CN113093135A - Target detection method and device based on F norm normalized distance - Google Patents
Target detection method and device based on F norm normalized distance Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/418—Theoretical aspects
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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Abstract
The invention discloses a target detection method based on F norm normalized distance, which comprises the following steps: receiving echo data of a target, randomly selecting one unit in the echo data as a unit to be detected, selecting a plurality of units around the unit to be detected as reference units, and calculating a covariance matrix of the echo data in the unit to be detected and a covariance mean matrix of the echo data in the plurality of reference units; acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in a plurality of reference units by utilizing a pre-constructed geometric measurement function based on the F norm; and comparing a preset judgment threshold with the geometric distance, and judging that the target exists in the unit to be detected if the geometric distance is greater than the judgment threshold. Compared with the conventional detection method, the algorithm has the advantages of small calculation amount and better detection performance.
Description
Technical Field
The invention belongs to the field of radar target detection, and particularly relates to a target detection method and device based on an F norm normalized distance.
Background
Sea surface target detection is always the key direction of sea clutter research. When a received radar echo signal only contains a small number of pulses, it is difficult to achieve satisfactory performance by using a conventional detection method such as a Fast Fourier Transform (FFT) -based matrix constant false alarm detection algorithm, which is denoted as FFT-CFAR. In recent years, information geometry has been rapidly developed, information science problems are generally researched by using the application of differential geometry to statistical manifold, and the method has been widely applied to a plurality of fields such as image processing, information coding and medical signal analysis. According to the information geometry theory, a covariance mean matrix can form a matrix manifold in a space angle, so that a correlation matrix of echo data received by a radar corresponds to a point on the matrix manifold in the information geometry, and the sea surface target detection problem is simplified into a geometric problem related to the covariance matrix on the matrix manifold. Based on this, the matrix CFAR detector proposed by Barbaresco et al uses the geodesic distance between the riemann mean value of the echo correlation matrix of the detection unit and the echo correlation matrix of the reference unit as the detection statistic, overcomes the problems of energy leakage, low doppler resolution and the like for short pulse sequences in the traditional detection method, and effectively improves the detection performance. On the basis, a related scholars put forward a series of matrix CFAR detection methods based on information geometry based on different geometric measurement methods such as information divergence, random distance and the like. However, these methods tend to have high computational complexity, and this disadvantage also severely limits their practical applications. For example, in an article "an improved matrix CFAR detection method based on KL resolution" from the early warning academy of air force, the document is based on a matrix CFAR detector of KL (Kullback-Leibler) divergence and corresponding divergence mean values, and obtains better detection and lower computation complexity by replacing geodesic distances with KL divergences with better information accumulation performance and replacing riemann mean values with corresponding divergence mean values. The document has the following defects: the geometric distance between the matrices and the computation complexity of the corresponding matrix mean are still high, and the practical application is limited. For another example, the document "Matrix CFAR detectors based on systematic compensated Kullback-Leibler and total Kullback-Leibler divergences" of the university of defense science derived and analyzed two kinds of spread KL divergences, i.e., SKL divergences and tKL divergences, and designed a corresponding Matrix CFAR detector. The document has the following defects: the computational complexity of the SKL mean matrix CFAR detector is slightly higher than that of the KL mean matrix CFAR detector, but the detection performance of the SKL mean matrix CFAR detector is not improved, while the detection performance of the tKL center matrix CFAR detector is better than that of the KL mean matrix CFAR detector, but the computational complexity of the SKL mean matrix CFAR detector is far higher than that of the KL mean matrix CFAR detector.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a target detection method and a device based on F norm normalized distance, deduces and analyzes a new matrix CFAR detector based on Frobenius norm normalized distance, and has the advantages of lower calculation complexity and better detection performance.
The invention discloses a target detection method based on F norm normalized distance, which comprises the following steps:
receiving echo data of a target, randomly selecting one unit in the echo data as a unit to be detected, selecting a plurality of units around the unit to be detected as reference units, and calculating a covariance matrix of the echo data in the unit to be detected and a covariance mean matrix of the echo data in the plurality of reference units;
acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in a plurality of reference units by utilizing a pre-constructed geometric measurement function based on the F norm;
and comparing a preset judgment threshold with the geometric distance, and judging that the target exists in the unit to be detected if the geometric distance is greater than the judgment threshold.
Further, the F-norm based geometric metric function is:
where d (,) represents the geometric distance between two matrices, | |. uFF norm, R, representing a matrix1,R2The covariance matrix is calculated by the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1，z2，...,zNIs any one of the received complex pulse data, N is the pulseLength of dash data, ()HWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
denotes the complex conjugate of z, R is a Toeplitz covariance matrix, RH＝R。
Further, the covariance mean matrix of the echo data in several reference cells:
wherein R iskIs the covariance matrix of the echo data in the reference cell, and K represents the total number of reference cells.
Further, the geometric distance and the decision threshold are determined by the following formula:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Alternative assumptions are indicated.
Further, the selected number of the reference units is 16.
Further, the target detection device based on the F norm normalized distance comprises:
a radar for receiving echo data of a target;
the to-be-detected unit selection module is used for randomly selecting one unit from the echo data as a to-be-detected unit;
the reference unit selection module is used for selecting a plurality of units as reference units at the periphery of the unit to be detected;
the geometric measurement module based on the F norm is used for acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in the plurality of reference units;
and the target detection module is used for calculating a judgment threshold and judging that a target exists in the unit to be detected if the geometric distance is greater than the judgment threshold.
Further, the F-norm based geometric metric function is:
where d (,) represents the geometric distance between two matrices, | |. uFF norm, R, representing a matrix1,R2The covariance mean matrix is obtained by calculating the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1,z2,...,zNIs any one complex pulse data received by the radar, and N is the length of the pulse dataHWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
denotes the complex conjugate of z, R is a covariance matrix, RH＝R。
Further, the covariance mean matrix of the echo data in the several reference cells:
wherein R iskIs the covariance matrix of the echo data in the reference cell, and K represents the total number of reference cells.
Further, the determination formula of the geometric distance and the decision threshold in the target detection module is as follows:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Alternative assumptions are indicated.
Further, the number of the reference cells selected by the reference cell selection module is 16.
The invention has the following beneficial effects:
(1) the geometric measurement and the calculation complexity of the corresponding mean value matrix provided by the invention are correspondingly reduced compared with the conventional matrix CFAR detectors such as a KL (Kullback-Leibler) divergence-based matrix CFAR detector, a Log-Euclidean divergence-based matrix CFAR detector, a Bhattacharyya distance-based matrix CFAR detector and the like, and the calculation amount of the algorithm is small.
(2) Compared with the existing other matrix CFAR detectors based on geometric distance, the matrix CFAR detector based on the F norm normalized distance provided by the invention has better detection performance in an actual sea clutter data measurement experiment.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a graph comparing the performance of the F-norm normalized distance target detection function (FC matrix CFAR detector) proposed by the present invention with a conventional matrix CFAR detector in the case of measured sea clutter HH polarization;
FIG. 3 is a graph comparing the performance of the F-norm normalized distance target detection function (FC matrix CFAR detector) proposed by the present invention with a conventional matrix CFAR detector under measured sea clutter HV polarization;
FIG. 4 is a graph comparing the performance of the F-norm normalized distance target detection function (FC matrix CFAR detector) proposed by the present invention with a conventional matrix CFAR detector under the condition of actually measured sea clutter VH polarization;
fig. 5 is a graph comparing the performance of the target detection function (FC matrix CFAR detector) of the F-norm normalized distance proposed by the present invention with that of a conventional matrix CFAR detector under the measured sea clutter VV polarization.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the target detection method based on the F-norm normalized distance of the present invention includes:
receiving echo data of a target, randomly selecting one unit in the echo data as a unit to be detected, selecting a plurality of units around the unit to be detected as reference units, and artificially assuming that the unit to be detected is the target unit because the unit where the target is located is unknown. The unit to be detected is one unit in the received echo data selected at random, the purpose is to determine the range of the selected reference unit, for each practical detection application, the reference unit in the echo data is different each time due to different sea conditions and targets, but the central idea of the selected reference unit is consistent, the number of the reference unit is actually not fixed, but according to an empirical value, we generally determine a unit to be detected at random first, and then select 16 units around the unit to be detected as the reference unit, if the number of the selected reference units is less, the unit to be detected does not have statistical general characteristics and is not representative, and if the number of the selected reference units is more, the calculation complexity is greatly increased. Calculating a covariance matrix of echo data in the unit to be detected and a covariance mean matrix of echo data in a plurality of reference units;
acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in a plurality of reference units by utilizing a pre-constructed geometric measurement function based on the F norm;
and calculating the threshold value by utilizing the geometric distance between the covariance matrix of the echo data in the unit to be detected and the mean covariance matrix of the echo data in all the reference units according to the mathematical principle of Monte Carlo. After the threshold value is obtained, it is only required to calculate whether the geometric distance between the covariance matrix of the echo data in any one cell and the mean covariance matrix of the echo data in all reference cells exceeds the required threshold value, and if so, the cell is determined to be the target. The method mainly converts the problem of radar target detection into a geometric problem on a matrix manifold formed by covariance matrixes, and has the key points that a mean matrix of the covariance matrixes in a reference unit and a dissimilarity measure between the two matrixes are estimated (the geometric distance between the covariance matrixes of the targets and the covariance matrixes of the clutters is far larger than that between the covariance matrixes of the two clutters, and the position of the targets can be judged according to the principle.
Specifically, firstly, a mathematical expression based on F norm geometric measurement is given;
r in formula (1)1,R2Is a covariance matrix, d (,) represents the geometric distance between two matrices, | |. the luminance | | -, |FRepresenting the F-norm of the matrix. R1,R2In practical application, the echo signal is calculated by the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1,z2,...,zNIs any one complex pulse data received by the radar, and N is the length of the pulse dataHWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
represents the complex conjugate of z. R is a Toeplitz covariance matrix, RH＝R。
An expression for analyzing the geometric mean matrix is derived based on formula (1).
For a set of covariance matrices R1,R2,...,RKK represents the total number of reference units, and the mean matrix of the algorithm provided by the invention
As can be given by the solution of the minimum problem,
let the objective function F (R) be:
derived from the derivation of R by F (R),
because R iskIs a positive definite matrix, therefore
The method has the advantages that the method is simplified and can be obtained,
the equation of the above formula can be solved by R,
from equation (7), a calculation formula of the mean matrix based on the geometric distance of equation (1) can be obtained.
The judgment formula of the geometric distance and the judgment threshold based on the F norm normalized distance is given as follows:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Indicating that the backup is assumed.
The main execution body of the method is a target detection device based on the F norm normalized distance, and the method comprises the following steps:
a radar for receiving echo data of a target;
the to-be-detected unit selection module is used for randomly selecting one unit from the echo data as a to-be-detected unit;
the reference unit selection module is used for selecting a plurality of units as reference units at the periphery of the unit to be detected;
the geometric measurement module based on the F norm is used for acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in the plurality of reference units;
and the target detection module is used for calculating a judgment threshold and judging that a target exists in the unit to be detected if the geometric distance is greater than the judgment threshold.
The geometric metric function based on the F norm is as follows:
where d (,) represents the geometric distance between two matrices, | |. uFF norm, R, representing a matrix1,R2The covariance matrix is calculated by the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1,z2，...，zNIs any one complex pulse data received by the radar, and N is the length of the pulse dataHWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
denotes the complex conjugate of z, R is a Toeplitz covariance matrix, RH＝R。
The covariance mean matrix of the echo data in the plurality of reference cells:
wherein R iskIs the covariance matrix of the echo data in the reference cell, and K represents the total number of reference cells.
The judgment formula of the geometric distance and the judgment threshold in the target detection module is as follows:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Alternative assumptions are indicated.
The number of the reference units selected by the reference unit selection module is 16.
The data used in the invention are all measured in real marine environment. The data is in four polarizations, HH, VV, HV, VH, which are derived from the transmitted and received signal patterns, each polarization containing 27 range cells, each range cell containing 60000 pulses of data. Due to the limited measured data the false alarm probability is set to 10-3The pulse number N is 8, the pulse repetition frequency is 1000Hz, and the number of reference units K is 16.
Description figures 2-5 are comparisons of the detection performance of the F-norm normalized distance object detection function (FC matrix CFAR detector) proposed by the present invention with conventional matrix CFAR detectors. The comparison algorithm includes a matrix CFAR detector based on Kullback-Leibler divergence, a matrix CFAR detector based on tKL (total Kullback-Leibler) divergence, a matrix CFAR detector based on Log-Euclidean divergence, a matrix CFAR detector based on Bhattacharyya distance, and a matrix CFAR detector based on Hellinger distance, which are respectively abbreviated as KLD, tKL, Log-E, Bha, and Hel for convenience. Obviously, in the measured sea clutter, the detection performance of the FC matrix CFAR detector is significantly better than that of the existing other geometric distance-based matrix CFAR detectors.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. The target detection method based on the F norm normalized distance is characterized by comprising the following steps:
receiving echo data of a target, randomly selecting one unit in the echo data as a unit to be detected, selecting a plurality of units around the unit to be detected as reference units, and calculating a covariance matrix of the echo data in the unit to be detected and a covariance mean matrix of the echo data in the plurality of reference units;
acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in a plurality of reference units by utilizing a pre-constructed geometric measurement function based on the F norm;
and comparing a preset judgment threshold with the geometric distance, and judging that the target exists in the unit to be detected if the geometric distance is greater than the judgment threshold.
2. The F-norm normalized distance-based target detection method of claim 1, wherein the F-norm-based geometric metric function is:
where d (,) represents the geometric distance between the two matrices, | · caly |FF norm, R, representing a matrix1,R2The covariance matrix is calculated by the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1，z2，...,zNIs any one of the received complex pulse data, and N is the length of the pulse dataHWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
denotes the complex conjugate of z, R is a covariance matrix, RH＝R。
3. The F-norm normalized distance-based target detection method of claim 2, wherein the covariance mean matrix of echo data in several reference cells:
wherein R iskIs the covariance matrix of the echo data in the reference cell, and K represents the total number of reference cells.
4. The target detection method based on the F-norm normalized distance of claim 3, wherein the decision formula of the geometric distance and the decision threshold is as follows:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Alternative assumptions are indicated.
5. The method according to claim 1, wherein the number of the reference units is 16.
6. Target detection device based on F norm normalized distance includes:
a radar for receiving echo data of a target;
the to-be-detected unit selection module is used for randomly selecting one unit from the echo data as a to-be-detected unit;
the reference unit selection module is used for selecting a plurality of units as reference units at the periphery of the unit to be detected;
the geometric measurement module based on the F norm is used for acquiring the geometric distance between the covariance matrix of the echo data in the unit to be detected and the covariance mean matrix of the echo data in the plurality of reference units;
and the target detection module is used for calculating a judgment threshold and judging that a target exists in the unit to be detected if the geometric distance is greater than the judgment threshold.
7. The F-norm normalized distance-based object detection device of claim 6, wherein the F-norm-based geometric metric function is:
where d (,) represents the geometric distance between the two matrices, | · caly |FF norm, R, representing a matrix1,R2The covariance matrix is calculated by the received echo data, and the calculation function is as follows:
wherein z ═ { z ═ z1，z2，...,zNIs any one complex pulse data received by the radar, and N is the length of the pulse dataHWhich represents the transpose of the conjugate,
referred to as the correlation coefficient(s),
denotes the complex conjugate of z, R is a covariance matrix, RH＝R。
8. The F-norm normalized distance-based object detection device of claim 6, wherein the covariance mean matrix of echo data in the plurality of reference cells:
wherein R iskIs the covariance matrix of the echo data in the reference cell, and K represents the total number of reference cells.
9. The apparatus according to claim 7, wherein the geometric distance and decision threshold in the object detection module are determined by the following formula:
wherein R isDA covariance matrix representing echo data in the unit to be detected, gamma being a decision threshold, H0Represents the original hypothesis, H1Alternative assumptions are indicated.
10. The apparatus according to claim 6, wherein the number of the reference cells selected by the reference cell selection module is 16.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2949882A1 (en) * | 2009-09-08 | 2011-03-11 | Thales Sa | Method for processing signal received by radar to detect presence of targets e.g. inhomogeneous sea clutters, involves estimating median matrix of sample from set of covariance matrices of series of signals received by radar |
| US20120249361A1 (en) * | 2011-04-04 | 2012-10-04 | Zafer Sahinoglu | Method for Detecting Targets Using Space-Time Adaptive Processing |
| US20130182788A1 (en) * | 2010-09-24 | 2013-07-18 | France Telecom | Double-iterative method of transmitting multiple streams per user in a multi-user mimo system, and a corresponding transmitter, computer program product, and data medium |
| CN103364769A (en) * | 2013-07-05 | 2013-10-23 | 南京邮电大学 | Adaptive estimation method for spatially non-uniform sea clutter covariance matrix |
| CN105738882A (en) * | 2016-03-31 | 2016-07-06 | 西安电子科技大学 | Whitening degree evaluation method for actual-measured clutter covariance matrix estimated performance |
| WO2017012671A1 (en) * | 2015-07-23 | 2017-01-26 | Huawei Technologies Co., Ltd. | Receiver device and method thereof |
| CN107179531A (en) * | 2017-03-29 | 2017-09-19 | 南京邮电大学 | Amendment sample covariance matrix algorithm for estimating based on maximum a posteriori |
| CN107390194A (en) * | 2017-07-20 | 2017-11-24 | 中国人民解放军国防科学技术大学 | A kind of radar target detection method based on the graceful divergence of full Donald Bragg |
| WO2018045567A1 (en) * | 2016-09-09 | 2018-03-15 | 深圳大学 | Robust stap method based on array manifold priori knowledge having measurement error |
| CN108535711A (en) * | 2018-03-14 | 2018-09-14 | 南京邮电大学 | A kind of packet-based sea clutter covariance matrix estimation method |
| CN109754018A (en) * | 2019-01-09 | 2019-05-14 | 北京工业大学 | A kind of image-recognizing method of the low-rank locality preserving projections based on F norm |
| CN111965632A (en) * | 2020-08-10 | 2020-11-20 | 中国人民解放军国防科技大学 | Radar target detection method based on Riemann manifold dimensionality reduction |
| CN112147603A (en) * | 2020-09-22 | 2020-12-29 | 南京大学 | Target detection method of matrix CFAR detector based on tKL divergence |
| US20210041548A1 (en) * | 2019-08-08 | 2021-02-11 | Syracuse University | Motion detection and classification using ambient wireless signals |
| CA3145696A1 (en) * | 2019-08-09 | 2021-02-18 | Ryoma YATAKA | Image change detection device and image change detection method |
| JP2021033711A (en) * | 2019-08-26 | 2021-03-01 | 日本電信電話株式会社 | Abnormality detector, abnormality detection method, and program |
-
2021
- 2021-03-23 CN CN202110306577.5A patent/CN113093135B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2949882A1 (en) * | 2009-09-08 | 2011-03-11 | Thales Sa | Method for processing signal received by radar to detect presence of targets e.g. inhomogeneous sea clutters, involves estimating median matrix of sample from set of covariance matrices of series of signals received by radar |
| US20130182788A1 (en) * | 2010-09-24 | 2013-07-18 | France Telecom | Double-iterative method of transmitting multiple streams per user in a multi-user mimo system, and a corresponding transmitter, computer program product, and data medium |
| US20120249361A1 (en) * | 2011-04-04 | 2012-10-04 | Zafer Sahinoglu | Method for Detecting Targets Using Space-Time Adaptive Processing |
| CN103364769A (en) * | 2013-07-05 | 2013-10-23 | 南京邮电大学 | Adaptive estimation method for spatially non-uniform sea clutter covariance matrix |
| WO2017012671A1 (en) * | 2015-07-23 | 2017-01-26 | Huawei Technologies Co., Ltd. | Receiver device and method thereof |
| CN105738882A (en) * | 2016-03-31 | 2016-07-06 | 西安电子科技大学 | Whitening degree evaluation method for actual-measured clutter covariance matrix estimated performance |
| WO2018045567A1 (en) * | 2016-09-09 | 2018-03-15 | 深圳大学 | Robust stap method based on array manifold priori knowledge having measurement error |
| CN107179531A (en) * | 2017-03-29 | 2017-09-19 | 南京邮电大学 | Amendment sample covariance matrix algorithm for estimating based on maximum a posteriori |
| CN107390194A (en) * | 2017-07-20 | 2017-11-24 | 中国人民解放军国防科学技术大学 | A kind of radar target detection method based on the graceful divergence of full Donald Bragg |
| CN108535711A (en) * | 2018-03-14 | 2018-09-14 | 南京邮电大学 | A kind of packet-based sea clutter covariance matrix estimation method |
| CN109754018A (en) * | 2019-01-09 | 2019-05-14 | 北京工业大学 | A kind of image-recognizing method of the low-rank locality preserving projections based on F norm |
| US20210041548A1 (en) * | 2019-08-08 | 2021-02-11 | Syracuse University | Motion detection and classification using ambient wireless signals |
| CA3145696A1 (en) * | 2019-08-09 | 2021-02-18 | Ryoma YATAKA | Image change detection device and image change detection method |
| JP2021033711A (en) * | 2019-08-26 | 2021-03-01 | 日本電信電話株式会社 | Abnormality detector, abnormality detection method, and program |
| CN111965632A (en) * | 2020-08-10 | 2020-11-20 | 中国人民解放军国防科技大学 | Radar target detection method based on Riemann manifold dimensionality reduction |
| CN112147603A (en) * | 2020-09-22 | 2020-12-29 | 南京大学 | Target detection method of matrix CFAR detector based on tKL divergence |
Non-Patent Citations (4)
| Title |
|---|
| 刘俊凯;李健兵;马梁;陈忠宽;蔡益朝;: "基于矩阵信息几何的飞机尾流目标检测方法", 雷达学报 * |
| 时艳玲;林毓峰;宛汀;: "部分均匀海杂波中雷达目标的平滑自适应检测", 系统工程与电子技术 * |
| 时艳玲;水鹏朗;: "ANMF和CM-CFAR在K分布海杂波下的性能分析", 西北大学学报(自然科学版) * |
| 赵兴刚;郑岱堃;王首勇;刘俊凯: "一种基于AR模型的矩阵CFAR检测器", 电子学报 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115598616A (en) * | 2022-12-13 | 2023-01-13 | 中国人民解放军国防科技大学（Cn） | Radar target detection method, device and equipment based on power spectrum information geometry |
| CN115598616B (en) * | 2022-12-13 | 2023-03-10 | 中国人民解放军国防科技大学 | Radar target detection method, device and equipment based on power spectrum information geometry |
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