CN109597034A - A kind of space-time adaptive processing method based on Euclidean distance - Google Patents
A kind of space-time adaptive processing method based on Euclidean distance Download PDFInfo
- Publication number
- CN109597034A CN109597034A CN201811521071.0A CN201811521071A CN109597034A CN 109597034 A CN109597034 A CN 109597034A CN 201811521071 A CN201811521071 A CN 201811521071A CN 109597034 A CN109597034 A CN 109597034A
- Authority
- CN
- China
- Prior art keywords
- unit
- distance
- angle
- space
- time adaptive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a kind of space-time adaptive processing methods based on Euclidean distance, and steps are as follows: handling the echo-signal of high frequency mixing day ground wave radar, obtain distance-speed-angle three-dimensional data block；The covariance of the corresponding local unit to be processed of each distance unit is calculated, covariance data block is constituted；The corresponding local processing unit of the corresponding distance unit of several minimum values is selected in Euclidean distance array as training sample data block；The adaptive weight vector for calculating distance to a declared goal unit obtains space-time adaptive treated output result；All distance unit interested are traversed, all distance unit output result of specified doppler cells and angle-unit is obtained；All doppler cells interested and angle-unit are traversed, is obtained by space-time adaptive treated distance-speed-angle three-dimensional data result.The present invention is able to suppress sea clutter information and ionospheric clutter information in high frequency mixing day earthwave radar return information.
Description
Technical field
The invention belongs to high frequency mixing day earthwave radar clutter suppression technology fields, are related to a kind of for radar clutter inhibition
Space-time adaptive Processing Algorithm.
Background technique
High frequency mixing day ground wave radar is a kind of new system radar, emits signal along sky wave Mode Launch, by ionosphere
Reflection, echo-signal are received by ground wave mode along oversea propagation.This new system radar combines the long of folded Clutter in Skywave Radars and visits
The advantages of ranging is from detection performance high with ground wave radar, while improving survival ability.In recent years, this New System has attracted greatly
The concern of amount.And the problem of one of most serious is clutter, one including ionospheric clutter and the broadening for being ionized layer pollution
Rank sea clutter.It can be submerged in the target of the low-speed motion near the peak Bragg in the clutter of Doppler's peacekeeping azimuth dimension extension, caused
Target can not be detected.
Space-time adaptive processing is to carry out a main method of clutter recognition.It is mainly used in airborne radar,
Steady and non-stationary clutter is inhibited in Doppler-angle dimension.It is also applicable in many other fields, either army
With fields such as field or civil fields, such as spaceborne radar, communication, sonar, navigation.Also have in high frequency over the horizon radar field
Certain application, wherein local combination treatment method (JDL) is a kind of algorithm of effective solution clutter problem.Local connection
Closing processing method is that array element-pulse data based on two dimensional discrete Fourier transform input transforms to interested local angle-
Doppler frequency data, and then the method for acquiring the adaptive weight vector of dimensionality reduction.It is required to be distributed in training sample to be measured
Clutter sample be it is independent identically distributed, this is relatively difficult to achieve in high frequency mixing day ground wave radar.And the non-stationary of clutter can lead
The evaluated error to covariance matrix is caused, so that Clutter suppression algorithm performance declines.Therefore it is carried on the back in high frequency mixing day ground wave radar
Under scape, how to select training sample is a difficult point.
Summary of the invention
The present invention provides one kind and is based on to solve the problems, such as the clutter recognition under high frequency mixing day ground wave radar background
The space-time adaptive processing method of Euclidean distance.This method is for high frequency mixing day ground wave radar to ionospheric clutter, exhibition
Wide First-order sea clutter and other non-stationary clutters are inhibited, and can be improved letter miscellaneous noise ratio, increase Methods for Target Detection Probability.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of space-time adaptive processing method based on Euclidean distance, includes the following steps:
Step 1: the echo-signal of high frequency mixing day ground wave radar is carried out apart from processing, doppler processing and digital wave
Beam formation processing, obtains distance-speed-angle three-dimensional data block；
Step 2: selected local processing unit size, all distances for choosing specified doppler cells and angle-unit are single
Metadata constitutes three-dimensional data block to be processed, calculates the covariance of the corresponding local unit to be processed of each distance unit, constitutes
Covariance data block；
Step 3: the Europe between the covariance matrix of distance to a declared goal unit and the covariance matrix of other distance unit is calculated
Distance is obtained in several, is constituted Euclidean distance array, is selected the wherein corresponding local of the corresponding distance unit of several minimum values
Processing unit is as training sample data block；
Step 4: according to selected training sample data, the adaptive weight vector of distance to a declared goal unit is calculated, when obtaining sky
Output result after self-adaptive processing；
Step 5: all distance unit interested of traversal obtain all distances of specified doppler cells and angle-unit
Unit exports result；
Step 6: all doppler cells interested of traversal and angle-unit obtain that treated by space-time adaptive
Distance-speed-angle three-dimensional data result.
Compared with the prior art, the present invention has the advantage that
The present invention is able to suppress sea clutter information and ionospheric clutter letter in high frequency mixing day earthwave radar return information
Breath, improves the signal to noise ratio of target, is conducive to target detection and Track In Track, has and implements simple and convenient, can be adaptive change
The features such as weight.
Detailed description of the invention
Fig. 1 is the schematic illustration of space-time adaptive processing method of the present invention.
Fig. 2 is the result schematic diagram of step 1 in embodiment.
Fig. 3 is the result schematic diagram of step 6 in embodiment.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawing, and however, it is not limited to this, all to this
Inventive technique scheme is modified or replaced equivalently, and without departing from the spirit and scope of the technical solution of the present invention, should all be covered
Within the protection scope of the present invention.
The present invention provides a kind of space-time adaptive processing methods based on Euclidean distance, as shown in Figure 1, the side
Method includes the following steps:
Step 1: the echo-signal of high frequency mixing day ground wave radar is carried out apart from processing, doppler processing and digital wave
Beam formation processing, obtains distance-speed-angle three-dimensional data block.Specific step is as follows:
The echo-signal of high frequency mixing day ground wave radar passes through distance processing, and the range dimension of data is R；By Doppler
Processing, the speed dimension of data are D；It is handled by digital beam froming, the angle dimension of data is A.Obtained distance-speed
Degree-angle three-dimensional data block is { data }, and dimension is R × D × A.
Step 2: selected local processing unit size, all distances for choosing specified doppler cells and angle-unit are single
Metadata constitutes three-dimensional data block to be processed, calculates the covariance of the corresponding local unit to be processed of each distance unit, constitutes
Covariance data block.Specific step is as follows:
(1) selecting local processing unit size to be is etaD comprising doppler cells number and angle-unit number is etaA, then
To specified doppler cells d and specified angle unit θ, choosing the unit three-dimensional data block to be processed that all distance unit are constituted is
{ dataJDL }, dimension are R × etaD × etaA；
(2) covariance of the corresponding local unit to be processed of a certain distance unit r is calculated in accordance with the following methods: first will
dataJDLrColumn vector is carried out, a dimensional vector vJDL is obtainedr, the length is etaD × etaA；Then it calculates corresponding
Covariance matrix is Rr=vJDLr×vJDLr H, whereinHIndicate conjugate transposition；
(3) each distance unit is calculated, available covariance data block is { Rd×θ, dimension be R ×
(etaD×etaA)×(etaD×etaA)。
Step 3: the Europe between the covariance matrix of distance to a declared goal unit and the covariance matrix of other distance unit is calculated
Distance is obtained in several, is constituted Euclidean distance array, is selected the wherein corresponding local of the corresponding distance unit of several minimum values
Processing unit is as training sample data block.Specific step is as follows:
(1) selected distance unit r, corresponding covariance matrix are Rr, it is corresponding with other distance unit successively to calculate it
Covariance matrix RjBetween Euclidean distance:
RD (j)=| | Rr-Rj||F,
Wherein, | | | |FFor Frobenius norm, j=1,2 ... R；
(2) number that protection location is arranged is 2 × etaG, and removing position in RD (j) is j=r-etaG ..., r-
1, r, r+1 ..., data when r+etaG obtain Euclidean distance array RD (j), j ≠ r- as protection location
etaG,...,r-1,r,r+1,...,r+etaG；
(3) selecting the smallest by 2 in RD (j) × corresponding position array of (etaD × etaA) a data is Rloca (n),
Middle n=1,2 ..., 2 × (etaD × etaA)；
(4) in unit three-dimensional data block { dataJDL } to be processed, the data of r ∈ Rloca distance unit is selected to constitute
Training sample data block { dataTra }.
Step 4: according to selected training sample data, the adaptive weight vector of distance to a declared goal unit is calculated, when obtaining sky
Output result after self-adaptive processing.Specific step is as follows:
(1) covariance matrix of the clutter of selected distance unit r is calculated by training sample:
(2) steering vector is when local skyT is transformation matrix, steering vector when v is empty, in which:
The Kronecker direct product of two vectors is represented, the doppler cells and angle-unit that d and θ are respectively specified,For the time domain steering vector of local processing unit,
For the airspace steering vector of local processing unit,
NPluse is correlative accumulation periodicity, and nCh is array channel number, and dCh is array spacings, and λ is transmitting signal wavelength,
fJDLFor the corresponding Doppler frequency range of local processing unit, θJDLFor the corresponding angular range of local processing unit；
fRFor pulse recurrence frequency, fdTo specify Doppler
The corresponding Doppler frequency of unit；
(3) according to output Signal to Interference plus Noise Ratio maximal criterion, weight vector is when obtaining optimal skyThen selected distance
Space-time adaptive processing output result at unit r is dataOut (r, d, θ)=wXJDL(r, d, θ), wherein XJDL(r,d,θ)
For the local processing unit data dataJDL at selected distance unit rrColumn vector data block, dimension be (etaD ×
etaA)×1。
Step 5: all distance unit interested of traversal obtain all distances of specified doppler cells and angle-unit
Unit exports result.Specific step is as follows:
Enable r=1,2 ..., R calculates the corresponding dataOut of each distance unit (r, d, θ), obtains specified how general
Strangle corresponding space-time adaptive processing output result dataOut (d, θ) of unit d and specified angle unit θ.
Step 6: all doppler cells interested of traversal and angle-unit obtain that treated by space-time adaptive
Distance-speed-angle three-dimensional data result.Specific step is as follows:
(1) d=1 is enabled, 2 ..., D calculates the empty Shi Zishi of the corresponding all distance unit of each doppler cells
Output result dataOut (θ) should be handled；
(2) θ=1 is enabled, 2 ..., A calculates the corresponding all distance unit of each angle-unit and Doppler's door
Space-time adaptive processing output result dataOut.
Embodiment:
Step 1: set high frequency mixing day ground wave radar echo-signal pass through distance processing data range dimension as
200, the speed dimension by the data of doppler processing is 309, by the angle dimension for the data that digital beam froming is handled
It is 31, obtained distance-speed-angle three-dimensional data block is { data }, and dimension is 200 × 309 × 31.Draw the 16th angle
Distance-hodograph of unit, as shown in Figure 2.
Step 2: selected local processing unit size: comprising doppler cells number be 3 and angle-unit number is 3, then to finger
Determine doppler cells 186 and specified angle unit 16, choosing the unit three-dimensional data block to be processed that all distance unit are constituted is
{ dataJDL }, dimension are 200 × 3 × 3；It calculates the covariance of the corresponding local unit to be processed of the 48th distance unit: first will
dataJDLrColumn vector is carried out, a dimensional vector vJDL is obtainedr, the length is 3 × 3；Then corresponding covariance is calculated
Matrix isWhereinHIndicate conjugate transposition；Each distance unit is calculated, it is available
Covariance data block is { R186×16, dimension is 200 × 9 × 9.
Step 3: selected 48th distance unit, corresponding covariance matrix are R48, it is single with other distances successively to calculate it
The corresponding covariance matrix R of memberj, j=1,2 ..., the Euclidean distance between 200 are as follows:
RD (j)=| | R48-Rj||F。
Wherein, | | | | it is Frobenius norm, λk, k=1,2 ..., m is matrixM be not
0 characteristic value.
The number that protection location is arranged is 2, and in RD (j), removing position is j=47, data when 48,49, as
Protection location obtains Euclidean distance array RD (j), j ≠ 47,48,49.
Selecting the corresponding position array of the smallest 18 data in RD (j) is Rloca (n), wherein n=1,2 ...,
18, it is as shown in table 1 in the training sample position of the 48th distance unit selection.
Table 1
In unit three-dimensional data block { dataJDL } to be processed, the data of r ∈ Rloca distance unit is selected to constitute instruction
Practice sample data block { dataTra }.
Step 4: the covariance matrix of the clutter of the 48th distance unit is calculated by training sample are as follows:
Steering vector is when local skyWherein, T is transformation matrix, steering vector when v is empty, in which:
Represent the Kronecker of two vectors
Direct product, specified doppler cells are Unit the 186th, and specified angle-unit is Unit the 16th,
For the time domain steering vector of local processing unit,For the sky of local processing unit
Domain steering vector, wherein nPluse is correlative accumulation periodicity, and nCh is array channel number, and dCh is array spacings, and λ is transmitting
Signal wavelength, fJDLFor the corresponding Doppler frequency range of local processing unit, θJDLFor the corresponding angle model of local processing unit
It encloses.
Wherein, fRFor pulse recurrence frequency, fdIt is specified
The corresponding Doppler frequency of doppler cells.
By output Signal to Interference plus Noise Ratio maximal criterion, weight vector when optimal sky can be obtained are as follows:
Then select the 48th distance unit at space-time adaptive processing output result be dataOut (48,186,16)=
w48·XJDL(48,186,16)。
Step 5: enabling r=1, and 2 ..., 200, the corresponding dataOut of each distance unit (r, 186,16) is calculated,
Obtain specified 186th doppler cells space-time adaptive processing output result dataOut corresponding with specified 16th angle-unit
(186,16)。
Step 6: enabling d=1, and 2 ..., 309, calculate the sky of the corresponding all distance unit of each doppler cells
When self-adaptive processing output result dataOut (θ)；Enable θ=1,2 ..., 31, calculate the corresponding institute of each angle-unit
There is the space-time adaptive processing output result dataOut of distance unit and Doppler's door.
Distance-hodograph of the 16th angle-unit is drawn, as shown in Figure 3.
It can see by the comparison of Fig. 2 and Fig. 3, sea clutter and ionospheric clutter have obtained effective inhibition, and target appears, letter
Miscellaneous ratio is greatly improved.
Claims (9)
1. a kind of space-time adaptive processing method based on Euclidean distance, it is characterised in that the method includes walking as follows
It is rapid:
Step 1: the echo-signal of high frequency mixing day ground wave radar is carried out apart from processing, doppler processing and digital beam shape
At processing, distance-speed-angle three-dimensional data block is obtained；
Step 2: selected local processing unit size chooses all distance unit numbers of specified doppler cells and angle-unit
According to constituting three-dimensional data block to be processed, calculate the covariance of the corresponding local unit to be processed of each distance unit, constitute association side
Difference data block；
Step 3: calculate distance to a declared goal unit covariance matrix and other distance unit covariance matrix between Europe it is several in
Distance is obtained, Euclidean distance array is constituted, selects the corresponding local processing of the wherein corresponding distance unit of several minimum values
Unit is as training sample data block；
Step 4: according to selected training sample data, the adaptive weight vector of distance to a declared goal unit is calculated, sky Shi Zishi is obtained
Answer treated to export result；
Step 5: all distance unit interested of traversal obtain all distance unit of specified doppler cells and angle-unit
Export result；
Step 6: all doppler cells interested of traversal and angle-unit are obtained by space-time adaptive treated distance-
Speed-angle three-dimensional data result.
2. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 1:
The echo-signal of high frequency mixing day ground wave radar passes through distance processing, and the range dimension of data is R；At Doppler
Reason, the speed dimension of data are D；It is handled by digital beam froming, the angle dimension of data is A；Obtained distance-speed-
Angle three-dimensional data block is { data }, and dimension is R × D × A.
3. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 2:
(1) selecting local processing unit size to be is etaD comprising doppler cells number and angle-unit number is etaA, then to finger
Determine doppler cells d and specified angle unit θ, choosing the unit three-dimensional data block to be processed that all distance unit are constituted is
{ dataJDL }, dimension are R × etaD × etaA；
(2) covariance of the corresponding local unit to be processed of a certain distance unit r is calculated in accordance with the following methods: first by dataJDLr
Column vector is carried out, a dimensional vector vJDL is obtainedr, the length is etaD × etaA；Then corresponding covariance is calculated
Matrix isWhereinHIndicate conjugate transposition；
(3) each distance unit is calculated, obtaining covariance data block is { Rd×θ, dimension be R × (etaD ×
etaA)×(etaD×etaA)。
4. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 3:
(1) selected distance unit r, corresponding covariance matrix are Rr, successively calculate its association side corresponding with other distance unit
Poor matrix RjBetween Euclidean distance:
RD (j)=| | Rr-Rj||F,
Wherein, | | | |FFor Frobenius norm, j=1,2 ... R；
(2) number that protection location is arranged is 2 × etaG, and removing position in RD (j) is j=r-etaG ..., r-1, r, r
Data when+1 ..., r+etaG obtain Euclidean distance array RD (j), j ≠ r-etaG ..., r- as protection location
1,r,r+1,...,r+etaG；
(3) selecting the smallest by 2 in RD (j) × corresponding position array of (etaD × etaA) a data is Rloca (n), wherein n=
1,2,......,2×(etaD×etaA)；
(4) in unit three-dimensional data block { dataJDL } to be processed, the data composing training of r ∈ Rloca distance unit is selected
Sample data block { dataTra }.
5. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 4:
(1) covariance matrix of the clutter of selected distance unit r is calculated by training sample:
N=2 × (etaD × etaA)；
(2) steering vector is when local skyT is transformation matrix, steering vector when v is empty；
(3) according to output Signal to Interference plus Noise Ratio maximal criterion, weight vector is when obtaining optimal skyThen selected distance unit r
The space-time adaptive processing output result at place is dataOut (r, d, θ)=wXJDL(r, d, θ), wherein XJDL(r, d, θ) is choosing
Local processing unit data dataJDL at set a distance unit rrColumn vector data block, dimension be (etaD ×
etaA)×1。
6. the space-time adaptive processing method according to claim 5 based on Euclidean distance, it is characterised in that described
The calculation formula of transformation matrix T is as follows:
The Kronecker direct product of two vectors is represented, the doppler cells and angle-unit that d and θ are respectively specified,For the time domain steering vector of local processing unit,
For the airspace steering vector of local processing unit, nPluse is correlative accumulation periodicity, and nCh is array channel number, and dCh is array
Interval, λ are transmitting signal wavelength, fJDLFor the corresponding Doppler frequency range of local processing unit, θJDLFor local processing unit
Corresponding angular range.
7. the space-time adaptive processing method according to claim 5 based on Euclidean distance, it is characterised in that described
The calculation formula of steering vector v is as follows when empty:
fRFor pulse recurrence frequency, fdTo specify the corresponding Doppler frequency of doppler cells.
8. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 5:
Enable r=1,2 ..., R calculates the corresponding dataOut of each distance unit (r, d, θ), and it is single to obtain specified Doppler
Corresponding space-time adaptive processing output result dataOut (d, θ) of first d and specified angle unit θ.
9. the space-time adaptive processing method according to claim 1 based on Euclidean distance, it is characterised in that described
Specific step is as follows for step 6:
(1) d=1 is enabled, 2 ..., D, at the space-time adaptive for calculating the corresponding all distance unit of each doppler cells
Reason output result dataOut (θ)；
(2) θ=1 is enabled, 2 ..., A, when calculating the sky of the corresponding all distance unit of each angle-unit and Doppler's door
Self-adaptive processing exports result dataOut.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811521071.0A CN109597034B (en) | 2018-12-12 | 2018-12-12 | Space-time adaptive processing method based on Euclidean distance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811521071.0A CN109597034B (en) | 2018-12-12 | 2018-12-12 | Space-time adaptive processing method based on Euclidean distance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109597034A true CN109597034A (en) | 2019-04-09 |
| CN109597034B CN109597034B (en) | 2021-08-31 |
Family
ID=65961596
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811521071.0A Active CN109597034B (en) | 2018-12-12 | 2018-12-12 | Space-time adaptive processing method based on Euclidean distance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109597034B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110133603A (en) * | 2019-06-27 | 2019-08-16 | 哈尔滨工业大学 | High-frequency ground wave radar ocean clutter cancellation method based on rooting Euclidean geometry center of gravity |
| CN111308436A (en) * | 2020-02-24 | 2020-06-19 | 清华大学 | Radar space-time adaptive processing method and device based on volume correlation function |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6252540B1 (en) * | 1999-12-21 | 2001-06-26 | The United States Of America As Represented By The Secretary Of The Air Force | Apparatus and method for two stage hybrid space-time adaptive processing in radar and communication systems |
| CN103744067A (en) * | 2014-01-15 | 2014-04-23 | 西安电子科技大学 | Non-adaptive airborne non-side-looking radar short-range clutter suppression method |
| CN104155632A (en) * | 2014-07-18 | 2014-11-19 | 南京航空航天大学 | Improved subspace sea clutter suppression method based on local correlation |
| CN104215939A (en) * | 2014-10-10 | 2014-12-17 | 北京航空航天大学 | Knowledge assisted space-time adaptive processing method integrating generalized symmetrical structure information |
| CN105785339A (en) * | 2016-03-21 | 2016-07-20 | 西安电子科技大学 | Airborne radar clutter covariance matrix estimation method in inhomogeneous clutter environment |
| CN106125039A (en) * | 2016-06-14 | 2016-11-16 | 河海大学 | Improvement space-time adaptive Monopulse estimation method based on local Combined Treatment |
| CN106680783A (en) * | 2016-12-29 | 2017-05-17 | 西安电子科技大学 | Method for withstanding false targets on basis of station's position error fusion algorithm |
| CN106950546A (en) * | 2017-03-22 | 2017-07-14 | 西安电子科技大学 | The non-homogeneous clutter suppression method weighted again based on mahalanobis distance |
| CN107315169A (en) * | 2017-07-02 | 2017-11-03 | 中国航空工业集团公司雷华电子技术研究所 | Clutter covariance matrix method of estimation based on second-order statistic similarity |
| CN107703490A (en) * | 2017-09-29 | 2018-02-16 | 西安电子科技大学 | Range ambiguity clutter suppression method based on FDA MIMO radars |
| CN108872948A (en) * | 2018-08-01 | 2018-11-23 | 哈尔滨工业大学 | A kind of high-frequency ground wave radar ionospheric clutter suppressing method |
-
2018
- 2018-12-12 CN CN201811521071.0A patent/CN109597034B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6252540B1 (en) * | 1999-12-21 | 2001-06-26 | The United States Of America As Represented By The Secretary Of The Air Force | Apparatus and method for two stage hybrid space-time adaptive processing in radar and communication systems |
| CN103744067A (en) * | 2014-01-15 | 2014-04-23 | 西安电子科技大学 | Non-adaptive airborne non-side-looking radar short-range clutter suppression method |
| CN104155632A (en) * | 2014-07-18 | 2014-11-19 | 南京航空航天大学 | Improved subspace sea clutter suppression method based on local correlation |
| CN104215939A (en) * | 2014-10-10 | 2014-12-17 | 北京航空航天大学 | Knowledge assisted space-time adaptive processing method integrating generalized symmetrical structure information |
| CN105785339A (en) * | 2016-03-21 | 2016-07-20 | 西安电子科技大学 | Airborne radar clutter covariance matrix estimation method in inhomogeneous clutter environment |
| CN106125039A (en) * | 2016-06-14 | 2016-11-16 | 河海大学 | Improvement space-time adaptive Monopulse estimation method based on local Combined Treatment |
| CN106680783A (en) * | 2016-12-29 | 2017-05-17 | 西安电子科技大学 | Method for withstanding false targets on basis of station's position error fusion algorithm |
| CN106950546A (en) * | 2017-03-22 | 2017-07-14 | 西安电子科技大学 | The non-homogeneous clutter suppression method weighted again based on mahalanobis distance |
| CN107315169A (en) * | 2017-07-02 | 2017-11-03 | 中国航空工业集团公司雷华电子技术研究所 | Clutter covariance matrix method of estimation based on second-order statistic similarity |
| CN107703490A (en) * | 2017-09-29 | 2018-02-16 | 西安电子科技大学 | Range ambiguity clutter suppression method based on FDA MIMO radars |
| CN108872948A (en) * | 2018-08-01 | 2018-11-23 | 哈尔滨工业大学 | A kind of high-frequency ground wave radar ionospheric clutter suppressing method |
Non-Patent Citations (5)
| Title |
|---|
| XAOMING LI 等: "Dimension-Reduced Space-Time Adaptive Clutter Suppression Algorithm Based on Lower-Rank Approximation to Weight Matrix in Airborne Radar", 《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 * |
| XIN ZHANG 等: "Weak Target Detection within the Nonhomogeneous Ionospheric Clutter Background of HFSWR Based on STAP", 《HINDAWI PUBLISHING CORPORATION INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION》 * |
| 张鑫: "STAP方法在高频雷达中的应用研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 衣春雷: "船载高频地波雷达海杂波抑制方法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
| 韩素丹: "STAP中基于知识的杂波协方差矩阵估计技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110133603A (en) * | 2019-06-27 | 2019-08-16 | 哈尔滨工业大学 | High-frequency ground wave radar ocean clutter cancellation method based on rooting Euclidean geometry center of gravity |
| CN111308436A (en) * | 2020-02-24 | 2020-06-19 | 清华大学 | Radar space-time adaptive processing method and device based on volume correlation function |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109597034B (en) | 2021-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102156279B (en) | Method for detecting moving target on ground by utilizing bistatic radar based on MIMO (Multiple Input Multiple Output) | |
| CN103901395B (en) | Coherent signal direction of arrival Dynamic Tracking under a kind of impulsive noise environment | |
| CN103728607A (en) | Space time code three-dimensional self-adaptation clutter cancelling method for onboard multiple input multiple output (MIMO) radar | |
| CN103901410B (en) | Airborne bistatic MIMO radar clutter suppression method based on sparse recovery | |
| CN105738879A (en) | Radar clutter time space adaptive pre-filtering method based on sparse recovery | |
| CN107942314B (en) | Doppler through-wall radar positioning method based on LASSO feature extraction | |
| CN103383449A (en) | ESPRIT algorithm based short-range clutter suppression method for airborne radar | |
| CN109814070B (en) | Distance fuzzy clutter suppression method based on auxiliary pulse | |
| CN110824428A (en) | Vertical vector array underwater sound ray matching passive positioning method | |
| CN109765529B (en) | Millimeter wave radar anti-interference method and system based on digital beam forming | |
| CN104502904B (en) | Torpedo homing beam sharpening method | |
| CN115963457B (en) | Multichannel passive radar direct wave interference suppression method | |
| CN107290732A (en) | A kind of single base MIMO radar direction-finding method of quantum huge explosion | |
| CN104360355A (en) | Anti-interference method and device | |
| CN110376560A (en) | A kind of airborne bistatic MIMO radar amplitude and phase error correction method based on single range gate | |
| CN109597034A (en) | A kind of space-time adaptive processing method based on Euclidean distance | |
| CN110646765B (en) | Riemann distance-based generalized sidelobe cancellation algorithm | |
| CN113376607A (en) | Airborne distributed radar small sample space-time adaptive processing method | |
| CN108490425B (en) | Angle measuring method of bistatic MIMO radar | |
| CN106093871B (en) | Smart antenna Mutual coupling system and method based on empirical mode decomposition | |
| CN110109077A (en) | MIMO radar based on time reversal is concerned with angle estimating method | |
| CN108896963B (en) | Airborne radar space-time self-adaptive dimension reduction processing method | |
| CN103809164B (en) | A kind of rear Doppler's optimum clutter suppression method of reconstructed reference passage | |
| CN103760540A (en) | Moving target detection and parameter estimation method based on reconstructed signals and 1-norm | |
| CN105572642B (en) | A kind of space-time adaptive processing method based on two level frameworks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |