CN108680907A - A kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix - Google Patents
A kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix Download PDFInfo
- Publication number
- CN108680907A CN108680907A CN201810392028.2A CN201810392028A CN108680907A CN 108680907 A CN108680907 A CN 108680907A CN 201810392028 A CN201810392028 A CN 201810392028A CN 108680907 A CN108680907 A CN 108680907A
- Authority
- CN
- China
- Prior art keywords
- matrix
- angle
- wave beam
- mimo radar
- passband
- 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
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 compressed sensing MIMO radar suppressing method based on observing matrix, including the construction of the equivalent ideal wave beam of compressed sensing MIMO radar, the solution with general formula beam forming coefficients vector and the AF panel observation for receipt signal matrix；Wherein：Equivalent ideal wave beam is constructed for solving Wave beam forming sparse vector；Band general formula beam forming coefficients vector is solved for constructing spatial domain AF panel observing matrix；The AF panel observation of receipt signal matrix is used to, by equivalent wave beam, inhibit the interference outside angular interval interested.The angle that the present invention is designed from observing matrix realizes inhibition of the compressed sensing radar to being interfered outside angular interval interested, significantly improves the detection performance of compressed sensing radar under jamming pattern by designing the observing matrix of specific structure.
Description
Technical field
Present invention relates particularly to a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix.
Background technology
Presently, there are the clutter suppression method based on Capon Wave beam formings, the covariance matrix of noise and clutter
In the case of knowing, targeted beam forming coefficients are acquired using the angle prior information of target, by beam forming coefficients structure
At observing matrix, by observing matrix so that compressed sensing MIMO radar carries out Voice segment to angle where target, realization removes
The inhibition of noise and clutter other than target.However, in practical radar system, the prior information of target is often inaccurate or inadequate
Accurately, in this case, clutter suppression method will be unable to ensure the accurate estimation of target information.
Invention content
Goal of the invention：In order to overcome the deficiencies in the prior art, a kind of design realization using observing matrix is provided
The compressed sensing MIMO radar based on observing matrix that compressed sensing MIMO radar inhibits the insufficient interference of prior information
Disturbance restraining method.
Technical solution：To achieve the above object, the present invention provides a kind of compressed sensing MIMO radar based on observing matrix
Suppressing method, including the construction of the equivalent ideal wave beam of compressed sensing MIMO radar, the solution with general formula beam forming coefficients vector
And it is observed for the AF panel of receipt signal matrix；Wherein：It is sparse for solving Wave beam forming to construct equivalent ideal wave beam
Vector；Band general formula beam forming coefficients vector is solved for constructing spatial domain AF panel observing matrix；To receipt signal matrix
AF panel observation is for by equivalent wave beam, inhibiting the interference outside angular interval interested.
It includes the following steps：
1) Ω is enabled to represent the interested angular sector of CS-MIMO radars, i.e. angle passband,Angle stopband is represented, that is, is felt
Angular range outside interest sector, the two meetAnd it is built according to angle passband and the division of angle stopband
Found the corresponding equivalent wave beam of ideal of compressed sensing MIMO radar
2) it is w to solve band general formula beam forming coefficients vector, N number of element difference in the length of reception array number N, w
It acts in N number of reception array element, forms the equivalent wave beam of ideal for focusing on intended pass-band；
3) it is directed to the interested angle passband of P CS-MIMO radar, construction obtains spatial domain AF panel observing matrix W；
4) consider that receipt signal matrix are Y=[y1,y2,…,yN], then after obtaining spatial domain AF panel observing matrix W,
By W effects and receipt signal matrix Y and obtain Wave beam forming data matrix
5) Wave beam forming data matrix is solvedVectorization data vectorUsing regularization orthogonal matching pursuit algorithm
ROMP solving-optimizing problemsS.t.y=Γ ' θ, obtain sparse vector θ, and wherein Γ ' is equivalent perception matrix after observation.
Further, the process that compressed sensing MIMO radar echo angular region divides is as follows：
1.1) it enablesWithThe angle in angle passband and in angle stopband is indicated respectively；
1.2) the ideal equivalent wave beam of constructionCharacteristicP and S is respectively angle
Passband and the walk-off angle number of degrees in angle stopband.
Further, it is as follows to solve the process with general formula beam forming coefficients vector：By passband formula beam forming coefficients
Design problem is summarized as following optimization problems.t.||wH||F≤ ε is whereinAll discrete angulars of representation spaceCorresponding reception steering vector matrix,
Wherein | | wH||F≤ ε limits the gain of background white noise during channel weighting, ensure that adding under random noise background
Weigh stability.Above-mentioned optimization problem is solved using the convex tool boxes optimization problem solving tool CVX, obtains optimal passband formula wave beam
The efficiency of formation vector w.
Further, based on beam forming coefficients vector construct spatial domain AF panel observing matrix process be specially：It enables
wpExpression makes CS-MIMO radar receiving array directional diagrams focus on p-th of angle passband ΩpOn passband formula beam forming coefficients
Vector, it is assumed that share the interested angle passband of P CS-MIMO radar, then can obtain spatial domain AF panel observing matrix W=
[w1,w2,…wP]。
Further, the process for receipt signal matrix being carried out with AF panel observation is specially to consider that receipt signal matrix are
Y=[y1,y2,…,yN], then after obtaining spatial domain AF panel observing matrix W, by W effects with receipt signal matrix Y and obtain
Wave beam forming data matrixWherein W*For the conjugate transposition of W.
Further, the vectorization data matrix of Wave beam forming data matrix is constructed, specially：
It is vectorization function to enable y=vec (Y), wherein vec (), constructs vectorization data matrixWherein Γ is the sparse dictionary of y, ILIt is the unit matrix of L × L for dimension.
Advantageous effect：Compared with prior art, the present invention in view of observing matrix pair in compressed sensing MIMO radar system
The weighting treatment mechanism of each receiving channel is designed based on structuring observing matrix, and the insufficient feelings of prior information are interfered for spatial domain
Condition, for the spatial domain interference for inhibiting compressed sensing MIMO radar to be faced, the angle designed from observing matrix is special by designing
Determine the observing matrix of structure, realizes inhibition of the compressed sensing radar to being interfered outside angular interval interested, significantly improve dry
Disturb the detection performance of compressed sensing radar under background.
Description of the drawings
Fig. 1 is compressed sensing radar echo pulse grouping accumulation flow diagram；
Fig. 2 is pulse accumulation observing matrix design cycle schematic diagram；
Fig. 3 is that the compressed sensing radar pulse of binding signal compression sampling accumulates schematic diagram data.
Specific implementation mode
In the following with reference to the drawings and specific embodiments, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate
It the present invention rather than limits the scope of the invention, after having read the present invention, those skilled in the art are to of the invention each
The modification of kind equivalent form falls within the application range as defined in the appended claims.
As shown in Fig. 1~Fig. 2, the present invention provides a kind of observing matrix by designing specific structure, realizes compressed sensing
Compressed sensing MIMO radar disturbance restraining method based on observing matrix of the radar to the inhibition interfered outside angular interval interested,
Generally comprise three parts：The construction of the equivalent ideal wave beam of compressed sensing MIMO radar, with general formula beam forming coefficients vector
It solves and is observed for the AF panel of receipt signal matrix.
Ω is enabled to represent the interested angular sector of CS-MIMO radars, i.e. angle passband,Angle stopband is represented, that is, feels emerging
Angular range outside interesting sector.The two meets following relationship
WithThe angle in angle passband and in angle stopband is indicated respectively, it is assumed that CS-MIMO radars need to obtain one
A equivalent wave beam of idealIts characteristic can be described as
Wherein P and S is respectively the walk-off angle number of degrees of angle passband and angle stopband.Assuming that passband formula beam forming coefficients to
Amount is w, and N number of element in the length of reception array number N, w is respectively acting in N number of reception array element, and formation focuses on target
The equivalent wave beam of ideal of passband.According to above definition, the design problem of passband formula beam forming coefficients can be summarized as
Under optimization problem
Wherein
The corresponding reception steering vector matrix of all discrete angulars of representation space, and | | wH||E≤ ε limits channel weighting
The gain of background white noise in the process ensure that the weighting stability under random noise background.
Optimization problem shown in formula (3) is a Second-order cone programming problem, and interior point method generally may be used and solve to obtain most
Excellent solution.This section solves above-mentioned optimization problem using the convex tool boxes optimization problem solving tool CVX, obtains optimal passband formula wave beam
The efficiency of formation vector w.
Enable wpExpression makes CS-MIMO radar receiving array directional diagrams focus on p-th of angle passband ΩpOn passband formula wave
Beam the efficiency of formation vector, it is assumed that share the interested angle passband of P CS-MIMO radar, then can obtain spatial domain AF panel
Observing matrix
W=[w1,w2,…wP] (5)
Consideration receipt signal matrix are Y=[y1,y2,…,yN], then after obtaining spatial domain AF panel observing matrix W, by W
Effect and receipt signal matrix Y simultaneously obtain Wave beam forming data matrix
It enablesFormula (5.39) can be rewritten as
As previously mentioned, being directed to formula (7), by the following optimization problem of solution, sparse vector θ can be obtained with Optimization Solution,
To obtain the estimation of target angle.
Above-mentioned optimization problem is solved using regularization orthogonal matching pursuit algorithm (ROMP), what is obtained at this time is sparse
Vector has no longer included that corresponding sparse coefficient is interfered in spatial domain, as shown in figure 3, the target prior information needed is no longer accurate
Target angle information, but angular interval information where target, thus realize the inhibition to unknown disturbances, so far, just complete
The spatial domain AF panels of CS-MIMO radar receiving arrays.
Claims (6)
1. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix, it is characterised in that：Include the following steps：
1) Ω is enabled to represent the interested angular sector of CS-MIMO radars, i.e. angle passband,Represent angle stopband, i.e., it is interested
Angular range outside sector, the two meetAnd it is established and is pressed according to the division of angle passband and angle stopband
The corresponding equivalent wave beam of ideal of contracting perception MIMO radar
2) it is w to solve band general formula beam forming coefficients vector, and N number of element in the length of reception array number N, w acts on respectively
In in N number of reception array element, formation focuses on the equivalent wave beam of ideal of intended pass-band；
3) it is directed to the interested angle passband of P CS-MIMO radar, construction obtains spatial domain AF panel observing matrix W；
4) consider that receipt signal matrix are Y=[y1,y2,…,yN], then after obtaining spatial domain AF panel observing matrix W, W is made
With with receipt signal matrix Y and obtain Wave beam forming data matrix
5) Wave beam forming data matrix is solvedVectorization data vectorUsing regularization orthogonal matching pursuit algorithm ROMP
Solving-optimizing problemSparse vector θ is obtained, wherein Γ ' is equivalent perception matrix after observation.
2. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix according to claim 1, special
Sign is：Ideal equivalent wave beam in the step 1Foundation, specifically comprise the following steps：
1.1) it enablesWithThe angle in angle passband and in angle stopband is indicated respectively；
1.2) the ideal equivalent wave beam of constructionCharacteristicP and S is respectively angle passband
With the walk-off angle number of degrees in angle stopband.
3. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix according to claim 1, special
Sign is：The solution of beam forming coefficients in the step 2, specially：
The design problem of passband formula beam forming coefficients is summarized as following optimization problemWhereinAll discrete angulars of representation spaceCorresponding reception steering vector matrix, wherein | | wH||F≤ ε limits background white noise during channel weighting
Gain, ensure that the weighting stability under random noise background.It is asked using the convex tool boxes optimization problem solving tool CVX
Above-mentioned optimization problem is solved, optimal passband formula beam forming coefficients vector w is obtained.
4. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix according to claim 1, special
Sign is：The construction of spatial domain AF panel observing matrix in the step 3, specially：
Enable wpExpression makes CS-MIMO radar receiving array directional diagrams focus on p-th of angle passband ΩpOn passband formula wave beam shape
At coefficient vector, it is assumed that share the interested angle passband of P CS-MIMO radar, then can obtain spatial domain AF panel observation
Matrix W=[w1,w2,…wP]。
5. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix according to claim 1, special
Sign is：Receipt signal matrix are observed in the step 4 to obtain Wave beam forming data matrix, specially：
Consideration receipt signal matrix are Y=[y1,y2,…,yN], then after obtaining spatial domain AF panel observing matrix W, W is acted on
With receipt signal matrix Y and obtain Wave beam forming data matrixWherein W*For the conjugate transposition of W.
6. a kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix according to claim 1, special
Sign is：The vectorization data matrix of Wave beam forming data matrix is constructed in the step 5, specially：
It is vectorization function to enable y=vec (Y), wherein vec (), constructs vectorization data matrixWherein Γ is the sparse dictionary of y, ILIt is the unit matrix of L × L for dimension.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810392028.2A CN108680907B (en) | 2018-04-27 | 2018-04-27 | Compressive sensing MIMO radar interference suppression method based on observation matrix |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810392028.2A CN108680907B (en) | 2018-04-27 | 2018-04-27 | Compressive sensing MIMO radar interference suppression method based on observation matrix |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108680907A true CN108680907A (en) | 2018-10-19 |
| CN108680907B CN108680907B (en) | 2020-12-11 |
Family
ID=63801696
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810392028.2A Active CN108680907B (en) | 2018-04-27 | 2018-04-27 | Compressive sensing MIMO radar interference suppression method based on observation matrix |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108680907B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110895331A (en) * | 2019-08-21 | 2020-03-20 | 常熟理工学院 | Pulse Doppler radar target sparse detection method based on structured observation matrix |
| CN111551910A (en) * | 2020-05-18 | 2020-08-18 | 南京众博达电子科技有限公司 | UDP communication-based transmission method for compressed data of radar clutter background map |
| CN112526497A (en) * | 2020-06-23 | 2021-03-19 | 常熟理工学院 | Compressed sensing radar interference suppression sparse observation method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100246920A1 (en) * | 2009-03-31 | 2010-09-30 | Iowa State University Research Foundation, Inc. | Recursive sparse reconstruction |
| US20110175770A1 (en) * | 2009-06-30 | 2011-07-21 | Petros Boufounos | High Resolution SAR Imaging Using Non-Uniform Pulse Timing |
| CN103091673A (en) * | 2013-01-18 | 2013-05-08 | 西安交通大学 | Compressed sensing-before testing tracking method based on phased array radar system |
| CN103091669A (en) * | 2013-01-21 | 2013-05-08 | 中国民航大学 | Maneuvering target parameter estimation method based on compressed sensing |
| CN103399316A (en) * | 2013-07-22 | 2013-11-20 | 西安电子科技大学 | Weighting-based two-dimensional compressive sensing SAR (Synthetic Aperture Radar) imaging and moving target detection method |
| CN103840838A (en) * | 2014-03-19 | 2014-06-04 | 哈尔滨工业大学 | Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix |
| CN103886207A (en) * | 2014-03-27 | 2014-06-25 | 西安电子科技大学 | Nest multiple-input and multiple-output radar DOA estimating method based on compressed sensing |
| CN103941246A (en) * | 2014-05-14 | 2014-07-23 | 中国人民解放军国防科学技术大学 | Method for imaging and identification integration based on compressed sensing and target prior information |
| CN104793210A (en) * | 2015-04-21 | 2015-07-22 | 中国民航大学 | Compressed sensing based onboard phased array radar low-altitude wind shear wind speed estimation method |
| CN105652273A (en) * | 2016-03-17 | 2016-06-08 | 哈尔滨工程大学 | MIMO (Multiple Input Multiple Output) radar sparse imaging algorithm based on hybrid matching pursuit algorithm |
-
2018
- 2018-04-27 CN CN201810392028.2A patent/CN108680907B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100246920A1 (en) * | 2009-03-31 | 2010-09-30 | Iowa State University Research Foundation, Inc. | Recursive sparse reconstruction |
| US20110175770A1 (en) * | 2009-06-30 | 2011-07-21 | Petros Boufounos | High Resolution SAR Imaging Using Non-Uniform Pulse Timing |
| CN103091673A (en) * | 2013-01-18 | 2013-05-08 | 西安交通大学 | Compressed sensing-before testing tracking method based on phased array radar system |
| CN103091669A (en) * | 2013-01-21 | 2013-05-08 | 中国民航大学 | Maneuvering target parameter estimation method based on compressed sensing |
| CN103399316A (en) * | 2013-07-22 | 2013-11-20 | 西安电子科技大学 | Weighting-based two-dimensional compressive sensing SAR (Synthetic Aperture Radar) imaging and moving target detection method |
| CN103840838A (en) * | 2014-03-19 | 2014-06-04 | 哈尔滨工业大学 | Method for Bayes compressed sensing signal recovery based on self-adaptive measurement matrix |
| CN103886207A (en) * | 2014-03-27 | 2014-06-25 | 西安电子科技大学 | Nest multiple-input and multiple-output radar DOA estimating method based on compressed sensing |
| CN103941246A (en) * | 2014-05-14 | 2014-07-23 | 中国人民解放军国防科学技术大学 | Method for imaging and identification integration based on compressed sensing and target prior information |
| CN104793210A (en) * | 2015-04-21 | 2015-07-22 | 中国民航大学 | Compressed sensing based onboard phased array radar low-altitude wind shear wind speed estimation method |
| CN105652273A (en) * | 2016-03-17 | 2016-06-08 | 哈尔滨工程大学 | MIMO (Multiple Input Multiple Output) radar sparse imaging algorithm based on hybrid matching pursuit algorithm |
Non-Patent Citations (1)
| Title |
|---|
| YU TAO 等: "Spatial filter measurement matrix design for interference/jamming suppression in collocated comprehensive sensing MIMO radars", 《ELECTRONICS LETTERS》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110895331A (en) * | 2019-08-21 | 2020-03-20 | 常熟理工学院 | Pulse Doppler radar target sparse detection method based on structured observation matrix |
| CN111551910A (en) * | 2020-05-18 | 2020-08-18 | 南京众博达电子科技有限公司 | UDP communication-based transmission method for compressed data of radar clutter background map |
| CN111551910B (en) * | 2020-05-18 | 2022-05-13 | 南京众博达电子科技有限公司 | UDP communication-based radar clutter background map compressed data transmission method |
| CN112526497A (en) * | 2020-06-23 | 2021-03-19 | 常熟理工学院 | Compressed sensing radar interference suppression sparse observation method |
| CN112526497B (en) * | 2020-06-23 | 2022-04-08 | 常熟理工学院 | Compressed sensing radar interference suppression sparse observation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108680907B (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104730491B (en) | A kind of virtual array DOA estimation method based on L-type battle array | |
| CN106324558B (en) | Broadband signal DOA estimation method based on co-prime array | |
| CN107450047B (en) | Compressed sensing DOA estimation method based on unknown mutual coupling information under nested array | |
| CN110045323B (en) | Matrix filling-based co-prime matrix robust adaptive beamforming algorithm | |
| CN110113085B (en) | Wave beam forming method and system based on covariance matrix reconstruction | |
| CN107092007A (en) | A kind of Wave arrival direction estimating method of virtual second order array extension | |
| CN107561484B (en) | Direction-of-arrival estimation method based on interpolation co-prime array covariance matrix reconstruction | |
| CN108710102B (en) | Direction-of-arrival estimation method based on second-order equivalent virtual signal inverse discrete Fourier transform of co-prime array | |
| CN108680907A (en) | A kind of compressed sensing MIMO radar disturbance restraining method based on observing matrix | |
| CN104811867B (en) | Microphone array airspace filter method based on array virtual extended | |
| CN108872926A (en) | A kind of amplitude and phase error correction and DOA estimation method based on convex optimization | |
| CN107728112B (en) | Robust beam forming method under condition of serious mismatching of target steering vector | |
| CN108710758A (en) | The adaptive beam-forming algorithm reconstructed based on nested battle array and covariance matrix | |
| CN105335336A (en) | Sensor array steady adaptive beamforming method | |
| CN103885045A (en) | Sub-array division based circulation combined adaptive beam forming method | |
| CN105911527B (en) | Airborne radar space-time adaptive processing method based on EFA and MWF | |
| CN109116334A (en) | Sonar wave beams forming method and system based on super beam weighting | |
| CN108398659B (en) | Direction-of-arrival estimation method combining matrix beam and root finding MUSIC | |
| CN110196417B (en) | Bistatic MIMO radar angle estimation method based on emission energy concentration | |
| Deylami et al. | Iterative minimum variance beamformer with low complexity for medical ultrasound imaging | |
| CN106842135B (en) | Adaptive beamformer method based on interference plus noise covariance matrix reconstruct | |
| CN113593596B (en) | Robust self-adaptive beam forming directional pickup method based on subarray division | |
| CN113466782A (en) | Deep Learning (DL) -based cross-coupling correction D O A estimation method | |
| CN107783081A (en) | A kind of SSTMV minimum variance Beamforming Methods for wideband radar | |
| CN106443672B (en) | A kind of orientation multichannel SAR signal adaptive reconstructing method |
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 | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221109 Address after: 215500 No.9, research institute road, Changshu Economic and Technological Development Zone, Suzhou City, Jiangsu Province Patentee after: CHANGSHU RESEARCH INSTITUTE OF DLUT Co.,Ltd. Address before: 215500 Changshou City South Three Ring Road No. 99, Suzhou, Jiangsu Patentee before: CHANGSHU INSTITUTE OF TECHNOLOGY |