CN111427014A - Adaptive signal processing realization method based on Gaussian elimination - Google Patents

Adaptive signal processing realization method based on Gaussian elimination Download PDF

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CN111427014A
CN111427014A CN202010195203.6A CN202010195203A CN111427014A CN 111427014 A CN111427014 A CN 111427014A CN 202010195203 A CN202010195203 A CN 202010195203A CN 111427014 A CN111427014 A CN 111427014A
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CN111427014B (en
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颜志升
郑昱
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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
    • G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
    • 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

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Abstract

The application discloses a method for realizing self-adaptive signal processing based on Gaussian elimination, which comprises the following steps: generating a corresponding echo vector according to the echo signal; calculating an autocorrelation matrix of an echo vector, calculating a generalized inner product value of a signal by adopting a Gaussian elimination method, screening the echo vector according to the generalized inner product value to generate a screening vector, calculating an autocorrelation matrix of the screening vector, and calculating a weighting coefficient by adopting the Gaussian elimination method; carrying out weighted summation on the echo vector, carrying out constant false alarm rate detection on the weighted summation result of any range gate, and calculating the self-adaptive correlation estimation value of the echo signal by adopting a Gaussian elimination method when the signal-to-noise ratio of the range gate is judged to be greater than a first preset threshold value; and when the self-adaptive correlation estimated value of the echo signal is larger than a second preset threshold value, judging that the target corresponding to the range gate exists. According to the technical scheme, the process of calculating the inverse matrix is avoided through a Gaussian elimination method, so that the realization efficiency of the self-adaptive signal processing algorithm is improved.

Description

Adaptive signal processing realization method based on Gaussian elimination
Technical Field
The application relates to the technical field of radar signal processing, in particular to a method for realizing adaptive signal processing based on Gaussian elimination.
Background
Because interference may exist in a working environment, a radar system may receive an interference signal and a target echo signal at the same Time, so that the interference signal needs to be suppressed, and the authenticity of the target needs to be determined, and the echo signal is usually processed by using Adaptive Digital Beamforming (ADBF), Space-Time Adaptive processing (STAP) and other Adaptive signal processing algorithms, where calculating a Generalized Inner-Product (GIP), an optimal weighting coefficient, and an Adaptive Correlation Estimation (ACE) is a core content of the algorithms.
However, in the prior art, a strategy of directly calculating an inverse matrix of an autocorrelation matrix is usually adopted, and the method for calculating the inverse matrix has large calculation amount and long time consumption, and seriously influences the realization efficiency of adaptive signal processing algorithms such as ADBF and STAP.
Disclosure of Invention
The purpose of this application lies in: the method solves the equation set by the Gaussian elimination method to avoid the process of calculating the inverse matrix, thereby improving the realization efficiency of the adaptive signal processing algorithm.
The technical scheme of the first aspect of the application is as follows: a method for realizing adaptive signal processing based on Gaussian elimination is provided, which comprises the following steps: step 1, acquiring echo signals of M channels of any range gate in a radar system, and generating corresponding echo vectors according to the echo signals; step 2, calculating an autocorrelation matrix according to the echo vector, namely a first autocorrelation matrix, calculating a generalized inner product value of the echo vector by adopting a Gaussian elimination method, screening the echo vector according to the generalized inner product value to generate a screening vector, calculating an autocorrelation matrix according to the screening vector, namely a second autocorrelation matrix, and calculating a weighting coefficient of the echo signal by adopting the Gaussian elimination method; step 3, carrying out weighted summation on the echo signals by using the weighting coefficients, carrying out constant false alarm rate detection on the weighted summation result of any range gate, and calculating the self-adaptive correlation estimation value of the echo signals corresponding to the range gate by adopting a Gaussian elimination method when the signal-to-noise ratio of the range gate is judged to be greater than a first preset threshold value; and 4, judging that the target corresponding to the range gate exists when the self-adaptive correlation estimated value of the echo signal corresponding to the range gate is larger than a second preset threshold.
In any one of the above technical solutions, further, step 2 specifically includes: step 21, calculating a first autocorrelation matrix according to the echo vector, and decomposing the first autocorrelation matrix into a first lower triangular matrix and a first upper triangular matrix; step 22, calculating a generalized inner product value of the echo vector according to the echo vector, the first lower triangular matrix and the first upper triangular matrix, wherein a calculation formula of the generalized inner product value is as follows:
Figure BDA0002417350820000021
U1α1＝β1
L1β1＝xn
Figure BDA0002417350820000022
wherein GIP is a generalized inner product value, xnFor the echo vector, N is the number of the range gate, N is 1,2, …, N, M is the number of the channel, M is 1,2, …, M, (·)HFor conjugate transpose operator, L1Is a first lower triangular matrix, U1Is a first upper triangular matrix;
step 23, filtering the echo vector according to the magnitude of the generalized inner product value and a preset rejection ratio to generate a filtered vector, calculating a second autocorrelation matrix according to the filtered vector, decomposing the second autocorrelation matrix into a second lower triangular matrix and a second upper triangular matrix, and calculating a weighting coefficient of the echo signal, wherein the calculation formula of the weighting coefficient is as follows:
Figure BDA0002417350820000023
U2α2＝β2
L2β2＝s
in the formula, woptS is a steering vector for the weighting factor, and has M components representing the ideal echo signal, L2Is a second lower triangular matrix, U2Is as followsAnd two upper triangular matrixes.
In any of the above technical solutions, further, in step 3, calculating an adaptive correlation estimation value of the echo signal corresponding to the range gate by using a gaussian elimination method specifically includes calculating an adaptive correlation estimation value according to the steering vector s and the second lower triangular matrix L2And a second upper triangular matrix U2Calculating the echo signal xnThe adaptive correlation estimation value of (1), wherein the calculation formula of the adaptive correlation estimation value is:
Figure BDA0002417350820000031
U2α3＝β3
L2β3＝xn
in the formula, ACE is an adaptive correlation estimation value.
In any one of the foregoing technical solutions, further, in step 23, the echo vector is filtered according to the size of the generalized inner product value and a preset rejection ratio, so as to generate a filtered vector, which specifically includes: sorting the elements in the echo vector according to the magnitude of the generalized inner product value, removing the elements at two ends in the sorted echo vector according to a preset removing proportion, and recording the echo vector after the elements are removed as a screening vector, wherein the preset removing proportion is 10% -20%.
The technical scheme of the second aspect of the application is as follows: the radar system comprises a target judging unit, wherein the radar system transmits an echo vector to the target judging unit after receiving the echo vector, and the target judging unit is used for judging whether a target corresponding to the echo signal exists by adopting the method for realizing the adaptive signal processing based on the Gaussian elimination element in any one of the technical schemes in the first aspect.
The beneficial effect of this application is:
according to the technical scheme, a Gaussian elimination method is used for replacing a method for solving an inverse matrix in the prior art, the product of the inverse matrix and a vector is regarded as the solution of a linear equation set, and then the solution of the equation set is solved by the Gaussian elimination method, so that a large number of intermediate operation links related to the inverse matrix in the prior art are avoided. Through analysis and comparison, the calculation amount is reduced by about 80% under the premise of the same discrimination effect.
In the method, the generalized inner product of the echo vector is calculated by adopting a Gaussian elimination method, a triangular matrix decomposition is carried out on a first autocorrelation matrix calculated according to the echo vector, and a triangular matrix equation set is solved by utilizing a forward substitution method and a reverse substitution method to realize the calculation of the generalized inner product value of the echo vector; and then screening is carried out according to the generalized inner product value, a second autocorrelation matrix is calculated according to the screening vector, the optimal weighting coefficient is obtained by adopting the Gaussian elimination method, further, weighting summation is carried out on the echo vector, constant false alarm rate detection is carried out, and finally, the adaptive correlation estimation value of the signal is calculated. The whole process of the technical scheme in the application avoids the process of calculating the inverse matrix in the prior art, so that the realization efficiency of the self-adaptive signal processing algorithm is improved.
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The advantages of the above and/or additional aspects of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart diagram of a method for implementing adaptive signal processing based on Gaussian elimination according to one embodiment of the present application;
FIG. 2 is a diagram comparing two methods of a GIP value calculating process in the prior art and a GIP value calculating process in the embodiment of the present application;
FIG. 3 is a diagram of a percentage reduction of a coefficient matrix by Gaussian elimination with respect to a calculated amount to calculate an inverse matrix of the coefficient matrix, according to an embodiment of the present application;
FIG. 4 is a diagram of a matrix dimension correspondence with a computation effort for decomposing a coefficient matrix and computing an inverse matrix of the coefficient matrix by Gaussian elimination, according to an embodiment of the present application;
FIG. 5 is a schematic illustration of the percentage reduction of the system of equations solving for which both coefficient matrices are triangular moments relative to the calculated quantity of the product of the inverse matrix of the autocorrelation matrix and the vector according to one embodiment of the present application;
FIG. 6 is a schematic diagram of solving a system of equations in which both coefficient matrices are triangular moments and calculating a matrix dimension correspondence of a calculated quantity of an inverse matrix and a vector product of an autocorrelation matrix, according to an embodiment of the present application;
FIG. 7 is a diagram of an optimal weighting factor and ACE value calculation process according to one embodiment of the present application;
FIG. 8 is a schematic diagram of the amplitude comparison of target signals after ADBF performed by both Gaussian elimination and inverse matrix computation according to an embodiment of the present application;
fig. 9 is a schematic diagram of target signal-to-noise ratio comparison after ADBF is achieved by both gaussian elimination and inverse matrix calculation according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, the present application will be described in further detail with reference to the accompanying drawings and detailed description. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited by the specific embodiments disclosed below.
As shown in fig. 1, the present embodiment provides a method for implementing adaptive signal processing based on gaussian elimination, including:
step 1, obtaining echo signals of M channels of any range gate in a radar system, and generating corresponding echo vectors according to the echo signals, wherein the calculation formula of the echo vectors is as follows:
Figure BDA0002417350820000051
in the formula, xnN is the number of the range gate, N is 1,2, …, N, m is the number of the channel,m＝1,2,…,M，xn,mrepresenting the signal component in the mth channel on the nth range gate.
In the prior art, the echo vector x is obtainednThen, the echo vector x is measurednAs a primary sample set, an autocorrelation matrix is calculated, and a GIP value is calculated in the form of a calculation inverse matrix, as shown in fig. 2 (a). And screening by using the GIP value to obtain a screened sample, and recalculating the autocorrelation matrix and the corresponding weighting coefficient.
Step 2, calculating an autocorrelation matrix according to the echo vector, recording the autocorrelation matrix as a first autocorrelation matrix, calculating a generalized inner product value GIP of the echo vector by adopting a Gaussian elimination method, screening the echo vector according to the generalized inner product value GIP to generate a screening vector, calculating the autocorrelation matrix according to the screening vector, recording the autocorrelation matrix as a second autocorrelation matrix, and calculating a weighting coefficient of the echo signal by adopting the Gaussian elimination method;
further, as shown in fig. 2(b), in this embodiment, in order to reduce the calculation amount of the inverse matrix in the calculation process, a gaussian elimination method is used to decompose the autocorrelation matrix, and the step 2 specifically includes:
step 21, calculating a first autocorrelation matrix according to the echo vector, and decomposing the first autocorrelation matrix into a first lower triangular matrix and a first upper triangular matrix;
specifically, the calculation formula for calculating the first autocorrelation matrix according to the echo vector is as follows:
Figure BDA0002417350820000061
in the formula, R1Is a first autocorrelation matrix, xnIs the echo vector, and N is the number of the echo vectors.
The first autocorrelation matrix R1Decomposed into a first lower triangular matrix L1And a first upper triangular matrix U1And then stored.
Step 22, calculating a generalized inner product value of the echo vector according to the echo vector, the first lower triangular matrix and the first upper triangular matrix, wherein a calculation formula of the generalized inner product value is as follows:
Figure BDA0002417350820000062
U1α1＝β1
L1β1＝xn
Figure BDA0002417350820000063
wherein GIP is a generalized inner product value, xnFor the echo vector, N is the number of the range gate, N is 1,2, …, N, M is the number of the channel, M is 1,2, …, M, (·)HFor conjugate transpose operator, L1Is a first lower triangular matrix, U1Is a first upper triangular matrix;
in particular, for the echo vector xnSolving the following formula by a forward substitution method:
L1β1＝xn
then, the following formula is solved by a reverse substitution method:
U1α1＝β1
then the echo vector x is processednAnd α1And performing inner product operation to obtain a generalized inner product value GIP, wherein the corresponding calculation formula is as follows:
Figure BDA0002417350820000071
taking complex number operation as an example, the calculation amount involved in the GIP value calculation process in the present embodiment is counted, and compared with the method of directly calculating the inverse matrix.
And decomposing the complex operation into addition/subtraction/multiplication/division operation of real numbers, and finally evaluating the advantages and disadvantages of two technologies of an inverse matrix and a solution equation set through the addition/subtraction/multiplication/division operation quantity of the real numbers. For fairness, the process of decomposing complex operations into real operations is specified in both techniques as follows:
(1) the addition of complex numbers a +1i b and c +1i d is divided into two real number additions: a + c and b + d;
(2) the multiplication of complex numbers a +1i b and c +1i d is divided into four real multiplications and one addition and subtraction, a c-b d and a d + b c.
It should be noted that, in the following description,
Figure BDA0002417350820000072
the matrix inversion function inv () in itself is implemented using a method based on the claus base (Cholesky) decomposition. The statistics and analysis were performed on the matrices of different dimensions, and the results are shown in tables 1 to 3.
TABLE 1
Figure BDA0002417350820000073
TABLE 2
Figure BDA0002417350820000081
TABLE 3
Figure BDA0002417350820000082
The calculated quantities of the four operations of addition/subtraction/multiplication/division in the different dimensions are summed up, and then the calculated quantities and the values are compared, compared with the calculation of an inverse matrix, the method of decomposing the coefficient matrix by the gaussian elimination method in the embodiment is adopted, the reduction range of the calculated quantities is about 80%, the reduction ranges of the calculated quantities in the different dimensions are shown in fig. 3, and the relationship between the calculated quantities of the two methods and the dimensions of the matrix is shown in fig. 4.
Step 23, filtering the echo vector according to the magnitude of the generalized inner product value and a preset rejection ratio to generate a filtered vector, calculating a second autocorrelation matrix according to the filtered vector, decomposing the second autocorrelation matrix into a second lower triangular matrix and a second upper triangular matrix, and calculating a weighting coefficient of the echo signal, wherein the calculation formula of the weighting coefficient is as follows:
Figure BDA0002417350820000083
U2α2＝β2
L2β2＝s
in the formula, woptS is a steering vector for the weighting factor, and has M components representing the ideal echo signal, L2Is a second lower triangular matrix, U2Is a second upper triangular matrix.
Further, in step 23, the echo vector is filtered according to the size of the generalized inner product value and a preset rejection ratio to generate a filtered vector, which specifically includes: sorting the elements in the echo vector according to the magnitude of the generalized inner product value, removing the elements at two ends in the sorted echo vector according to a preset removing proportion, and recording the echo vector after the elements are removed as a screening vector, wherein the preset removing proportion is 10% -20%.
Specifically, by the above calculation, each echo vector xnAll correspond to a GIP value, and the echo vector x is measured by the GIP valuenScreening is performed, in general, the probability that a target (such as an aircraft or a ship) exists on a range gate with a large GIP value is high, and an echo signal of the range gate with a small GIP value may correspond to thermal noise, so that echo vectors with a medium GIP value are retained, that is, 10% -20% of elements at two ends of the sorted echo vectors are removed, the retained elements are regarded as screening vectors, and a second autocorrelation matrix is calculated according to the screening vectors, wherein a corresponding calculation formula is as follows:
Figure BDA0002417350820000091
in the formula, R2Is a first autocorrelation matrix, x'nFor the filter vectors, N is 1,2, …, N ', and N' is the number of filter vectors.
Similarly, a second autocorrelation matrix R2Decomposing to obtain a second lower triangular matrix L2And a second upper triangular matrix U2And then stored.
And combining the guide vector s, and solving the following formula by a forward substitution method:
L2β2＝s
then, the following formula is solved by a reverse substitution method:
U2α2＝β2
calculating a weighting coefficient corresponding to the echo signal:
Figure BDA0002417350820000092
in the prior art, the calculation formula of the weighting coefficient is as follows:
Figure BDA0002417350820000101
still taking complex numbers as an example, the complex number operation is decomposed into real number addition/subtraction/multiplication/division operations, and the calculated amount of the numerator in different dimensions in the present embodiment and the prior art is counted as shown in tables 4 to 6.
TABLE 4
Figure BDA0002417350820000102
TABLE 5
Figure BDA0002417350820000103
TABLE 6
Figure BDA0002417350820000104
Summing up the calculated quantities of the four operations of addition/subtraction/multiplication/division in different dimensions, and comparing the calculated quantities with values, wherein the difference of the calculated quantities is small, the reduction range of the calculated quantities in different dimensions is shown in fig. 5, and the relationship between the calculated quantities of the two methods and the matrix dimensions is shown in fig. 6.
Through the statistics of the calculated amount twice, the Gaussian elimination method is adopted to avoid calculating the inverse matrix in the embodiment, the calculated amount in the self-adaptive signal processing process is greatly reduced, specifically, the autocorrelation matrix is decomposed into a lower triangular matrix and an upper triangular matrix, compared with the calculation of the inverse matrix of the autocorrelation matrix, the decomposition process can reduce the calculated amount by about 80%, and the forward substitution method and the reverse substitution method are used for solving an equation set with two coefficient matrixes being three solution matrixes, and compared with the calculation of the product of the inverse matrix and the vector, the calculated amount is basically equivalent.
Step 3, carrying out weighted summation on the echo signals by using the weighting coefficients, carrying out Constant False Alarm Rate (CFAR) detection on the weighted summation result of any range gate, and calculating the self-adaptive correlation estimation value of the echo signals corresponding to the range gate by adopting a Gaussian elimination method when the signal-to-noise ratio of the range gate is judged to be greater than a first preset threshold value;
specifically, the echo signals are weighted and summed by using the weighting coefficients to obtain corresponding signal amplitudes, then the CFAR detection background is calculated by an averaging method, and the ratio of the signal amplitudes to the detection background is recorded as the signal-to-noise ratio.
And when the signal-to-noise ratio is greater than a first preset threshold value, calculating a corresponding self-adaptive correlation estimated value.
Further, in step 3, calculating an adaptive correlation estimation value of the echo signal corresponding to the range gate by using a gaussian elimination method specifically includes:
based on the steering vector s, the second lower triangular matrix L2And a second upper triangular matrix U2Calculating the echo signal xnThe adaptive correlation estimation value of (1), wherein the calculation formula of the adaptive correlation estimation value is:
Figure BDA0002417350820000111
U2α3＝β3
L2β3＝xn
in the formula, ACE is an adaptive correlation estimation value.
Specifically, as shown in fig. 7, after CFAR detection, for the range gate that determines that there is a possible target, the following formula is solved by forward substitution:
L2β3＝xn
then, the following formula is solved by a reverse substitution method:
U2α3＝β3
transposed matrix sHAnd α3The square of the inner product of (a) is taken as the numerator of ACE, and the denominator is sHAnd α3Inner product of (2) and transposed matrix
Figure BDA0002417350820000121
And α3And then calculating the adaptive correlation estimation value ACE:
Figure BDA0002417350820000122
in the prior art, the calculation formula of ACE is as follows:
Figure BDA0002417350820000123
and judging whether a target corresponding to the echo signal of the range gate exists or not by using the calculated adaptive correlation estimation value ACE, wherein the process is the prior art and is not repeated.
And 4, judging that the target corresponding to the range gate exists when the self-adaptive correlation estimated value of the echo signal corresponding to the range gate is larger than a second preset threshold.
In order to verify the correctness of the implementation method in the embodiment, taking ADBF adaptive signal processing as an example, a method of directly calculating an inverse matrix in the prior art is used as a verification method, and a target signal amplitude and a target signal-to-noise ratio are compared. When the ADBF adaptive signal processing is realized by using the two methods, the double-precision floating point number is adopted, and the verification results are shown in fig. 8 and 9. As can be seen from data analysis, the ADBF implementation method in the present embodiment has the same effect as the method of directly calculating the inverse matrix.
The technical solution of the present application is described in detail above with reference to the accompanying drawings, and the present application provides a method for implementing adaptive signal processing based on gaussian elimination, including:
step 1, acquiring echo signals of M channels of any range gate in a radar system, and generating corresponding echo vectors according to the echo signals;
step 2, calculating a first autocorrelation matrix according to the echo vector, calculating a generalized inner product value of the signal echo vector by adopting a Gaussian elimination method, screening the echo vector according to the generalized inner product value to generate a screening vector, calculating a second autocorrelation matrix according to the screening vector, and calculating a weighting coefficient of the echo signal by adopting the Gaussian elimination method;
step 3, carrying out weighted summation on the echo signals by using the weighting coefficients, carrying out constant false alarm rate detection on the weighted summation result of any range gate, and calculating the self-adaptive correlation estimation value of the echo signals corresponding to the range gate by adopting a Gaussian elimination method when the signal-to-noise ratio of the range gate is judged to be greater than a first preset threshold value;
and 4, judging that the target corresponding to the range gate exists when the self-adaptive correlation estimated value of the echo signal corresponding to the range gate is larger than a second preset threshold.
According to the technical scheme, the process of calculating the inverse matrix is avoided through a Gaussian elimination method, so that the realization efficiency of the self-adaptive signal processing algorithm is improved.
The steps in the present application may be sequentially adjusted, combined, and subtracted according to actual requirements.
The units in the device can be merged, divided and deleted according to actual requirements.
Although the present application has been disclosed in detail with reference to the accompanying drawings, it is to be understood that such description is merely illustrative and not restrictive of the application of the present application. The scope of the present application is defined by the appended claims and may include various modifications, adaptations, and equivalents of the invention without departing from the scope and spirit of the application.

Claims (5)

1. The method for realizing the self-adaptive signal processing based on the Gaussian elimination is characterized by comprising the following steps:
step 1, acquiring echo signals of M channels of any range gate in a radar system, and generating corresponding echo vectors according to the echo signals;
step 2, calculating a first autocorrelation matrix according to the echo vector, calculating a generalized inner product value of the echo vector by adopting a Gaussian elimination method, screening the echo vector according to the generalized inner product value to generate a screening vector, calculating a second autocorrelation matrix according to the screening vector, and calculating a weighting coefficient of the echo signal by adopting the Gaussian elimination method;
step 3, carrying out weighted summation on the echo signals by using the weighting coefficients, carrying out constant false alarm rate detection on the weighted summation result of any range gate, and calculating the self-adaptive correlation estimation value of the echo signals corresponding to the range gate by adopting a Gaussian elimination method when the signal-to-noise ratio of the range gate is judged to be greater than a first preset threshold value;
and 4, judging that the target corresponding to the range gate exists when the self-adaptive correlation estimated value of the echo signal corresponding to the range gate is larger than a second preset threshold value.
2. The method for implementing adaptive signal processing based on gaussian elimination according to claim 1, wherein said step 2 specifically comprises:
step 21, calculating the first autocorrelation matrix according to the echo vector, and decomposing the first autocorrelation matrix into a first lower triangular matrix and a first upper triangular matrix;
step 22, calculating a generalized inner product value of the echo vector according to the echo vector, the first lower triangular matrix and the first upper triangular matrix, wherein a calculation formula of the generalized inner product value is as follows:
Figure FDA0002417350810000011
U1α1＝β1
L1β1＝xn
Figure FDA0002417350810000012
wherein GIP is the generalized inner product value, xnN is the number of the range gate, N is 1,2, …, N, M is the number of the channel, M is 1,2, …, M (·)HFor conjugate transpose operator, L1Is the first lower triangular matrix, U1Is the first upper triangular matrix;
step 23, filtering the echo vector according to the magnitude of the generalized inner product value and a preset rejection ratio to generate a filtered vector, calculating the second autocorrelation matrix according to the filtered vector, decomposing the second autocorrelation matrix into a second lower triangular matrix and a second upper triangular matrix, and calculating a weighting coefficient of the echo signal, wherein the calculation formula of the weighting coefficient is as follows:
Figure FDA0002417350810000021
U2α2＝β2
L2β2＝s
in the formula, woptFor the weighting coefficients, s is a steering vector, containing M components, representing the ideal echo signal, L2Is the second lower triangular matrix, U2Is the second upper triangular matrix.
3. The method for implementing adaptive signal processing based on gaussian elimination according to claim 2, wherein in step 3, the calculating the adaptive correlation estimation value of the echo signal corresponding to the range gate by using the gaussian elimination method specifically includes:
according to the guide vector s and the second lower triangular momentArray L2And the second upper triangular matrix U2Calculating said echo signal xnThe estimated adaptive correlation value of (1), wherein the estimated adaptive correlation value is calculated by the following formula:
Figure FDA0002417350810000022
U2α3＝β3
L2β3＝xn
wherein ACE is the adaptive correlation estimate.
4. The method for implementing adaptive signal processing based on gaussian elimination according to claim 2, wherein in step 23, the echo vector is filtered according to the magnitude of the generalized inner product value and a preset rejection ratio to generate a filtered vector, which specifically includes:
sorting the elements in the echo vector according to the magnitude of the generalized inner product value, rejecting the elements at two ends in the sorted echo vector according to a preset rejection proportion, recording the echo vector from which the elements are rejected as the screening vector,
wherein the preset rejection proportion is 10-20%.
5. A radar system, characterized in that the radar system comprises a target discrimination unit, after receiving an echo vector, the radar system transmits the echo vector to the target discrimination unit, and the target discrimination unit is configured to determine whether a target corresponding to the echo signal exists by using the method for implementing adaptive signal processing based on Gaussian elimination according to any one of claims 1 to 4.
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