CN112558015A - Method and system for interference suppression before self-adaptive detection in complex electromagnetic environment - Google Patents

Method and system for interference suppression before self-adaptive detection in complex electromagnetic environment Download PDF

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CN112558015A
CN112558015A CN202110200749.0A CN202110200749A CN112558015A CN 112558015 A CN112558015 A CN 112558015A CN 202110200749 A CN202110200749 A CN 202110200749A CN 112558015 A CN112558015 A CN 112558015A
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matrix
interference
detection
suppression
detection threshold
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CN112558015B (en
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刘维建
李槟槟
周必雷
张昭建
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
    • 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
    • G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter

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  • Computer Networks & Wireless Communication (AREA)
  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention relates to a method and a system for self-adaptive detection after interference suppression in a complex electromagnetic environment. The detection method designed by the invention can thoroughly remove interference, can effectively suppress clutter, has the constant false alarm characteristic, does not need additional constant false alarm processing, and has excellent detection performance and high efficiency.

Description

Method and system for interference suppression before self-adaptive detection in complex electromagnetic environment
Technical Field
The invention relates to the technical field of radar signal detection, in particular to a method and a system for interference suppression and adaptive detection in a complex electromagnetic environment.
Background
Target detection in complex electromagnetic environments has been a difficult problem for radar target detection. On the one hand, clutter components in complex electromagnetic environments are large, with powers several orders of magnitude higher than the target signal. On the other hand, a large amount of interference signals often exist in the complex electromagnetic environment, although the interference generally exists in only one or a few directions, the interference intensity is large, the interference occupies the whole frequency band of the radar, the real radar target signals can be submerged, and the radar target detection in the complex electromagnetic environment is further aggravated.
Disclosure of Invention
In order to solve the problem of radar target detection in a complex electromagnetic environment, the invention provides a method and a system for interference suppression before self-adaptive detection in the complex electromagnetic environment, which are used for overcoming the problem of low detection efficiency in the prior art.
On one hand, the invention provides a method for self-adaptive detection after interference suppression in a complex electromagnetic environment, which comprises the following steps:
step 1: constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
step 2: constructing an interference suppression matrix according to the interference matrix;
and step 3: carrying out interference information suppression on a signal matrix, a data vector to be detected and a training sample matrix by using the interference suppression matrix;
and 4, step 4: constructing detection statistics by using the data after the interference information suppression;
and 5: calculating a detection threshold according to the system parameters and the false alarm probability;
step 6: and comparing the detection statistic with the detection threshold to judge whether the target exists.
Further, in the step 1, constructing the interference matrix and the training sample matrix are respectively realized by the following two equations
Figure 174978DEST_PATH_IMAGE001
And
Figure 968622DEST_PATH_IMAGE002
in the formula (I), the compound is shown in the specification,
Figure 338423DEST_PATH_IMAGE003
Figure 917435DEST_PATH_IMAGE004
the distance between the antenna array elements is the same,
Figure 714489DEST_PATH_IMAGE005
in order for the radar to emit a signal wavelength,
Figure 979117DEST_PATH_IMAGE006
is as follows
Figure 559134DEST_PATH_IMAGE007
The azimuth angle of the individual interference,
Figure 569816DEST_PATH_IMAGE008
；
Figure 960608DEST_PATH_IMAGE009
representing the number of interferers, i.e. interference matrix
Figure 728844DEST_PATH_IMAGE010
The number of columns; upper label
Figure 440448DEST_PATH_IMAGE011
Representing a transpose;
Figure 227007DEST_PATH_IMAGE012
in the vicinity of the unit to be detected
Figure 788701DEST_PATH_IMAGE013
Training samples;
Figure 372129DEST_PATH_IMAGE014
representing the number of training samples;
Figure 559528DEST_PATH_IMAGE015
representing an imaginary number, i.e.
Figure 279222DEST_PATH_IMAGE016
。
Further, in step 2, the interference suppression matrix is
Figure 510352DEST_PATH_IMAGE017
In the formula (I), the compound is shown in the specification,
Figure 761771DEST_PATH_IMAGE018
is a matrix
Figure 815178DEST_PATH_IMAGE019
After
Figure 327062DEST_PATH_IMAGE020
The columns of the image data are,
Figure 807721DEST_PATH_IMAGE021
as an interference matrix
Figure 287113DEST_PATH_IMAGE022
Left unitary matrices of singular value decomposition, i.e.
Figure 301468DEST_PATH_IMAGE023
Is decomposed into singular values
Figure 995754DEST_PATH_IMAGE024
；
Figure 584999DEST_PATH_IMAGE025
And
Figure 286107DEST_PATH_IMAGE026
are respectively as
Figure 681317DEST_PATH_IMAGE027
And
Figure 167793DEST_PATH_IMAGE028
a dimensional unitary matrix;
Figure 413092DEST_PATH_IMAGE029
is composed of
Figure 680125DEST_PATH_IMAGE030
A dimensional diagonal matrix;
Figure 800396DEST_PATH_IMAGE031
indicating the number of system channels.
Further, in the step 3, the interference information suppression of the signal matrix, the data vector to be detected and the training sample matrix by using the interference suppression matrix is respectively performed by the following three formulas
Figure 469275DEST_PATH_IMAGE032
In the formula (I), the compound is shown in the specification,
Figure 869164DEST_PATH_IMAGE033
in the form of a matrix of signals,
Figure 46329DEST_PATH_IMAGE034
for the data vector to be detected,
Figure 783341DEST_PATH_IMAGE035
a training sample matrix is obtained;
Figure 228098DEST_PATH_IMAGE036
、
Figure 330046DEST_PATH_IMAGE037
and
Figure 571672DEST_PATH_IMAGE038
respectively of dimension
Figure 269631DEST_PATH_IMAGE039
、
Figure 913102DEST_PATH_IMAGE040
And
Figure 169640DEST_PATH_IMAGE041
；
Figure 570666DEST_PATH_IMAGE042
、
Figure 649480DEST_PATH_IMAGE043
and
Figure 570294DEST_PATH_IMAGE044
respectively of dimension
Figure 997733DEST_PATH_IMAGE045
、
Figure 948371DEST_PATH_IMAGE046
And
Figure 502981DEST_PATH_IMAGE047
；
Figure 855465DEST_PATH_IMAGE048
representing a signal matrix
Figure 220849DEST_PATH_IMAGE049
The number of columns; upper label
Figure 330888DEST_PATH_IMAGE050
Representing a conjugate transpose.
Further, the detection statistic constructed by using the data after the interference information suppression in the step 4 is
Figure 751505DEST_PATH_IMAGE051
In the formula (I), the compound is shown in the specification,
Figure 145446DEST_PATH_IMAGE052
。
further, the detection threshold in step 5 is obtained by using a numerical search method according to the following formula
Figure 258895DEST_PATH_IMAGE053
Wherein the content of the first and second substances,
Figure 606962DEST_PATH_IMAGE054
the false alarm probability is preset for the system,
Figure 503374DEST_PATH_IMAGE055
is the detection threshold of the detector and is,
Figure 564871DEST_PATH_IMAGE056
，
Figure 770593DEST_PATH_IMAGE057
，
Figure 340377DEST_PATH_IMAGE058
，
Figure 368376DEST_PATH_IMAGE059
and
Figure 222063DEST_PATH_IMAGE060
are respectively expressed as
Figure 411735DEST_PATH_IMAGE061
And
Figure 232930DEST_PATH_IMAGE062
Figure 221877DEST_PATH_IMAGE063
representing a factorial of integers.
Further, the comparison between the detection statistic and the detection threshold in step 6 is determined according to the following two cases:
if the statistic is detected
Figure 523545DEST_PATH_IMAGE064
Greater than or equal to the detection threshold
Figure 556223DEST_PATH_IMAGE065
If yes, judging that the target exists;
if the statistic is detected
Figure 864713DEST_PATH_IMAGE064
Less than the detection threshold
Figure 234515DEST_PATH_IMAGE065
Then the target is determined to be absent.
In another aspect, the present invention provides an adaptive detection system after interference suppression in a complex electromagnetic environment, including:
the data matrix construction module is used for constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
an interference rejection matrix construction module, configured to construct an interference rejection matrix;
the interference component suppression module is used for suppressing interference components in the signal matrix, the data vector to be detected and the training sample matrix;
the detection statistic constructing module is used for constructing detection statistics;
the detection threshold calculation module is used for determining a detection threshold by using the false alarm probability set by the system and the system parameters;
and the target judgment module is used for comparing the detection statistic with the detection threshold, judging that the target exists if the detection statistic is larger than the detection threshold, and otherwise judging that the target does not exist.
Compared with the prior art, the method has the beneficial effects that the interference can be well inhibited by constructing the interference inhibiting matrix; after interference suppression, the number of required training samples is further reduced in order to suppress clutter; in addition, the detector has the constant false alarm characteristic, extra constant false alarm processing is not needed, the time required by detection is effectively shortened, and the detection efficiency is improved.
Drawings
FIG. 1 is a schematic flow chart of a method and system for interference rejection followed by adaptive detection in a complex electromagnetic environment according to the present invention;
fig. 2 is a structural framework diagram of the method and system for adaptive detection after interference suppression in a complex electromagnetic environment 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 797214DEST_PATH_IMAGE066
If the data to be detected is available
Figure 17105DEST_PATH_IMAGE067
Dimension vector
Figure 625941DEST_PATH_IMAGE068
And (4) showing. Assuming data to be detected
Figure 720805DEST_PATH_IMAGE069
In existence of
Figure 731486DEST_PATH_IMAGE070
Each interference at an angle to the normal of the radar array
Figure 105967DEST_PATH_IMAGE071
，
Figure 624935DEST_PATH_IMAGE072
Then it is first
Figure 336540DEST_PATH_IMAGE073
The steering vector of each interference can be expressed as
Figure 123099DEST_PATH_IMAGE074
（1）
In the formula (I), the compound is shown in the specification,
Figure 934060DEST_PATH_IMAGE075
the distance between the antenna array elements is the same,
Figure 517488DEST_PATH_IMAGE076
for radar emission of signal wavelengths, superscript
Figure 190040DEST_PATH_IMAGE077
Indicating transposition.
If the data to be detected
Figure 175314DEST_PATH_IMAGE078
Including target echo signals
Figure 406444DEST_PATH_IMAGE079
Can be represented by a subspace model as
Figure 149272DEST_PATH_IMAGE080
Wherein, in the step (A),
Figure 202678DEST_PATH_IMAGE081
dimension matrix
Figure 465295DEST_PATH_IMAGE082
A matrix of known signals is represented, and,
Figure 601747DEST_PATH_IMAGE083
vector of dimension column
Figure 894188DEST_PATH_IMAGE084
Representing the unknown coordinates of the signal.
In addition to interference and targets, clutter components often exist in the data to be detected
Figure 423389DEST_PATH_IMAGE085
Dimension vector
Figure 117676DEST_PATH_IMAGE086
And (4) showing. Thus, the data to be detected can be expressed as:
Figure 192073DEST_PATH_IMAGE087
make clutter
Figure 909494DEST_PATH_IMAGE088
Has a covariance matrix of
Figure 304703DEST_PATH_IMAGE089
In real environment, clutter covariance matrix
Figure 774867DEST_PATH_IMAGE090
Is generally unknown, for which a certain number of training sample pairs are required
Figure 285746DEST_PATH_IMAGE091
And (6) estimating. Suppose there is
Figure 552779DEST_PATH_IMAGE092
A training sample containing only clutter components, denoted
Figure 423783DEST_PATH_IMAGE093
Each training sample was:
Figure 827082DEST_PATH_IMAGE094
wherein the content of the first and second substances,
Figure 7397DEST_PATH_IMAGE095
，
Figure 895546DEST_PATH_IMAGE096
is as follows
Figure 632558DEST_PATH_IMAGE097
Clutter in individual training samples.
The invention aims to solve the problem of radar target detection in the presence of clutter and interference in a complex electromagnetic environment. To achieve the above object, please refer to fig. 1, the present invention provides a method for adaptive detection after interference suppression in a complex electromagnetic environment, including:
step 1: constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
step 2: constructing an interference suppression matrix according to the interference matrix;
and step 3: carrying out interference information suppression on a signal matrix, a data vector to be detected and a training sample matrix by using the interference suppression matrix;
and 4, step 4: constructing detection statistics by using the data after the interference information suppression;
and 5: calculating a detection threshold according to the system parameters and the false alarm probability;
step 6: and comparing the detection statistic with the detection threshold to judge whether the target exists.
Specifically, in step 1, constructing the interference matrix and the training sample matrix are respectively implemented by the following two equations
Figure 828047DEST_PATH_IMAGE098
And
Figure 257891DEST_PATH_IMAGE099
in the formula (I), the compound is shown in the specification,
Figure 155309DEST_PATH_IMAGE100
Figure 118848DEST_PATH_IMAGE004
the distance between the antenna array elements is the same,
Figure 762319DEST_PATH_IMAGE005
in order for the radar to emit a signal wavelength,
Figure 769589DEST_PATH_IMAGE006
is as follows
Figure 498511DEST_PATH_IMAGE007
The azimuth angle of the individual interference,
Figure 498697DEST_PATH_IMAGE008
；
Figure 668778DEST_PATH_IMAGE009
representing the number of interferers, i.e. interference matrix
Figure 174846DEST_PATH_IMAGE010
The number of columns; upper label
Figure 813900DEST_PATH_IMAGE011
Representing a transpose;
Figure 447137DEST_PATH_IMAGE012
in the vicinity of the unit to be detected
Figure 799621DEST_PATH_IMAGE013
Training samples;
Figure 663541DEST_PATH_IMAGE014
representing the number of training samples;
Figure 773579DEST_PATH_IMAGE015
representing an imaginary number, i.e.
Figure 194196DEST_PATH_IMAGE016
。
Specifically, in the step 2, the interference suppression matrix is
Figure 89602DEST_PATH_IMAGE101
In the formula (I), the compound is shown in the specification,
Figure 937473DEST_PATH_IMAGE102
is a matrix
Figure 784075DEST_PATH_IMAGE103
After
Figure 680487DEST_PATH_IMAGE104
The columns of the image data are,
Figure 430399DEST_PATH_IMAGE105
as an interference matrix
Figure 370542DEST_PATH_IMAGE106
Left unitary matrices of singular value decomposition, i.e.
Figure 455173DEST_PATH_IMAGE107
Is decomposed into singular values
Figure 217592DEST_PATH_IMAGE108
，
Figure 822011DEST_PATH_IMAGE109
And
Figure 277264DEST_PATH_IMAGE110
are respectively as
Figure 849190DEST_PATH_IMAGE111
And
Figure 336672DEST_PATH_IMAGE112
a dimensional unitary matrix is formed by a plurality of unitary matrices,
Figure 372761DEST_PATH_IMAGE113
is composed of
Figure 421751DEST_PATH_IMAGE114
A dimensional diagonal matrix;
Figure 480974DEST_PATH_IMAGE031
indicating the number of system channels.
Specifically, in the step 3, the interference suppression matrix is used to perform interference information suppression on the signal matrix, the data vector to be detected and the training sample matrix through the following three formulas respectively
Figure 585196DEST_PATH_IMAGE032
In the formula (I), the compound is shown in the specification,
Figure 662743DEST_PATH_IMAGE033
in the form of a matrix of signals,
Figure 459797DEST_PATH_IMAGE034
for the data vector to be detected,
Figure 225890DEST_PATH_IMAGE035
a training sample matrix is obtained;
Figure 71486DEST_PATH_IMAGE036
、
Figure 82168DEST_PATH_IMAGE037
and
Figure 971495DEST_PATH_IMAGE038
respectively of dimension
Figure 67627DEST_PATH_IMAGE039
、
Figure 936488DEST_PATH_IMAGE040
And
Figure 739359DEST_PATH_IMAGE041
；
Figure 878217DEST_PATH_IMAGE042
、
Figure 383016DEST_PATH_IMAGE043
and
Figure 304836DEST_PATH_IMAGE044
respectively of dimension
Figure 290109DEST_PATH_IMAGE045
、
Figure 22704DEST_PATH_IMAGE046
And
Figure 93428DEST_PATH_IMAGE047
；
Figure 68207DEST_PATH_IMAGE048
representing a signal matrix
Figure 314511DEST_PATH_IMAGE049
The number of columns; upper label
Figure 795171DEST_PATH_IMAGE050
Representing a conjugate transpose.
Specifically, the detection statistic value constructed by using the data after the interference information suppression in the step 4 is
Figure 510449DEST_PATH_IMAGE051
In the formula (I), the compound is shown in the specification,
Figure 288918DEST_PATH_IMAGE052
。
specifically, the detection threshold in step 5 is obtained by using a numerical search method according to the following formula
Figure 983204DEST_PATH_IMAGE053
Wherein the content of the first and second substances,
Figure 306869DEST_PATH_IMAGE054
the false alarm probability is preset for the system,
Figure 775022DEST_PATH_IMAGE055
is the detection threshold of the detector and is,
Figure 170231DEST_PATH_IMAGE056
，
Figure 640396DEST_PATH_IMAGE057
，
Figure 728437DEST_PATH_IMAGE058
，
Figure 667574DEST_PATH_IMAGE059
and
Figure 23732DEST_PATH_IMAGE115
are respectively expressed as
Figure 692610DEST_PATH_IMAGE061
And
Figure 607346DEST_PATH_IMAGE116
Figure 768200DEST_PATH_IMAGE117
representing a factorial of integers.
Specifically, the comparison between the detection statistic and the detection threshold in step 6 is determined according to the following two cases:
if the statistic is detected
Figure 770791DEST_PATH_IMAGE064
Greater than or equal to the detection threshold
Figure 717012DEST_PATH_IMAGE065
If yes, judging that the target exists;
if the statistic is detected
Figure 881277DEST_PATH_IMAGE064
Less than the detection threshold
Figure 795007DEST_PATH_IMAGE065
Then the target is determined to be absent.
Referring to fig. 2, the present invention further provides an adaptive detection system after interference suppression in a complex electromagnetic environment, including:
the data matrix construction module is used for constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
the interference suppression matrix constructing module is used for constructing a matrix for suppressing interference information;
the interference component suppression module is used for suppressing interference components in the signal matrix, the data vector to be detected and the training sample matrix;
the detection statistic constructing module is used for constructing detection statistics;
the detection threshold calculation module is used for determining a detection threshold by using the false alarm probability set by the system and the system parameters;
and the target judgment module is used for comparing the detection statistic with the detection threshold, judging that the target exists if the detection statistic is larger than the detection threshold, and otherwise judging that the target does not exist.
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.

Claims (8)

1. A method for self-adaptive detection after interference suppression in a complex electromagnetic environment is characterized by comprising the following steps:
step 1: constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
step 2: constructing an interference suppression matrix according to the interference matrix;
and step 3: carrying out interference information suppression on a signal matrix, a data vector to be detected and a training sample matrix by using the interference suppression matrix;
and 4, step 4: constructing detection statistics by using the data after the interference information suppression;
and 5: calculating a detection threshold according to the system parameters and the false alarm probability;
step 6: and comparing the detection statistic with the detection threshold to judge whether the target exists.
2. The method according to claim 1, wherein the method comprises the following steps: in the step 1, the interference matrix and the training sample matrix are constructed by the following two equations respectively
Figure 893800DEST_PATH_IMAGE001
And
Figure 694528DEST_PATH_IMAGE002
in the formula (I), the compound is shown in the specification,
Figure 295273DEST_PATH_IMAGE003
Figure 430720DEST_PATH_IMAGE004
the distance between the antenna array elements is the same,
Figure 696485DEST_PATH_IMAGE005
in order for the radar to emit a signal wavelength,
Figure 194462DEST_PATH_IMAGE006
is as follows
Figure 834350DEST_PATH_IMAGE007
The azimuth angle of the individual interference,
Figure 722671DEST_PATH_IMAGE008
；
Figure 605176DEST_PATH_IMAGE009
representing the number of interferers, i.e. interference matrix
Figure 879032DEST_PATH_IMAGE010
Column number superscript of
Figure 978837DEST_PATH_IMAGE011
Representing a transpose;
Figure 416772DEST_PATH_IMAGE012
in the vicinity of the unit to be detected
Figure 775072DEST_PATH_IMAGE013
Training samples;
Figure 903434DEST_PATH_IMAGE014
representing the number of training samples;
Figure 33195DEST_PATH_IMAGE015
representing an imaginary number, i.e.
Figure 364950DEST_PATH_IMAGE016
。
3. The method according to claim 2, wherein the method comprises the following steps: in the step 2, the interference suppression matrix is
Figure 589258DEST_PATH_IMAGE017
In the formula (I), the compound is shown in the specification,
Figure 339171DEST_PATH_IMAGE018
is a matrix
Figure 544893DEST_PATH_IMAGE019
After
Figure 363944DEST_PATH_IMAGE020
The columns of the image data are,
Figure 814780DEST_PATH_IMAGE021
as an interference matrix
Figure 996362DEST_PATH_IMAGE022
Left unitary matrices of singular value decomposition, i.e.
Figure 372986DEST_PATH_IMAGE023
Is decomposed into singular values
Figure 944912DEST_PATH_IMAGE024
；
Figure 511023DEST_PATH_IMAGE025
And
Figure 969948DEST_PATH_IMAGE026
are respectively as
Figure 251894DEST_PATH_IMAGE027
And
Figure 373434DEST_PATH_IMAGE028
a dimensional unitary matrix;
Figure 415339DEST_PATH_IMAGE029
is composed of
Figure 994350DEST_PATH_IMAGE030
A dimensional diagonal matrix;
Figure 463509DEST_PATH_IMAGE031
indicating the number of system channels.
4. The method according to claim 3, wherein the method comprises the following steps: in the step 3, the interference information suppression is performed on the signal matrix, the data vector to be detected and the training sample matrix by using the interference suppression matrix through the following three formulas respectively
Figure 72345DEST_PATH_IMAGE032
In the formula (I), the compound is shown in the specification,
Figure 901629DEST_PATH_IMAGE033
in the form of a matrix of signals,
Figure 335147DEST_PATH_IMAGE034
for the data vector to be detected,
Figure 303103DEST_PATH_IMAGE035
a training sample matrix is obtained;
Figure 71339DEST_PATH_IMAGE036
、
Figure 704314DEST_PATH_IMAGE037
and
Figure 835082DEST_PATH_IMAGE038
respectively of dimension
Figure 131196DEST_PATH_IMAGE039
、
Figure 121149DEST_PATH_IMAGE040
And
Figure 636444DEST_PATH_IMAGE041
；
Figure 543089DEST_PATH_IMAGE042
、
Figure 852847DEST_PATH_IMAGE043
and
Figure 80828DEST_PATH_IMAGE044
respectively of dimension
Figure 806339DEST_PATH_IMAGE045
、
Figure 646119DEST_PATH_IMAGE046
And
Figure 720254DEST_PATH_IMAGE047
；
Figure 701111DEST_PATH_IMAGE048
to representSignal matrix
Figure 292629DEST_PATH_IMAGE049
The number of columns; upper label
Figure 173866DEST_PATH_IMAGE050
Representing a conjugate transpose.
5. The method according to claim 4, wherein the method comprises the following steps: the detection statistic value constructed by using the data after the interference information suppression in the step 4 is
Figure 231952DEST_PATH_IMAGE051
In the formula (I), the compound is shown in the specification,
Figure 434526DEST_PATH_IMAGE052
。
6. the method according to claim 5, wherein the method comprises the following steps: the detection threshold in the step 5 is obtained by using a numerical search method according to the following formula
Figure 829735DEST_PATH_IMAGE053
Wherein the content of the first and second substances,
Figure 565479DEST_PATH_IMAGE054
the false alarm probability is preset for the system,
Figure 60045DEST_PATH_IMAGE055
is the detection threshold of the detector and is,
Figure 749915DEST_PATH_IMAGE056
，
Figure 948815DEST_PATH_IMAGE057
，
Figure 539065DEST_PATH_IMAGE058
，
Figure 204533DEST_PATH_IMAGE059
and
Figure 958862DEST_PATH_IMAGE060
are respectively expressed as
Figure 384290DEST_PATH_IMAGE061
And
Figure 563467DEST_PATH_IMAGE062
Figure 399836DEST_PATH_IMAGE063
representing a factorial of integers.
7. The method according to claim 6, wherein the method comprises the following steps: in the step 6, the comparison between the detection statistic and the detection threshold is determined according to the following two conditions:
if the detection statistic
Figure 641461DEST_PATH_IMAGE064
Greater than or equal to the detection threshold
Figure 605000DEST_PATH_IMAGE065
If yes, judging that the target exists;
if the detection statistic
Figure 654996DEST_PATH_IMAGE066
Less than the detection threshold
Figure 255742DEST_PATH_IMAGE067
Then the target is determined to be absent.
8. An interference suppression before adaptive detection system in a complex electromagnetic environment, comprising:
the data matrix construction module is used for constructing a signal matrix, an interference matrix, a data vector to be detected and a training sample matrix;
an interference rejection matrix construction module, configured to construct an interference rejection matrix;
the interference component suppression module is used for suppressing interference components in the signal matrix, the data vector to be detected and the training sample matrix;
the detection statistic constructing module is used for constructing detection statistics;
the detection threshold calculation module is used for determining a detection threshold by using the false alarm probability set by the system and the system parameters;
and the target judgment module is used for comparing the detection statistic with the detection threshold, judging that the target exists if the detection statistic is larger than the detection threshold, and otherwise judging that the target does not exist.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113267758A (en) * 2021-07-16 2021-08-17 中国人民解放军空军预警学院 Target detection method and system in presence of interference in composite Gaussian environment
CN113391281A (en) * 2021-05-12 2021-09-14 中国人民解放军空军预警学院 Method and system for detecting polarization radar weighted extension target in signal mismatch
CN113567931A (en) * 2021-09-28 2021-10-29 中国人民解放军空军预警学院 Method and system for detecting double subspace signals when training samples are insufficient

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2011146990A (en) * 2011-11-18 2013-05-27 Открытое акционерное общество "НИИ измерительных приборов - Новосибирский завод имени Коминтерна" (ОАО "НПО НИИИП-НЗиК") METHOD FOR STABILIZING A FALSE ALARM PROBABILITY AND A DEVICE FOR ITS IMPLEMENTATION
CN104133195A (en) * 2014-07-23 2014-11-05 成都天奥信息科技有限公司 Sea wave interference suppression method
CN109407083A (en) * 2018-12-19 2019-03-01 中国人民解放军空军预警学院 The Weighted adaptive detector that a kind of pair of mismatch subspace signal flexibly controls
CN109444820A (en) * 2018-12-13 2019-03-08 中国人民解放军空军预警学院 Object detection method after Multichannel radar elder generation AF panel when clutter and interference co-existence
CN110988831A (en) * 2019-04-20 2020-04-10 中国人民解放军空军预警学院 Parameter adjustable detector for signal mismatch in clutter and interference coexistence environment

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2011146990A (en) * 2011-11-18 2013-05-27 Открытое акционерное общество "НИИ измерительных приборов - Новосибирский завод имени Коминтерна" (ОАО "НПО НИИИП-НЗиК") METHOD FOR STABILIZING A FALSE ALARM PROBABILITY AND A DEVICE FOR ITS IMPLEMENTATION
CN104133195A (en) * 2014-07-23 2014-11-05 成都天奥信息科技有限公司 Sea wave interference suppression method
CN109444820A (en) * 2018-12-13 2019-03-08 中国人民解放军空军预警学院 Object detection method after Multichannel radar elder generation AF panel when clutter and interference co-existence
CN109407083A (en) * 2018-12-19 2019-03-01 中国人民解放军空军预警学院 The Weighted adaptive detector that a kind of pair of mismatch subspace signal flexibly controls
CN110988831A (en) * 2019-04-20 2020-04-10 中国人民解放军空军预警学院 Parameter adjustable detector for signal mismatch in clutter and interference coexistence environment

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113391281A (en) * 2021-05-12 2021-09-14 中国人民解放军空军预警学院 Method and system for detecting polarization radar weighted extension target in signal mismatch
CN113267758A (en) * 2021-07-16 2021-08-17 中国人民解放军空军预警学院 Target detection method and system in presence of interference in composite Gaussian environment
CN113267758B (en) * 2021-07-16 2021-09-24 中国人民解放军空军预警学院 Target detection method and system in presence of interference in composite Gaussian environment
CN113567931A (en) * 2021-09-28 2021-10-29 中国人民解放军空军预警学院 Method and system for detecting double subspace signals when training samples are insufficient

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