximum error of the stopband response, and obtaining the following formula according to the constraint optimization:
min r
Figure BDA0002077041870000026
wherein | · | purple sweet2Representing the 2-norm of the vector.
Figure BDA0002077041870000027
Is a passband response error constraint, r is a stopband constraint;
the fifth step: estimating a strong interference orientation within a pass band Θ, the strong interference orientation estimate within the pass band Θ being represented by:
Figure BDA0002077041870000028
UI＝[u1,u2,...,uP-1](7)
wherein the content of the first and second substances,
Figure BDA0002077041870000029
is a subspace azimuth spectrum, a (theta), within the pass band thetam) As a guide vector of the orientation within the pass band, thetam∈Θ，
Figure BDA00020770418700000210
An estimate of the steering vector for the strong interference azimuth within the pass band,
Figure BDA00020770418700000211
for estimating the orientation of strong interference within the pass band, UIIs RxStrong interference subspace.
Preferably, the second step is specifically:
the first step is as follows: according to estimated
Figure BDA0002077041870000031
Constructing a blocking matrix, which is represented by:
Figure BDA0002077041870000032
b is a blocking matrix of NxN, d is an array element interval, and lambda is the wavelength of a source signal;
the second step is that: the source signal is processed by a blocking matrix, and the processed source signal is represented by the following formula:
y(t)＝Bx(t) (9)
wherein, y (t) is the array data processed by the blocking matrix.
Preferably, the third step is specifically:
the first step is as follows: processing the covariance matrix using a blocking matrix, the processed covariance matrix being represented by:
Figure BDA0002077041870000033
wherein the content of the first and second substances,
Figure BDA0002077041870000034
the covariance matrix processed by the blocking matrix is adopted;
the second step is that: reducing the influence of the blocking matrix on Gaussian white noise in the covariance matrix, and expressing the covariance matrix reducing the influence of the blocking matrix by the following formula:
Figure BDA0002077041870000035
wherein the content of the first and second substances,
Figure BDA0002077041870000036
to reduce the covariance matrix affected by the blocking matrix,
Figure BDA0002077041870000037
is composed of
Figure BDA0002077041870000038
An estimate of (d).
Preferably, the
Figure BDA0002077041870000039
The estimation is performed by:
Figure BDA00020770418700000310
preferably, the fourth step is specifically:
the first step is as follows: processing the covariance matrix with a matrix filter, the processed covariance matrix being represented by:
Figure BDA00020770418700000311
wherein the content of the first and second substances,
Figure BDA00020770418700000312
is a covariance matrix, gamma, after matrix filter processingiRepresenting a characteristic value, eiIs and gammaiA corresponding feature vector (i ═ 1.., N);
the second step is that: solving the MUSIC spatial spectrum according to the following formula:
Figure BDA0002077041870000041
wherein, a (theta)k) In order to search for a guide vector,
Figure BDA0002077041870000042
Pmusicfor the spatial spectrum of MUSIC, En＝[eP,eP+1,...,eN]Is a noise space.
Preferably, the characteristic value γiSatisfy gamma1≥γ2≥···≥γP≥···γNAnd E iss＝[e1,e2,...,eP-1]，EsIs a covariance matrix
Figure BDA0002077041870000043
And after strong interference in the pass band is removed, a signal space containing the target position is obtained.
The invention has the following beneficial effects:
(1) under a plurality of strong interferences, the method of the invention can not only inhibit the strong interference outside the observation sector, but also inhibit the strong interference near the target direction in the observation sector.
(2) Compared with the traditional blocking matrix, the invention constructs a new blocking matrix without reducing dimension and more completely retains the target information.
(3) Compared with the traditional DOA estimation method, the method has the advantage that the weak target DOA estimation capability is improved after strong interference is suppressed.
Drawings
Fig. 1 is a flow chart of a strong interference suppression algorithm based on spatial matrix filtering and blocking matrix processing.
Figure 2 is the MUSIC spectrum estimation result under strong interference.
Fig. 3 shows the target orientation estimation result under strong interference.
Detailed Description
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
as shown in fig. 1, the present invention provides a strong interference suppression method based on spatial matrix filtering and interference cancellation, which includes the following steps:
the method comprises the following steps: establishing an array element signal receiving model, setting a matrix filter pass band, and determining a strong interference direction in the pass band;
step two: constructing a blocking matrix according to the estimated strong interference direction, processing the data received by the blocking matrix, and removing the strong interference in a passband;
step three: removing the influence of the blocking matrix on the covariance matrix;
step four: and performing spatial filtering processing on the covariance matrix to obtain an MUSIC spatial spectrum.
The method comprises the following steps: and determining the orientation of the strong interference in the passband.
Firstly, an array signal receiving model is established, for a uniform linear array consisting of N array elements, P far-field narrow-band plane wave signals are supposed to be incident to the array from different directions, the distance between the array elements is half wavelength of signal frequency, and the array receiving signal can be expressed as
Figure BDA0002077041870000051
Wherein s isiAnd (t) is a source signal which comprises P-1 strong interference signals and 1 target signal. The present invention assumes: one interference is positioned in the observation sector of the target, the other interference is positioned outside the observation sector, and the interference intensity is higher than that of the target. The array manifold vector of the ith source signal is a (theta)i) The noise vector is n (t) ═ n1(t),n2(t),...,nN(t)]TRepresenting the background complex noise received by each array element [. degree]TIndicating transposition. It is assumed that the source signals and the noise are not correlated, the background noise of each array element is zero mean Gaussian white noise which is independent from each other, and the variance is
Figure BDA0002077041870000052
The covariance matrix of the array received data is expressed as
Figure BDA0002077041870000053
Wherein p isiRepresents the energy of the ith source signal, K represents the total number of snapshots, K represents the number of snapshots,
Figure BDA0002077041870000054
representing the noise energy, I is the identity matrix ηiIs a characteristic value, uiIs ηiCorresponding feature vector, the feature value satisfying η1≥η2≥···≥ηP≥···ηNHere it is assumed that the source number is known. U shapeI＝[u1,u2,...,uP-1]Is a covariance matrix RxA subspace of medium-strong interferences.
Defining the pass band of the matrix filter as theta and the stop band as
Figure BDA0002077041870000055
For strong interference outside the passband, the received array signal data may be filtered using a spatial matrix filter T that passes the signals in the passband without distortion, the filter having an expected response of
Figure BDA0002077041870000056
Wherein a (theta) is a guide vector of a specified angle domain,
Figure BDA0002077041870000057
t is the spatial matrix filter for the expected response.
Designing a pass band response constraint stopband response error maximum minimization spatial matrix filter, carrying out constraint optimization on the pass band response to minimize the stopband response maximum error, and obtaining the following formula according to the constraint optimization:
min r
Figure BDA0002077041870000058
wherein I is a unit matrix, | · | | non-woven phosphor2Representing the 2-norm of the vector.
Figure BDA0002077041870000061
The pass band response error constraint is to protect the signal characteristic components between the interested regions, and r is the stop band constraint is to filter the interference characteristic components outside the interested regions. Equation (4) is a convex optimization problem that can be solved using the interior point method. T of equation 4 is used to solve the orientation of equation 5
The estimate of the orientation of the strong interference within the pass band Θ can be derived by
Figure BDA0002077041870000062
Wherein the content of the first and second substances,
Figure BDA0002077041870000063
is a subspace azimuth spectrum, a (theta), within the pass band thetam) As a guide vector of the orientation within the pass band, thetam∈Θ，
Figure BDA0002077041870000064
For the steering vector of the strong interference azimuth within the estimated pass band,
Figure BDA0002077041870000065
is the estimated location of the strong interference within the pass band.
Step two: and constructing a blocking matrix to process the data.
Firstly, estimating the strong interference orientation in the pass band according to the previous step
Figure BDA0002077041870000066
The blocking matrix is constructed as follows
Figure BDA0002077041870000067
Where B is an N matrix,
Figure BDA0002077041870000068
the azimuth angle of strong interference in the passband is calculated in the foregoing, d is the array element spacing, and λ is the wavelength of the source signal.
The array data after the blocking matrix processing can then be represented as
y(t)＝Bx(t)
Step three: and removing the influence of the introduced blocking matrix on the covariance matrix.
First, the covariance matrix after the blocking matrix processing is expressed as
Figure BDA0002077041870000069
While
Figure BDA00020770418700000610
The estimation can be performed by the following formula
Figure BDA00020770418700000611
Then, the influence of the blocking matrix on white gaussian noise in the covariance matrix is reduced, and the covariance matrix with the effect of the blocking matrix reduced is expressed by the following formula
Figure BDA00020770418700000612
Step four: and performing spatial filtering processing on the covariance matrix to obtain an MUSIC spatial spectrum.
First, the covariance matrix after matrix filter processing is
Figure BDA0002077041870000071
Wherein, γiRepresenting a characteristic value, eiIs and gammaiA corresponding feature vector (i ═ 1.. An., N), and the feature values satisfy a relationship γ1≥γ2≥···≥γP≥···γNThen E iss＝[e1,e2,...,eP-1]Is a covariance matrix
Figure BDA0002077041870000072
After removing strong interference in the pass band, a signal space containing the target position, En＝[eP,eP+1,...,eN]Is a noise space.
Then, the spatial spectrum is solved according to the following formula
Figure BDA0002077041870000073
Where a (θ) is a search steering vector, PmusicIs the MUSIC spatial spectrum. Finally, the position of the weak target under strong interference can be estimated through the formula (12).
The second embodiment is as follows:
the strong interference suppression algorithm based on spatial domain matrix filtering and blocking matrix processing designed by the invention is verified by adopting simulation data, and the result is explained.
The simulation experiment adopts a uniform linear array composed of 16 array elements, the half wavelength of signal frequency is used as the interval of the array elements, the interested area is set as theta [ -20 DEG, 20 DEG ], the arrival direction of the signal is-1 DEG, the signal-to-noise ratio is 0dB, the other 3 uncorrelated strong interferences enter the basic array from the directions of-35 DEG, 1 DEG and 30 DEG, the dry-to-noise ratios are respectively set as 20dB, 40dB and 30dB, wherein the strong interference at 1 DEG is in the pass band range, the other two strong interferences are in the stop band, and the fast beat number is 500.
Fig. 2 is a MUSIC spatial spectrum under strong interference, and fig. 3 is a spatial spectrum of the algorithm proposed by the present invention.
As can be seen from fig. 2, the MUSIC spectrum can estimate strong interference orientations at-35 °, 1 ° and 30 °, but the weak target orientation of-1 ° is difficult to estimate. As can be seen from FIG. 3, the method of the present invention suppresses strong interference outside the region of interest and strong interference within the region of interest, and the estimated target position is consistent with the actual target position.
The above description is only a preferred embodiment of the strong interference suppression method based on spatial domain matrix filtering and interference cancellation, and the protection range of the strong interference suppression method based on spatial domain matrix filtering and interference cancellation is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection range of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.

Claims (7)

1. A strong interference suppression method based on spatial domain matrix filtering and interference cancellation is characterized in that: the method comprises the following steps:
the method comprises the following steps: establishing an array element signal receiving model, setting a matrix filter pass band, and determining a strong interference direction in the pass band;
step two: constructing a blocking matrix according to the estimated strong interference direction, processing the data received by the blocking matrix, and removing the strong interference in a passband;
step three: removing the influence of the blocking matrix on the covariance matrix;
step four: and performing spatial filtering processing on the covariance matrix to obtain an MUSIC spatial spectrum.
2. The method of claim 1, wherein the method comprises: the first step is specifically as follows:
the first step is as follows: establishing an array element signal receiving model, wherein the model is a uniform linear array consisting of N array elements, P far-field narrow-band plane wave signals are incident to the model from different directions, the spacing between the array elements is half wavelength of signal frequency, and the array element receiving signal is obtained by calculation and is represented by the following formula:
Figure FDA0002077041860000011
n(t)＝[n1(t),n2(t),...,nN(t)]T(2)
wherein x (t) is array element receiving signal si(t) is a source signal, P is the number of far-field narrow-band plane wave signals, a (theta)i) Is an array manifold vector of the ith source signal, n (t) is a noise vector [ ·]TFor transposition, N is the number of array elements;
the second step is that: setting incoherent signals and noise of the source signals, wherein the background noise of each array element is zero mean Gaussian white noise which is independent from each other, and establishing a covariance matrix of array received data, wherein the covariance matrix is represented by the following formula:
Figure FDA0002077041860000012
wherein R isxCovariance matrix for data received for the array, K representing total number of snapshots, K representing number of snapshots, piRepresenting the energy of the ith source signal, I being an identity matrix [ ·]HWhich represents the transpose of the conjugate,
Figure FDA0002077041860000013
representing the noise energy, λiAs a characteristic value, ηiRepresents the ith characteristic value and satisfies η1＞η2＞···＞ηN，uiIs ηiA corresponding feature vector;
the third step: setting the pass band of matrix filter as theta and stop band as
Figure FDA0002077041860000014
Filtering the received array signal data by adopting a spatial matrix filter to the strong interference outside the passband theta so that the signal in the passband theta passes through without distortion, wherein the spatial matrix filter is expected to respond under the passing conditionThe formula is as follows:
Figure FDA0002077041860000021
wherein T is a spatial matrix filter, a (theta) is a guide vector of a specified angle domain,
Figure FDA0002077041860000022
is an expected response;
the fourth step: setting a spatial matrix filter, carrying out constraint optimization on the passband response to minimize the maximum error of the stopband response, and obtaining the following formula according to the constraint optimization:
Figure FDA0002077041860000023
wherein | · | purple sweet2Representing the 2-norm of the vector.
Figure FDA0002077041860000024
Is a passband response error constraint, r is a stopband constraint;
the fifth step: estimating a strong interference orientation within a pass band Θ, the strong interference orientation estimate within the pass band Θ being represented by:
Figure FDA0002077041860000025
UI＝[u1,u2,...,uP-1](7)
wherein the content of the first and second substances,
Figure FDA0002077041860000026
is a subspace azimuth spectrum, a (theta), within the pass band thetam) As a guide vector of the orientation within the pass band, thetam∈Θ，
Figure FDA0002077041860000027
An estimate of the steering vector for the strong interference azimuth within the pass band,
Figure FDA0002077041860000028
for estimating the orientation of strong interference within the pass band, UIIs RxStrong interference subspace.
3. The method of claim 2, wherein the method comprises: the second step is specifically as follows:
the first step is as follows: according to estimated
Figure FDA0002077041860000029
Constructing a blocking matrix, which is represented by:
Figure FDA00020770418600000210
b is a blocking matrix of NxN, d is an array element interval, and lambda is the wavelength of a source signal;
the second step is that: the source signal is processed by a blocking matrix, and the processed source signal is represented by the following formula:
y(t)＝Bx(t) (9)
wherein, y (t) is the array data processed by the blocking matrix.
4. The method of claim 3, wherein the method comprises: the third step is specifically as follows:
the first step is as follows: processing the covariance matrix using a blocking matrix, the processed covariance matrix being represented by:
Figure FDA0002077041860000031
wherein the content of the first and second substances,
Figure FDA0002077041860000032
to adopt a blocking matrixA physical covariance matrix;
the second step is that: reducing the influence of the blocking matrix on Gaussian white noise in the covariance matrix, and expressing the covariance matrix reducing the influence of the blocking matrix by the following formula:
Figure FDA0002077041860000033
wherein the content of the first and second substances,
Figure FDA0002077041860000034
to reduce the covariance matrix affected by the blocking matrix,
Figure FDA0002077041860000035
is composed of
Figure FDA0002077041860000036
An estimate of (d).
5. The method of claim 4, wherein the method comprises: the above-mentioned
Figure FDA0002077041860000037
The estimation is performed by:
Figure FDA0002077041860000038
6. the method of claim 1, wherein the method comprises: the fourth step is specifically as follows:
the first step is as follows: processing the covariance matrix with a matrix filter, the processed covariance matrix being represented by:
Figure FDA0002077041860000039
wherein the content of the first and second substances,
Figure FDA00020770418600000310
is a covariance matrix, gamma, after matrix filter processingiRepresenting a characteristic value, eiIs and gammaiA corresponding feature vector (i ═ 1.., N);
the second step is that: solving the MUSIC spatial spectrum according to the following formula:
Figure FDA00020770418600000311
wherein, a (theta)k) In order to search for a guide vector,
Figure FDA00020770418600000312
Pmusicfor the spatial spectrum of MUSIC, En＝[eP,eP+1,...,eN]Is a noise space.
7. The method of claim 6, wherein the method comprises: characteristic value gammaiSatisfy gamma1≥γ2≥···≥γP≥···γNAnd E iss＝[e1,e2,...,eP-1]，EsIs a covariance matrix
Figure FDA0002077041860000041
And after strong interference in the pass band is removed, a signal space containing the target position is obtained.
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