US20120062409A1 - Method for detecting targets using space-time adaptive processing and shared knowledge of the environment - Google Patents
Method for detecting targets using space-time adaptive processing and shared knowledge of the environment Download PDFInfo
- Publication number
- US20120062409A1 US20120062409A1 US12/879,288 US87928810A US2012062409A1 US 20120062409 A1 US20120062409 A1 US 20120062409A1 US 87928810 A US87928810 A US 87928810A US 2012062409 A1 US2012062409 A1 US 2012062409A1
- Authority
- US
- United States
- Prior art keywords
- target
- max
- test
- test signal
- signal
- 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
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000012545 processing Methods 0.000 title claims abstract description 8
- 238000012360 testing method Methods 0.000 claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims abstract description 35
- 238000012549 training Methods 0.000 claims abstract description 23
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 description 9
- 238000001514 detection method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000003657 Likelihood-ratio test Methods 0.000 description 5
- 230000002087 whitening effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000010355 oscillation Effects 0.000 description 2
- 108010014173 Factor X Proteins 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
Images
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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
Definitions
- This invention relates generally to signal processing, and in particular to space-time adaptive processing (STAP) for target detection using radar signals.
- STAP space-time adaptive processing
- STAP Space-time adaptive processing
- STAP involves a two-dimensional filtering technique applied to signals acquired by a phased-array antenna with multiple spatial channels. Coupling the multiple spatial channels with time dependent pulse-Doppler waveforms leads to STAP. By applying statistics of interference of the environment, a space-time adaptive weight vector is formed. Then, the weight vector is applied to the coherent signals received by the radar to detect the target.
- FIG. 1 shows the signal model of the conventional STAP.
- acquired signals 102 include the test signal 110 consisting of a target signal and the disturbance d 0 111 , and a set of i.i.d. training signals x k 120 with respect to d 0 111 .
- the target signal can be expressed as a product of a known steering vector s 130 and an unknown amplitude ⁇ .
- two types of the estimation sources of the disturbance covariance matrix are usually used for a homogeneous environment where the covariance matrix of the test signal 110 is the same as that of the training signal 120 .
- These two methods are the estimation of disturbance covariance matrix 220 from training signals 120 via a covariance matrix estimator 210 , and the generation of the disturbance covariance matrix 250 from prior knowledge 230 via a covariance matrix generator 240 .
- the knowledge database can include maps of the environment, past measurements, etc.
- a conventional method known as Kelly's generalized likelihood ratio test (GLRT)
- GLRT generalized likelihood ratio test
- ⁇ is the amplitude of the target signal
- x k are target free training signals
- x 0 is the test signal
- R is the covariance matrix of the training signals
- ⁇ 1 ( ) and ⁇ 0 ( ) are likelihood functions under two hypothesis H 1 , i.e., the target is present in the test signal, and H 0 , i.e., the target is not present in the test signal, respectively.
- the resulting test statistic 340 is compared to a threshold 350 to detect 360 the target.
- FIG. 5 shows a conventional Bayesian treatment for the detection problem in a homogeneous environment, which assumes the disturbance covariance matrix is randomly distributed with some prior probability distribution.
- Inputs are the test signal 110 , the training signals 120 and a knowledge database 230 .
- the resulting detectors are often referred to as the knowledge aided (KA) detectors for the homogeneous environment.
- the detector determines the ratio 530 of
- the resulting test statistic T 540 is compared to a threshold 550 to detect 560 whether a target is present, or not.
- One model is the well-known compound-Gaussian model, in which the training signal is a product of a scalar texture, and a Gaussian vector.
- the texture is used to simulate power oscillations among the signals.
- Another model is the partially homogeneous environment, by which the training signals 120 share the covariance matrix with the test signal 110 up to an unknown scaling factor X.
- FIG. 4 shows a conventional GLRT treatment on the detection problem, which results in the well-known adaptive coherence estimator (ACE) for the partially homogeneous environment.
- the input includes the acquired signals 101 comprising the test 110 and training signals 120 .
- ⁇ is amplitude of the test signal
- x k are target free training signals
- x 0 is the test signal
- R is the covariance matrix
- ⁇ 1 ( ) and ⁇ 0 ( ) are the likelihood functions under two hypothesis H 1 , i.e., the target is present in the test signal
- H 0 i.e., the target is not present in the test signal.
- the resulting test statistic 440 is compared to a threshold 450 to detect 460 the presence of a target.
- the embodiments of the invention provide a method for detecting targets in radar signals using space-time adaptive processing (STAP).
- STAP space-time adaptive processing
- a stochastic partially homogeneous model is used by the embodiments of the invention, which incorporate some a priori knowledge to the partially homogeneous model.
- the stochastic partially homogeneous retains the power heterogeneity between the test signal and the training signals with an additional power scaling factor.
- the scale invariant generalized likelihood ratio test is developed from using Bayesian framework.
- a likelihood function is integrated over a prior probability distribution of the covariance matrix to obtain an integrated likelihood function. Then, the integrated likelihood function is maximized with respect to deterministic but unknown parameters, a scaling factor ⁇ and a signal amplitude ⁇ .
- GLRT generalized likelihood ratio test
- our KA-ACE uses a linear combination of the sample covariance matrix and the a priori matrix R as its weighting matrix.
- the loading factor of R is linear with respect to the parameter ⁇ , which reflects the accuracy of the priori matrix R .
- FIG. 1 is a block diagram of prior art signals when a target is present or not;
- FIG. 2 is a block diagram of prior art covariances matrix estimates of background clutter from training signals and from a knowledge database via the estimates;
- FIG. 3 is a block diagram of prior art generalized likelihood ratio test (GLRT) for homogeneous environments in the prior art
- FIG. 4 is a block diagram of prior art GLRT for partially homogeneous environments, referred to as adaptive coherence estimator (ACE);
- ACE adaptive coherence estimator
- FIG. 5 is a block diagram of prior art knowledge aided GLRT for stochastic homogeneous environments.
- FIG. 6 is a block diagram of knowledge aided ACE for stochastic partially homogeneous environments according to embodiments of the invention.
- the embodiments of the invention provide a method for detecting targets using space-time adaptive processing (STAP) of test signals, and a generalized likelihood ratio test (GLRT).
- STAP space-time adaptive processing
- GLRT generalized likelihood ratio test
- Our scale-invariant GLRT is a knowledge-aided (KA) version of an adaptive coherence estimator (ACE).
- ACE adaptive coherence estimator
- H 0 is that the target is not present in the test signal
- H 1 the target is present in the test signal
- x k are target free training signals 120
- x 0 is the test signal 110
- s is an array of a presumed known response
- a is an unknown complex-valued amplitude of the test signal
- d 0 and d k are the disturbance covariance matrices R 0 and R of the test and training signals, respectively.
- the covariance matrix R of the training signals is random and has a probability density function p(R), which is a function of the covariance matrix prior probability matrix R .
- a test statistic T 630 is determined from a Bayesian framework according to Equation (2), a ratio 330 of 610 to 620 , and a scaling factor ⁇
- T max ⁇ ⁇ max ⁇ ⁇ ⁇ R ⁇ f 1 ⁇ ( x 0 , x 1 , ... ⁇ , x K ⁇ ⁇ , ⁇ , R ) ⁇ p ⁇ ( R ) ⁇ ⁇ R max ⁇ ⁇ ⁇ R ⁇ f 0 ⁇ ( x 0 , x 1 , ... ⁇ , x K ⁇ ⁇ , R ) ⁇ p ⁇ ( R ) ⁇ ⁇ R , ( 2 )
- ⁇ can be in a range of about [1-16].
- Equation (2) The GRLT in Equation (2) can be reduced to
- Equation (4) the amplitude ⁇ in Equation (4) is replaced by a maximum likelihood estimate of the amplitude ⁇ according to
- ⁇ ⁇ ML s H ⁇ ⁇ - 1 ⁇ x 0 s H ⁇ ⁇ - 1 ⁇ s ( 5 )
- T KA ⁇ - ⁇ ACE ⁇ s H ⁇ ⁇ - 1 ⁇ x 0 ⁇ 2 ( s H ⁇ ⁇ - 1 ⁇ s ) ⁇ ( x 0 H ⁇ ⁇ - 1 ⁇ x 0 ) ⁇ ⁇ ⁇ KA ⁇ - ⁇ ACE ( 6 )
- ⁇ KA-ACE denotes a threshold subject to a probability of a false alarm.
- the KA-ACE for the stochastic partially homogeneous environment takes the form of the standard ACE, except that the whitening matrix is
- the weighting factor of the prior knowledge is controlled by ⁇ . It makes sense that the KA-ACE puts more weights on the prior matrix R , when the prior matrix is more accurate, i.e., ⁇ is relatively large.
- the embodiments of the invention provide a method for detecting targets.
- a knowledge-aided adaptive coherence estimator ACE is provided for a stochastic partially homogeneous environment, which models the power oscillation between the test and the training signals and treats the disturbance covariance matrix as a random matrix.
- the KA-ACE has a color-loading form between the sample covariance matrix and the prior knowledge used for the whitening matrix.
- the KA-ACE is scale invariant and performs better than the conventional ACE in various applications.
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
Description
- This invention relates generally to signal processing, and in particular to space-time adaptive processing (STAP) for target detection using radar signals.
- Space-time adaptive processing (STAP) is frequently used in radar systems to detect a target. STAP has been known since the early 1970's. In airborne radar systems, STAP improves target detection when interference in an environment, e.g., ground clutter and jamming, is a problem. STAP can achieve order-of-magnitude sensitivity improvements in target detection.
- Typically, STAP involves a two-dimensional filtering technique applied to signals acquired by a phased-array antenna with multiple spatial channels. Coupling the multiple spatial channels with time dependent pulse-Doppler waveforms leads to STAP. By applying statistics of interference of the environment, a space-time adaptive weight vector is formed. Then, the weight vector is applied to the coherent signals received by the radar to detect the target.
-
FIG. 1 shows the signal model of the conventional STAP. When no target is detected, acquiredsignals 101 include a test signal x0 110 consisting of thedisturbance d 0 111 only, and a set of training signals xk, k=1, 2, . . . , K, 120, which are independent and identically distributed (i.i.d.) with thedisturbance d 0 111. When a target is detected, acquiredsignals 102 include thetest signal 110 consisting of a target signal and thedisturbance d 0 111, and a set of i.i.d. training signals xk 120 with respect tod 0 111. The target signal can be expressed as a product of a known steering vector s 130 and an unknown amplitude α. - As shown in
FIG. 2 for conventional target detection with STAP, two types of the estimation sources of the disturbance covariance matrix are usually used for a homogeneous environment where the covariance matrix of thetest signal 110 is the same as that of thetraining signal 120. These two methods are the estimation ofdisturbance covariance matrix 220 fromtraining signals 120 via acovariance matrix estimator 210, and the generation of thedisturbance covariance matrix 250 fromprior knowledge 230 via acovariance matrix generator 240. The knowledge database can include maps of the environment, past measurements, etc. - As shown in
FIG. 3 , a conventional method, known as Kelly's generalized likelihood ratio test (GLRT), takes the acquired signals including thetest signal 110 andtraining signals 120 as input, and then determines theratio 330 of -
- where α is the amplitude of the target signal, xk are target free training signals, x0 is the test signal, R is the covariance matrix of the training signals, and ƒ1( ) and ƒ0( ) are likelihood functions under two hypothesis H1, i.e., the target is present in the test signal, and H0, i.e., the target is not present in the test signal, respectively. The resulting
test statistic 340 is compared to athreshold 350 to detect 360 the target. -
FIG. 5 shows a conventional Bayesian treatment for the detection problem in a homogeneous environment, which assumes the disturbance covariance matrix is randomly distributed with some prior probability distribution. - Inputs are the
test signal 110, thetraining signals 120 and aknowledge database 230. The resulting detectors are often referred to as the knowledge aided (KA) detectors for the homogeneous environment. The detector determines theratio 530 of -
- The resulting
test statistic T 540 is compared to athreshold 550 to detect 560 whether a target is present, or not. - For non-homogeneous environments, several models are known. One model is the well-known compound-Gaussian model, in which the training signal is a product of a scalar texture, and a Gaussian vector. The texture is used to simulate power oscillations among the signals.
- Another model is the partially homogeneous environment, by which the
training signals 120 share the covariance matrix with thetest signal 110 up to an unknown scaling factor X. -
FIG. 4 shows a conventional GLRT treatment on the detection problem, which results in the well-known adaptive coherence estimator (ACE) for the partially homogeneous environment. In that method, the input includes the acquiredsignals 101 comprising thetest 110 andtraining signals 120. Then, theratio 430 of -
- is determined, where α is amplitude of the test signal, xk are target free training signals, x0 is the test signal, R is the covariance matrix, ƒ1( ) and ƒ0( ) are the likelihood functions under two hypothesis H1, i.e., the target is present in the test signal, and H0, i.e., the target is not present in the test signal. The resulting
test statistic 440 is compared to athreshold 450 to detect 460 the presence of a target. - The embodiments of the invention provide a method for detecting targets in radar signals using space-time adaptive processing (STAP). Different from the conventional partially homogeneous model, a stochastic partially homogeneous model is used by the embodiments of the invention, which incorporate some a priori knowledge to the partially homogeneous model. The stochastic partially homogeneous retains the power heterogeneity between the test signal and the training signals with an additional power scaling factor.
- In this invention, according to the stochastic partially homogeneous model, the scale invariant generalized likelihood ratio test is developed from using Bayesian framework.
- Accordingly, a likelihood function is integrated over a prior probability distribution of the covariance matrix to obtain an integrated likelihood function. Then, the integrated likelihood function is maximized with respect to deterministic but unknown parameters, a scaling factor λ and a signal amplitude α.
- Finally, an integrated generalized likelihood ratio test (GLRT) is derived in a closed-form. The resulting scale-invariant GLRT is a knowledge-aided (KA) version of an adaptive coherence estimator (ACE).
- Specifically, our KA-ACE uses a linear combination of the sample covariance matrix and the a priori matrix
R as its weighting matrix. The loading factor ofR is linear with respect to the parameter μ, which reflects the accuracy of the priori matrixR . -
FIG. 1 is a block diagram of prior art signals when a target is present or not; -
FIG. 2 is a block diagram of prior art covariances matrix estimates of background clutter from training signals and from a knowledge database via the estimates; -
FIG. 3 is a block diagram of prior art generalized likelihood ratio test (GLRT) for homogeneous environments in the prior art; -
FIG. 4 is a block diagram of prior art GLRT for partially homogeneous environments, referred to as adaptive coherence estimator (ACE); -
FIG. 5 is a block diagram of prior art knowledge aided GLRT for stochastic homogeneous environments; and -
FIG. 6 is a block diagram of knowledge aided ACE for stochastic partially homogeneous environments according to embodiments of the invention. - As shown in
FIG. 6 , the embodiments of the invention provide a method for detecting targets using space-time adaptive processing (STAP) of test signals, and a generalized likelihood ratio test (GLRT). Our scale-invariant GLRT is a knowledge-aided (KA) version of an adaptive coherence estimator (ACE). The steps of the method can be performed in aprocessor 600 connected to a memory and input/output interfaces as known in the art. - Specifically, we use the following hypothesis testing problem:
-
H 0 x 0 =d 0, -
x k =d k , k=1, . . . ,K, -
H 1 :x 0 =αs+d 0, -
x k =d k , k=1, . . . ,K, (1) - where the hypothesis H0 is that the target is not present in the test signal, H1, the target is present in the test signal, xk are target free training signals 120, x0 is the
test signal 110, s is an array of a presumed known response, a is an unknown complex-valued amplitude of the test signal, and d0 and dk are the disturbance covariance matrices R 0 and R of the test and training signals, respectively. - The covariance matrix R of the training signals is random and has a probability density function p(R), which is a function of the covariance matrix prior probability matrix
R . - A test
statistic T 630 is determined from a Bayesian framework according to Equation (2), aratio 330 of 610 to 620, and a scaling factor λ -
- wherein a function max returns a maximum value, and λ can be in a range of about [1-16].
- The GRLT in Equation (2) can be reduced to
-
- where L=K+μ+1, and
-
Σ i=Σi+(μ−N)R =λ −1 Y i y i H +S+(μ−N)R , - with yi=x0−βiαs, β1=1, β0=0, and
-
- After deriving and substituting the maximum likelihood estimate of the scalar λ into Equation (3), the our test statistics T becomes
-
- Next, the amplitude α in Equation (4) is replaced by a maximum likelihood estimate of the amplitude α according to
-
- Taking the Nth square root of Equation (4) and using monotonic properties of the function ƒ(x)=1/(1−x), the
new test statistic 630 is -
- where γKA-ACE denotes a threshold subject to a probability of a false alarm.
- The KA-ACE for the stochastic partially homogeneous environment takes the form of the standard ACE, except that the whitening matrix is
-
- which uses a linear combination between the sample covariance matrix S and the prior knowledge covariance matrix
R . The weighting factor of the prior knowledge is controlled by μ. It makes sense that the KA-ACE puts more weights on the prior matrixR , when the prior matrix is more accurate, i.e., μ is relatively large. - In comparison, the conventional ACE also takes the same form, but with the whitening matrix given by the sample covariance matrix=Γ=statistic is finally compared to a
threshold 350 to detect 360 whether atarget signal 130 is present in thetest signal 110. - The embodiments of the invention provide a method for detecting targets. A knowledge-aided adaptive coherence estimator ACE is provided for a stochastic partially homogeneous environment, which models the power oscillation between the test and the training signals and treats the disturbance covariance matrix as a random matrix.
- The KA-ACE has a color-loading form between the sample covariance matrix and the prior knowledge used for the whitening matrix. We note that the KA-ACE is scale invariant and performs better than the conventional ACE in various applications.
- Although the invention has been described by way of exes of preferred embodiments, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.
Claims (4)
H 0 x 0 =d 0,
x k =d k , k=1, . . . ,K,
H 1 :x 0 =αs+d 0,
x k =d k , k=1, . . . ,K,
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/879,288 US8138963B1 (en) | 2010-09-10 | 2010-09-10 | Method for detecting targets using space-time adaptive processing and shared knowledge of the environment |
JP2011191941A JP5611157B2 (en) | 2010-09-10 | 2011-09-02 | A method for detecting targets in radar signals using spatio-temporal adaptive processing |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/879,288 US8138963B1 (en) | 2010-09-10 | 2010-09-10 | Method for detecting targets using space-time adaptive processing and shared knowledge of the environment |
Publications (2)
Publication Number | Publication Date |
---|---|
US20120062409A1 true US20120062409A1 (en) | 2012-03-15 |
US8138963B1 US8138963B1 (en) | 2012-03-20 |
Family
ID=45806150
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/879,288 Expired - Fee Related US8138963B1 (en) | 2010-09-10 | 2010-09-10 | Method for detecting targets using space-time adaptive processing and shared knowledge of the environment |
Country Status (2)
Country | Link |
---|---|
US (1) | US8138963B1 (en) |
JP (1) | JP5611157B2 (en) |
Cited By (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
CN105223560A (en) * | 2015-10-13 | 2016-01-06 | 中国人民解放军空军工程大学 | Based on the airborne radar object detection method of the sparse recovery of clutter pitching azimuth spectrum |
US20160223646A1 (en) * | 2011-04-13 | 2016-08-04 | Raytheon Company | Enhanced detection and automatic signature extraction of radar resonance reflections in above and below-ground man-made objects |
CN106483509A (en) * | 2015-08-27 | 2017-03-08 | 南京理工大学 | A kind of towed decoy Detection of Existence method based on Generalized Likelihood Ratio |
US10101445B2 (en) * | 2014-04-29 | 2018-10-16 | Research Foundation Of The City University Of New York | Power centroid radar |
CN109709526A (en) * | 2018-12-12 | 2019-05-03 | 南京邮电大学 | A kind of knowledge assistance grouping generalized likelihood test method |
CN111427014A (en) * | 2020-03-19 | 2020-07-17 | 中国电子科技集团公司第十四研究所 | Adaptive signal processing realization method based on Gaussian elimination |
CN111999718A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Knowledge-aided adaptive fusion detection method based on geometric mean estimation |
CN111999714A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance |
CN111999715A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Target knowledge auxiliary self-adaptive fusion detection method under heterogeneous clutter |
CN111999716A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Clutter prior information-based target adaptive fusion detection method |
CN113219432A (en) * | 2021-05-14 | 2021-08-06 | 内蒙古工业大学 | Moving object detection method based on knowledge assistance and sparse Bayesian learning |
US20210333356A1 (en) * | 2020-04-22 | 2021-10-28 | US Gov't as represented by Secretary of Air Force | Space-time adaptive processing using random matrix theory |
CN114994632A (en) * | 2022-08-03 | 2022-09-02 | 中国人民解放军空军预警学院 | Radar target detection method and system based on symmetric power spectral density |
CN115508828A (en) * | 2022-10-20 | 2022-12-23 | 中国人民解放军海军航空大学 | Intelligent fusion detection method for radar target under subspace interference |
CN115685082A (en) * | 2022-11-10 | 2023-02-03 | 山东工商学院 | Wald-based method for detecting distance extension target under interference plus noise background |
CN116299387A (en) * | 2023-01-04 | 2023-06-23 | 中国人民解放军海军航空大学 | Target intelligent detection method for interference orthogonal suppression under heterogeneous clutter |
CN116819480A (en) * | 2023-07-17 | 2023-09-29 | 中国人民解放军空军预警学院 | Self-adaptive target detection method and system in strong clutter of airborne radar |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108089211B (en) * | 2016-11-23 | 2021-08-10 | 北京自动化控制设备研究所 | Self-adaptive power consumption reduction method suitable for space-time anti-interference |
CN109946689B (en) * | 2019-04-04 | 2020-07-21 | 电子科技大学 | Target detection method based on spatial energy focusing technology |
KR102296923B1 (en) * | 2019-07-29 | 2021-09-02 | 부경대학교 산학협력단 | Radar Signal Detection Method |
Citations (10)
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 |
US6822606B2 (en) * | 2002-03-13 | 2004-11-23 | Raytheon Canada Limited | System and method for spectral generation in radar |
US7038618B2 (en) * | 2004-04-26 | 2006-05-02 | Budic Robert D | Method and apparatus for performing bistatic radar functions |
US7079072B1 (en) * | 1987-01-23 | 2006-07-18 | Raytheon Company | Helicopter recognition radar processor |
US7212150B2 (en) * | 2005-04-21 | 2007-05-01 | The United States Of America As Represented By The Secretary Of The Navy | Doppler-sensitive adaptive coherence estimate detector methods |
US7259714B1 (en) * | 2005-05-04 | 2007-08-21 | Cataldo Thomas J | Unique space time adaptive system (USS) |
USH2222H1 (en) * | 2005-10-13 | 2008-08-05 | The United States Of America As Represented By The Secretary Of The Air Force | Normalized matched filter—a low rank approach |
US7474258B1 (en) * | 2005-06-06 | 2009-01-06 | Signal Labs, Inc. | System and method for detection and discrimination of targets in the presence of interference |
US7535410B2 (en) * | 2006-07-26 | 2009-05-19 | Kabushiki Kaisha Toshiba | Weight calculation method, weight calculation device, adaptive array antenna, and radar device |
US20090237294A1 (en) * | 2008-03-19 | 2009-09-24 | Yoshikazu Shoji | Weight calculation method, weight calculation device, adaptive array antenna, and radar device |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6091361A (en) * | 1998-05-12 | 2000-07-18 | Davis; Dennis W. | Method and apparatus for joint space-time array signal processing |
JP2007003325A (en) * | 2005-06-23 | 2007-01-11 | Toshiba Corp | Method of calculating covariance matrix, weight calculation circuit, adaptive array antennas and radar installation |
JP4792348B2 (en) * | 2006-07-31 | 2011-10-12 | 大成建設株式会社 | Air outlet direction control mechanism and ductless ventilation system |
CN101819269A (en) * | 2010-03-19 | 2010-09-01 | 清华大学 | Space-time adaptive processing method under non-homogeneous clutter environment |
-
2010
- 2010-09-10 US US12/879,288 patent/US8138963B1/en not_active Expired - Fee Related
-
2011
- 2011-09-02 JP JP2011191941A patent/JP5611157B2/en not_active Expired - Fee Related
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7079072B1 (en) * | 1987-01-23 | 2006-07-18 | Raytheon Company | Helicopter recognition radar processor |
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 |
US6822606B2 (en) * | 2002-03-13 | 2004-11-23 | Raytheon Canada Limited | System and method for spectral generation in radar |
US7038618B2 (en) * | 2004-04-26 | 2006-05-02 | Budic Robert D | Method and apparatus for performing bistatic radar functions |
US7369083B2 (en) * | 2004-04-26 | 2008-05-06 | Budic Robert D | Method for performing bistatic radar functions |
US7212150B2 (en) * | 2005-04-21 | 2007-05-01 | The United States Of America As Represented By The Secretary Of The Navy | Doppler-sensitive adaptive coherence estimate detector methods |
US7259714B1 (en) * | 2005-05-04 | 2007-08-21 | Cataldo Thomas J | Unique space time adaptive system (USS) |
US7474258B1 (en) * | 2005-06-06 | 2009-01-06 | Signal Labs, Inc. | System and method for detection and discrimination of targets in the presence of interference |
US20090027257A1 (en) * | 2005-06-06 | 2009-01-29 | Orhan Arikan | System and method for detection and discrimination of targets in the presence of interference |
USH2222H1 (en) * | 2005-10-13 | 2008-08-05 | The United States Of America As Represented By The Secretary Of The Air Force | Normalized matched filter—a low rank approach |
US7535410B2 (en) * | 2006-07-26 | 2009-05-19 | Kabushiki Kaisha Toshiba | Weight calculation method, weight calculation device, adaptive array antenna, and radar device |
US20090237294A1 (en) * | 2008-03-19 | 2009-09-24 | Yoshikazu Shoji | Weight calculation method, weight calculation device, adaptive array antenna, and radar device |
Cited By (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20160223646A1 (en) * | 2011-04-13 | 2016-08-04 | Raytheon Company | Enhanced detection and automatic signature extraction of radar resonance reflections in above and below-ground man-made objects |
US9910134B2 (en) * | 2011-04-13 | 2018-03-06 | Raytheon Company | Enhanced detection and automatic signature extraction of radar resonance reflections in above and below-ground man-made objects |
US10101445B2 (en) * | 2014-04-29 | 2018-10-16 | Research Foundation Of The City University Of New York | Power centroid radar |
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 |
CN104215939B (en) * | 2014-10-10 | 2017-02-15 | 北京航空航天大学 | Knowledge assisted space-time adaptive processing method integrating generalized symmetrical structure information |
CN106483509A (en) * | 2015-08-27 | 2017-03-08 | 南京理工大学 | A kind of towed decoy Detection of Existence method based on Generalized Likelihood Ratio |
CN105223560A (en) * | 2015-10-13 | 2016-01-06 | 中国人民解放军空军工程大学 | Based on the airborne radar object detection method of the sparse recovery of clutter pitching azimuth spectrum |
CN109709526A (en) * | 2018-12-12 | 2019-05-03 | 南京邮电大学 | A kind of knowledge assistance grouping generalized likelihood test method |
CN111427014A (en) * | 2020-03-19 | 2020-07-17 | 中国电子科技集团公司第十四研究所 | Adaptive signal processing realization method based on Gaussian elimination |
US20210333356A1 (en) * | 2020-04-22 | 2021-10-28 | US Gov't as represented by Secretary of Air Force | Space-time adaptive processing using random matrix theory |
CN111999714A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Self-adaptive fusion detection method based on multi-scattering point estimation and clutter knowledge assistance |
CN111999715A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Target knowledge auxiliary self-adaptive fusion detection method under heterogeneous clutter |
CN111999716A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Clutter prior information-based target adaptive fusion detection method |
CN111999718A (en) * | 2020-09-02 | 2020-11-27 | 中国人民解放军海军航空大学 | Knowledge-aided adaptive fusion detection method based on geometric mean estimation |
CN113219432A (en) * | 2021-05-14 | 2021-08-06 | 内蒙古工业大学 | Moving object detection method based on knowledge assistance and sparse Bayesian learning |
CN114994632A (en) * | 2022-08-03 | 2022-09-02 | 中国人民解放军空军预警学院 | Radar target detection method and system based on symmetric power spectral density |
CN115508828A (en) * | 2022-10-20 | 2022-12-23 | 中国人民解放军海军航空大学 | Intelligent fusion detection method for radar target under subspace interference |
CN115685082A (en) * | 2022-11-10 | 2023-02-03 | 山东工商学院 | Wald-based method for detecting distance extension target under interference plus noise background |
CN116299387A (en) * | 2023-01-04 | 2023-06-23 | 中国人民解放军海军航空大学 | Target intelligent detection method for interference orthogonal suppression under heterogeneous clutter |
CN116819480A (en) * | 2023-07-17 | 2023-09-29 | 中国人民解放军空军预警学院 | Self-adaptive target detection method and system in strong clutter of airborne radar |
Also Published As
Publication number | Publication date |
---|---|
JP2012058234A (en) | 2012-03-22 |
JP5611157B2 (en) | 2014-10-22 |
US8138963B1 (en) | 2012-03-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8138963B1 (en) | Method for detecting targets using space-time adaptive processing and shared knowledge of the environment | |
US8907841B2 (en) | Method for detecting targets using space-time adaptive processing | |
Greco et al. | Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance | |
Gini et al. | Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter | |
JP2012220492A5 (en) | ||
Guan et al. | Subspace detection for range and Doppler distributed targets with Rao and Wald tests | |
US9057783B2 (en) | Change detection method and system for use in detecting moving targets behind walls, barriers or otherwise visually obscured | |
Raghavan | Statistical interpretation of a data adaptive clutter subspace estimation algorithm | |
USH2222H1 (en) | Normalized matched filter—a low rank approach | |
CN103412290A (en) | Knowledge-assisted APR non-uniform sample detection method | |
Magraner et al. | Detection in gamma-distributed nonhomogeneous backgrounds | |
AU656871B2 (en) | Improvements to dipole detection and localisation processing | |
Sangston et al. | Adaptive detection of radar targets in compound-Gaussian clutter | |
Weinberg et al. | A Bayesian-based CFAR detector for Pareto type II clutter | |
Ouchi et al. | Statistical analysis of azimuth streaks observed in digitally processed CASSIE imagery of the sea surface | |
CN113687321A (en) | Radar target detection distance evaluation method and device | |
Sha et al. | Bayesian sonar detection performance prediction in the presence of interference in uncertain environments | |
McDonald et al. | Performance prediction for a coherent X-band radar in a maritime environment with K-distributed sea clutter | |
Jia | Multi-target CFAR detection of a digital phased array radar system | |
Çetin | CFAR detection in K-distributed sea clutter | |
Herselman et al. | Evaluation and performance comparison of detection algorithms in a maritime environment | |
Blake et al. | High resolution SAR clutter textural analysis | |
Yan et al. | Exact fisher information matrix with state-dependent probability of detection | |
CN114578333B (en) | Active sonar target dynamic and static identification method | |
Liu et al. | Diagonal loading for STAP and its performance analysis |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: MITSUBISHI ELECTRIC RESEARCH LABORATORIES, INC., M Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:PUN, MAN-ON;SAHINOGLU, ZAFER;WANG, PU;SIGNING DATES FROM 20100910 TO 20100916;REEL/FRAME:025049/0440 |
|
ZAAA | Notice of allowance and fees due |
Free format text: ORIGINAL CODE: NOA |
|
ZAAB | Notice of allowance mailed |
Free format text: ORIGINAL CODE: MN/=. |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
FPAY | Fee payment |
Year of fee payment: 4 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 8TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1552); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY Year of fee payment: 8 |
|
FEPP | Fee payment procedure |
Free format text: MAINTENANCE FEE REMINDER MAILED (ORIGINAL EVENT CODE: REM.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
LAPS | Lapse for failure to pay maintenance fees |
Free format text: PATENT EXPIRED FOR FAILURE TO PAY MAINTENANCE FEES (ORIGINAL EVENT CODE: EXP.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY |
|
STCH | Information on status: patent discontinuation |
Free format text: PATENT EXPIRED DUE TO NONPAYMENT OF MAINTENANCE FEES UNDER 37 CFR 1.362 |
|
FP | Lapsed due to failure to pay maintenance fee |
Effective date: 20240320 |