CN112799042A - Extended target self-adaptive detection method and system based on oblique projection under interference - Google Patents

Extended target self-adaptive detection method and system based on oblique projection under interference Download PDF

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CN112799042A
CN112799042A CN202110376465.7A CN202110376465A CN112799042A CN 112799042 A CN112799042 A CN 112799042A CN 202110376465 A CN202110376465 A CN 202110376465A CN 112799042 A CN112799042 A CN 112799042A
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matrix
interference
detection
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oblique projection
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CN112799042B (en
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刘维建
李槟槟
周必雷
杜庆磊
陈辉
王永良
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Air Force Early Warning Academy
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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
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/04—Systems determining presence of a target
    • 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 invention relates to an extended target self-adaptive detection method and system based on oblique projection under interference. Firstly, constructing a signal matrix, an interference matrix, a data matrix to be detected and a training sample matrix, then constructing a sampling covariance matrix and an inverse matrix of a square root matrix thereof according to a training sample, whitening the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix, constructing a slant projection matrix according to the whitened signal matrix and the whitened interference matrix, constructing detection statistics according to the slant projection matrix and the whitened data matrix to be detected, and further determining a detection threshold according to the false alarm probability and the detection statistics set by the system; and finally, comparing the detection statistic with the detection threshold, and judging whether a target exists or not. The detector designed by the invention can thoroughly inhibit interference and realize target detection without independent filtering and constant false alarm processing flow.

Description

Extended target self-adaptive detection method and system based on oblique projection under interference
Technical Field
The invention relates to the technical field of signal detection, in particular to an extended target self-adaptive detection method and system based on oblique projection under interference.
Background
With the development and progress of radar technology, the capability of the radar is continuously improved, and the distance resolution is continuously enhanced, so that targets often occupy a plurality of distance resolution units, particularly targets such as large ship targets and strategic bombers. At this time, the conventional detection method based on the point target model is no longer applicable. On the other hand, electromagnetic interference is increasingly frequent, and the performance of radar detection performance is seriously influenced.
For the problem of target detection under interference, the traditional method detects a target by adopting a mode of filtering first and then constant false alarm processing, firstly inhibits the interference and reserves the target through filtering processing, and then realizes target detection by adopting constant false alarm processing such as unit averaging or unit selection. The method has the disadvantages of complicated process and limited detection effect.
Disclosure of Invention
In order to solve the problems of complex flow and poor detection performance of the existing detection technology, the invention provides an extended target self-adaptive detection method and system based on oblique projection under interference based on a self-adaptive detection idea.
In one aspect, the invention provides an extended target adaptive detection method and system based on oblique projection under interference, comprising the following steps:
step 1: constructing a signal matrix, an interference matrix, a data matrix to be detected and a training sample matrix;
step 2: constructing a sampling covariance matrix and an inverse matrix of a square root matrix thereof according to the training samples;
and step 3: whitening the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix;
and 4, step 4: constructing an oblique projection matrix according to the whitened signal matrix and the whitened interference matrix;
and 5: constructing detection statistics according to the oblique projection matrix and the whitened data matrix to be detected;
step 6: determining a detection threshold according to the false alarm probability set by the system and the detection statistic;
and 7: comparing the detection statistic with the detection threshold, and judging whether a target exists or not;
in the step 5, the detection statistic constructed according to the oblique projection matrix and the whitened data matrix to be detected is
Figure 985501DEST_PATH_IMAGE001
Wherein the content of the first and second substances,
Figure 417488DEST_PATH_IMAGE002
a trace representing a matrix;
Figure 525121DEST_PATH_IMAGE003
the whitening processing is carried out on the data matrix to be detected according to the square root matrix;
Figure 185910DEST_PATH_IMAGE004
representing an oblique projection matrix;
Figure 78910DEST_PATH_IMAGE005
a signal matrix representing the construct;
in the step 6, the detection threshold is determined according to the false alarm probability set by the system and the detection statistic, and the detection threshold is realized by the following formula
Figure 166952DEST_PATH_IMAGE006
In the formula (I), the compound is shown in the specification,
Figure 11149DEST_PATH_IMAGE007
，
Figure 475628DEST_PATH_IMAGE008
for the number of monte carlo simulations,
Figure 472403DEST_PATH_IMAGE009
the false alarm probability value set for the system,
Figure 482079DEST_PATH_IMAGE010
in order to carry out the rounding operation,
Figure 564304DEST_PATH_IMAGE011
is a sequence of
Figure 612900DEST_PATH_IMAGE012
Arranged from large to small
Figure 401865DEST_PATH_IMAGE013
The maximum value of the number of the first and second,
Figure 159605DEST_PATH_IMAGE014
，
Figure 417543DEST_PATH_IMAGE015
，
Figure 20562DEST_PATH_IMAGE016
for sampling covariance matrix
Figure 929612DEST_PATH_IMAGE017
Second implementation
Figure 847802DEST_PATH_IMAGE018
The decomposition of the characteristic value of (a),
Figure 107882DEST_PATH_IMAGE019
for data matrices to be detected containing only interference and noise components
Figure 452276DEST_PATH_IMAGE020
In the second implementation, the first and second antennas are connected,
Figure 28881DEST_PATH_IMAGE021
。
further, in the step 1, a signal matrix and an interference matrix are constructedThe matrix of data to be detected and the matrix of training samples may be represented as
Figure 66108DEST_PATH_IMAGE022
、
Figure 593910DEST_PATH_IMAGE023
、
Figure 741994DEST_PATH_IMAGE024
And
Figure 422374DEST_PATH_IMAGE025
the data dimensions of the four are respectively
Figure 381234DEST_PATH_IMAGE026
、
Figure 147065DEST_PATH_IMAGE027
、
Figure 98840DEST_PATH_IMAGE028
And
Figure 882995DEST_PATH_IMAGE029
，
Figure 527603DEST_PATH_IMAGE030
the system dimension, i.e. the number of rows of the data matrix to be detected,
Figure 718412DEST_PATH_IMAGE031
the number of columns of the signal matrix is represented,
Figure 21349DEST_PATH_IMAGE032
the number of columns of the interference matrix is represented,
Figure 348425DEST_PATH_IMAGE033
representing the number of columns of the data matrix to be detected,
Figure 163934DEST_PATH_IMAGE034
representing the number of training samples, i.e. trainingThe number of columns of the training sample matrix.
Further, in the step 2, the sampling covariance matrix constructed according to the training samples and the inverse matrix of the square root matrix thereof are respectively
Figure 153625DEST_PATH_IMAGE035
And
Figure 243941DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 707414DEST_PATH_IMAGE037
for sampling covariance matrix
Figure 162666DEST_PATH_IMAGE038
The decomposition of the characteristic value of (a),
Figure 593648DEST_PATH_IMAGE039
is composed of
Figure 754500DEST_PATH_IMAGE040
Is determined by the characteristic matrix of (a),
Figure 321747DEST_PATH_IMAGE041
in the form of a diagonal matrix,
Figure 213480DEST_PATH_IMAGE042
is composed of
Figure 679228DEST_PATH_IMAGE043
Is/are as follows
Figure 580188DEST_PATH_IMAGE044
The value of the characteristic is used as the characteristic value,
Figure 533100DEST_PATH_IMAGE045
in the formula, superscript
Figure 845002DEST_PATH_IMAGE046
Representing a conjugate transpose.
Further, in step 3, whitening processing is respectively performed on the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix, and the whitening processing is respectively realized through the following 3 equations
Figure 516154DEST_PATH_IMAGE047
Figure 220805DEST_PATH_IMAGE048
And
Figure 310115DEST_PATH_IMAGE049
。
further, in the step 4, the oblique projection matrix constructed according to the whitened signal matrix and the whitened interference matrix is
Figure 543650DEST_PATH_IMAGE050
Wherein the content of the first and second substances,
Figure 170941DEST_PATH_IMAGE051
，
Figure 459708DEST_PATH_IMAGE052
。
further, in step 7, the detection statistic is compared with the detection threshold, and whether a target exists is determined, where the determination is made according to the following two cases:
if the statistic is detected
Figure 590475DEST_PATH_IMAGE053
Greater than or equal to the detection threshold
Figure 260491DEST_PATH_IMAGE054
If yes, judging that the target exists;
if the statistic is detected
Figure 922548DEST_PATH_IMAGE055
Less than the detection threshold
Figure 437843DEST_PATH_IMAGE056
Then the target is determined to be absent.
In another aspect, the present invention provides an extended target adaptive detection system based on oblique projection under interference, including the following modules:
the data matrix construction module is used for constructing a data matrix to be detected, a training sample matrix, an interference matrix and a signal matrix;
the sampling covariance matrix and square matrix construction module is used for constructing a sampling covariance matrix and an inverse matrix of a square root matrix by using the training sample;
the data whitening module is used for whitening the data matrix to be detected, the signal matrix and the interference matrix by utilizing an inverse matrix of a square root matrix of the sampling covariance matrix;
the oblique projection matrix constructing module is used for constructing an oblique projection matrix by using the whitened signal matrix and the whitened interference matrix;
the detection statistic construction module is used for constructing detection statistic by utilizing the oblique projection matrix and the whitened data matrix to be detected;
the detection threshold determining module is used for determining a detection threshold according to the detection statistic and the false alarm probability value set by the system;
and the target judgment module is used for comparing the detection statistic with the detection threshold and making a judgment whether the target exists or not.
Compared with the prior art, the invention has the beneficial effects that:
1) the detector designed by the invention is suitable for subspace signal models and the situations when single and multiple interferences exist;
2) the detector designed by the invention can thoroughly inhibit interference and can effectively accumulate signals;
3) the detection method designed by the invention does not need an independent filtering step, effectively simplifies the detection flow and improves the detection efficiency;
drawings
FIG. 1 is a schematic flow chart of an extended target adaptive detection method based on oblique projection under interference according to the present invention;
FIG. 2 is a structural framework diagram of an extended target adaptive detection system based on oblique projection under interference according to the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
Assuming that the system channel number of the radar is
Figure 954275DEST_PATH_IMAGE057
The target extension dimension is
Figure 575618DEST_PATH_IMAGE058
When the data to be detected contains target, interference, clutter and thermal noise, the data to be detected is available
Figure 974238DEST_PATH_IMAGE059
The dimension matrix is represented as:
Figure 43957DEST_PATH_IMAGE060
(1)
wherein the content of the first and second substances,
Figure 211633DEST_PATH_IMAGE061
dimension matrix
Figure 957872DEST_PATH_IMAGE062
A matrix of signals is represented which is,
Figure 833336DEST_PATH_IMAGE063
dimension matrix
Figure 221592DEST_PATH_IMAGE064
A matrix of coordinates of the signals is represented,
Figure 181458DEST_PATH_IMAGE065
dimension matrix
Figure 911647DEST_PATH_IMAGE066
A matrix of interferences is represented,
Figure 222543DEST_PATH_IMAGE067
dimension matrix
Figure 148911DEST_PATH_IMAGE068
A matrix of interference coordinates is represented by a matrix of,
Figure 274867DEST_PATH_IMAGE069
dimension matrix
Figure 628488DEST_PATH_IMAGE070
Representing the sum of the clutter and thermal noise components. Summing the clutter and thermal noise components
Figure 426680DEST_PATH_IMAGE071
The corresponding covariance matrix is
Figure 704209DEST_PATH_IMAGE072
。
Conversely, if the data to be detected does not contain the target signal, the data to be detected can be expressed as:
Figure 373088DEST_PATH_IMAGE073
(2)
in the above-mentioned variant, the variable,
Figure 163189DEST_PATH_IMAGE074
and
Figure 494682DEST_PATH_IMAGE075
is known, and
Figure 497273DEST_PATH_IMAGE076
、
Figure 551817DEST_PATH_IMAGE077
and
Figure 60290DEST_PATH_IMAGE078
is unknown. In general,
Figure 301915DEST_PATH_IMAGE079
and
Figure 373776DEST_PATH_IMAGE080
obtained by maximum likelihood estimation, and are
Figure 594411DEST_PATH_IMAGE081
A certain number of training samples are required for the estimation. Suppose there is
Figure 195157DEST_PATH_IMAGE082
A training sample containing only noise component, denoted
Figure 2707DEST_PATH_IMAGE083
Each training sample was:
Figure 612680DEST_PATH_IMAGE084
(3)
wherein the content of the first and second substances,
Figure 376236DEST_PATH_IMAGE085
，
Figure 413463DEST_PATH_IMAGE086
is as follows
Figure 200985DEST_PATH_IMAGE086
The noise in the individual training samples, based on the training samples,
Figure 349069DEST_PATH_IMAGE087
is the sampling covariance matrix
Figure 780182DEST_PATH_IMAGE088
Upper label of
Figure 785047DEST_PATH_IMAGE089
Representing a conjugate transpose.
The results in assemblies (1), (2) and (3) can represent the detection problem as follows using the following binary hypothesis test:
Figure 754140DEST_PATH_IMAGE090
(4)
in the formula (I), the compound is shown in the specification,
Figure 220762DEST_PATH_IMAGE091
representing data to be detected
Figure 224490DEST_PATH_IMAGE092
The target signal is not contained in the signal,
Figure 150989DEST_PATH_IMAGE093
representing data to be detected
Figure 607378DEST_PATH_IMAGE094
Containing the target signal.
The invention aims to solve the problem of extended target detection in the presence of interference. To achieve the above object, please refer to fig. 1, the present invention provides a method and a system for adaptively detecting an extended target based on oblique projection under interference, including the following steps:
step 1: constructing a signal matrix, an interference matrix, a data matrix to be detected and a training sample matrix;
step 2: constructing a sampling covariance matrix and an inverse matrix of a square root matrix thereof according to the training samples;
and step 3: whitening the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix;
and 4, step 4: constructing an oblique projection matrix according to the whitened signal matrix and the whitened interference matrix;
and 5: constructing detection statistics according to the oblique projection matrix and the whitened data matrix to be detected;
step 6: determining a detection threshold according to the false alarm probability set by the system and the detection statistic;
and 7: comparing the detection statistic with the detection threshold, and judging whether a target exists or not;
in the step 5, the detection statistic constructed according to the oblique projection matrix and the whitened data matrix to be detected is
Figure 97265DEST_PATH_IMAGE001
Wherein the content of the first and second substances,
Figure 955500DEST_PATH_IMAGE002
a trace representing a matrix;
Figure 551435DEST_PATH_IMAGE003
the whitening processing is carried out on the data matrix to be detected according to the square root matrix;
Figure 495120DEST_PATH_IMAGE004
representing an oblique projection matrix;
Figure 336169DEST_PATH_IMAGE005
a signal matrix representing the construct;
in the step 6, the detection threshold is determined according to the false alarm probability set by the system and the detection statistic, and the detection threshold is realized by the following formula
Figure 48910DEST_PATH_IMAGE006
In the formula (I), the compound is shown in the specification,
Figure 769741DEST_PATH_IMAGE007
，
Figure 200722DEST_PATH_IMAGE008
for the number of monte carlo simulations,
Figure 343997DEST_PATH_IMAGE009
the false alarm probability value set for the system,
Figure 911244DEST_PATH_IMAGE010
in order to carry out the rounding operation,
Figure 802977DEST_PATH_IMAGE011
is a sequence of
Figure 471987DEST_PATH_IMAGE012
Arranged from large to small
Figure 904105DEST_PATH_IMAGE013
The maximum value of the number of the first and second,
Figure 591438DEST_PATH_IMAGE014
，
Figure 909199DEST_PATH_IMAGE015
，
Figure 845931DEST_PATH_IMAGE016
for sampling covariance matrix
Figure 550582DEST_PATH_IMAGE017
Second implementation
Figure 577575DEST_PATH_IMAGE018
The decomposition of the characteristic value of (a),
Figure 873427DEST_PATH_IMAGE019
to be detected containing only interference and noise componentsFirst of the data matrix
Figure 500717DEST_PATH_IMAGE020
In the second implementation, the first and second antennas are connected,
Figure 461589DEST_PATH_IMAGE021
。
specifically, in step 1, the constructed signal matrix, interference matrix, to-be-detected data matrix and training sample matrix can be respectively represented as
Figure 920252DEST_PATH_IMAGE022
、
Figure 590268DEST_PATH_IMAGE023
、
Figure 190008DEST_PATH_IMAGE024
And
Figure 767620DEST_PATH_IMAGE025
the data dimensions of the four are respectively
Figure 284052DEST_PATH_IMAGE026
、
Figure 108657DEST_PATH_IMAGE027
、
Figure 507277DEST_PATH_IMAGE028
And
Figure 91842DEST_PATH_IMAGE029
，
Figure 947934DEST_PATH_IMAGE030
the system dimension, i.e. the number of rows of the data matrix to be detected,
Figure 756490DEST_PATH_IMAGE031
the number of columns of the signal matrix is represented,
Figure 580090DEST_PATH_IMAGE032
the number of columns of the interference matrix is represented,
Figure 952034DEST_PATH_IMAGE033
representing the number of columns of the data matrix to be detected,
Figure 177479DEST_PATH_IMAGE034
the number of training samples, i.e. the number of columns of the training sample matrix, is indicated.
Specifically, in the step 2, the sampling covariance matrix and the inverse matrix of the square root matrix constructed from the training samples are respectively set to
Figure 156936DEST_PATH_IMAGE035
And
Figure 218564DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 144932DEST_PATH_IMAGE037
for sampling covariance matrix
Figure 21621DEST_PATH_IMAGE038
The decomposition of the characteristic value of (a),
Figure 642088DEST_PATH_IMAGE039
is composed of
Figure 237017DEST_PATH_IMAGE040
Is determined by the characteristic matrix of (a),
Figure 701497DEST_PATH_IMAGE041
in the form of a diagonal matrix,
Figure 386687DEST_PATH_IMAGE042
is composed of
Figure 973526DEST_PATH_IMAGE043
Is/are as follows
Figure 993435DEST_PATH_IMAGE044
The value of the characteristic is used as the characteristic value,
Figure 510873DEST_PATH_IMAGE045
in the formula, superscript
Figure 565416DEST_PATH_IMAGE046
Representing a conjugate transpose.
Specifically, in step 3, whitening processing is performed on the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix, and is respectively realized through the following 3 equations
Figure 57577DEST_PATH_IMAGE047
Figure 315515DEST_PATH_IMAGE048
And
Figure 184114DEST_PATH_IMAGE049
。
specifically, in the step 4, the oblique projection matrix constructed from the whitened signal matrix and the whitened interference matrix is
Figure 93164DEST_PATH_IMAGE050
Wherein the content of the first and second substances,
Figure 208756DEST_PATH_IMAGE051
，
Figure 999995DEST_PATH_IMAGE052
。
specifically, in step 7, the detection statistic is compared with the detection threshold, and whether a target exists is determined, where the determination is made according to the following two cases:
if the statistic is detected
Figure 875547DEST_PATH_IMAGE053
Greater than or equal to the detection threshold
Figure 389836DEST_PATH_IMAGE054
If yes, judging that the target exists;
if the statistic is detected
Figure 223800DEST_PATH_IMAGE055
Less than the detection threshold
Figure 971176DEST_PATH_IMAGE056
Then the target is determined to be absent.
Referring to fig. 2, the present invention further provides an extended target adaptive detection system based on oblique projection under interference, including the following modules:
the data matrix construction module is used for constructing a data matrix to be detected, a training sample matrix, an interference matrix and a signal matrix;
the sampling covariance matrix and square matrix construction module is used for constructing a sampling covariance matrix and an inverse matrix of a square root matrix by using the training sample;
the data whitening module is used for whitening the data matrix to be detected, the signal matrix and the interference matrix by utilizing an inverse matrix of a square root matrix of the sampling covariance matrix;
the oblique projection matrix constructing module is used for constructing an oblique projection matrix by using the whitened signal matrix and the whitened interference matrix;
the detection statistic construction module is used for constructing detection statistic by utilizing the oblique projection matrix and the whitened data matrix to be detected;
the detection threshold determining module is used for determining a detection threshold according to the detection statistic and the false alarm probability value set by the system;
and the target judgment module is used for comparing the detection statistic with the detection threshold and making a judgment whether the target exists or not.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. An extended target self-adaptive detection method and system based on oblique projection under interference are characterized in that: the method comprises the following steps:
step 1: constructing a signal matrix, an interference matrix, a data matrix to be detected and a training sample matrix;
step 2: constructing a sampling covariance matrix and an inverse matrix of a square root matrix thereof according to the training samples;
and step 3: whitening the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix;
and 4, step 4: constructing an oblique projection matrix according to the whitened signal matrix and the whitened interference matrix;
and 5: constructing detection statistics according to the oblique projection matrix and the whitened data matrix to be detected;
step 6: determining a detection threshold according to the false alarm probability set by the system and the detection statistic;
and 7: comparing the detection statistic with the detection threshold, and judging whether a target exists or not;
in the step 5, the detection statistic constructed according to the oblique projection matrix and the whitened data matrix to be detected is
Figure 316511DEST_PATH_IMAGE001
Wherein the content of the first and second substances,
Figure 934574DEST_PATH_IMAGE002
a trace representing a matrix;
Figure 142701DEST_PATH_IMAGE003
the whitening processing is carried out on the data matrix to be detected according to the square root matrix;
Figure 892220DEST_PATH_IMAGE004
representing an oblique projection matrix;
Figure 906313DEST_PATH_IMAGE005
a signal matrix representing the construct;
in the step 6, the detection threshold is determined according to the false alarm probability set by the system and the detection statistic, and the detection threshold is realized by the following formula
Figure 129615DEST_PATH_IMAGE006
In the formula (I), the compound is shown in the specification,
Figure 570960DEST_PATH_IMAGE007
，
Figure 27350DEST_PATH_IMAGE008
for the number of monte carlo simulations,
Figure 766504DEST_PATH_IMAGE009
the false alarm probability value set for the system,
Figure 421477DEST_PATH_IMAGE010
in order to carry out the rounding operation,
Figure 971407DEST_PATH_IMAGE011
is a sequence of
Figure 196983DEST_PATH_IMAGE012
Arranged from large to small
Figure 490561DEST_PATH_IMAGE013
The maximum value of the number of the first and second,
Figure 266885DEST_PATH_IMAGE014
，
Figure 987717DEST_PATH_IMAGE015
，
Figure 215436DEST_PATH_IMAGE016
for sampling covariance matrix
Figure 797858DEST_PATH_IMAGE017
Second implementation
Figure 161843DEST_PATH_IMAGE018
The decomposition of the characteristic value of (a),
Figure 53576DEST_PATH_IMAGE019
for data matrices to be detected containing only interference and noise components
Figure 955541DEST_PATH_IMAGE020
In the second implementation, the first and second antennas are connected,
Figure 653239DEST_PATH_IMAGE021
。
2. the extended target adaptive detection method based on oblique projection under interference according to claim 1,the method is characterized in that: in step 1, the constructed signal matrix, interference matrix, to-be-detected data matrix and training sample matrix can be respectively expressed as
Figure 74993DEST_PATH_IMAGE022
、
Figure 950676DEST_PATH_IMAGE023
、
Figure 825091DEST_PATH_IMAGE024
And
Figure 310168DEST_PATH_IMAGE025
the data dimensions of the four are respectively
Figure 586429DEST_PATH_IMAGE026
、
Figure 85543DEST_PATH_IMAGE027
、
Figure 260304DEST_PATH_IMAGE028
And
Figure 237487DEST_PATH_IMAGE029
，
Figure 696150DEST_PATH_IMAGE030
the system dimension, i.e. the number of rows of the data matrix to be detected,
Figure 349854DEST_PATH_IMAGE031
the number of columns of the signal matrix is represented,
Figure 198862DEST_PATH_IMAGE032
the number of columns of the interference matrix is represented,
Figure 776474DEST_PATH_IMAGE033
representing the number of columns of the data matrix to be detected,
Figure 840376DEST_PATH_IMAGE034
the number of training samples, i.e. the number of columns of the training sample matrix, is indicated.
3. The extended target adaptive detection method based on oblique projection under interference according to claim 2, characterized in that: in the step 2, the sampling covariance matrix constructed according to the training samples and the inverse matrix of the square root matrix thereof are respectively
Figure 415713DEST_PATH_IMAGE035
And
Figure 803881DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 122867DEST_PATH_IMAGE037
for sampling covariance matrix
Figure 493806DEST_PATH_IMAGE038
The decomposition of the characteristic value of (a),
Figure 53094DEST_PATH_IMAGE039
is composed of
Figure 876694DEST_PATH_IMAGE040
Is determined by the characteristic matrix of (a),
Figure 530529DEST_PATH_IMAGE041
in the form of a diagonal matrix,
Figure 270821DEST_PATH_IMAGE042
is composed of
Figure 781437DEST_PATH_IMAGE043
Is/are as follows
Figure 577485DEST_PATH_IMAGE044
The value of the characteristic is used as the characteristic value,
Figure 503853DEST_PATH_IMAGE045
in the formula, superscript
Figure 646121DEST_PATH_IMAGE046
Representing a conjugate transpose.
4. The extended target adaptive detection method based on oblique projection under interference according to claim 3, characterized in that: in the step 3, whitening processing is respectively carried out on the signal matrix, the interference matrix and the data matrix to be detected according to the square root matrix, and the whitening processing is respectively realized through the following 3 equations
Figure 983431DEST_PATH_IMAGE047
Figure 578360DEST_PATH_IMAGE048
And
Figure 42840DEST_PATH_IMAGE049
。
5. the extended target adaptive detection method based on oblique projection under interference according to claim 4, characterized in that: in the step 4, the oblique projection matrix constructed according to the whitened signal matrix and the whitened interference matrix is
Figure 993609DEST_PATH_IMAGE050
Wherein the content of the first and second substances,
Figure 314869DEST_PATH_IMAGE051
，
Figure 334778DEST_PATH_IMAGE052
。
6. the extended target adaptive detection method based on oblique projection under interference according to claim 5, characterized in that: in step 7, the detection statistic and the detection threshold are compared, and whether a target exists is judged, and the judgment is carried out according to the following two conditions:
if the statistic is detected
Figure 914532DEST_PATH_IMAGE053
Greater than or equal to the detection threshold
Figure 703497DEST_PATH_IMAGE054
If yes, judging that the target exists;
if the statistic is detected
Figure 946391DEST_PATH_IMAGE055
Less than the detection threshold
Figure 719174DEST_PATH_IMAGE056
Then the target is determined to be absent.
7. An extended target self-adaptive detection system based on oblique projection under interference is characterized in that: the system comprises the following modules:
the data matrix construction module is used for constructing a data matrix to be detected, a training sample matrix, an interference matrix and a signal matrix;
the sampling covariance matrix and square matrix construction module is used for constructing a sampling covariance matrix and an inverse matrix of a square root matrix by using the training sample;
the data whitening module is used for whitening the data matrix to be detected, the signal matrix and the interference matrix by utilizing an inverse matrix of a square root matrix of the sampling covariance matrix;
the oblique projection matrix constructing module is used for constructing an oblique projection matrix by using the whitened signal matrix and the whitened interference matrix;
the detection statistic construction module is used for constructing detection statistic by utilizing the oblique projection matrix and the whitened data matrix to be detected;
the detection threshold determining module is used for determining a detection threshold according to the detection statistic and the false alarm probability value set by the system;
and the target judgment module is used for comparing the detection statistic with the detection threshold and making a judgment whether the target exists or not.
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