阅读说明：本技术 部分均匀环境中的双子空间信号检测方法、系统及装置 (Method, system and device for detecting double subspace signals in partially uniform environment ) 是由 刘维建 孙梦茹 郝程鹏 于 2021-09-29 设计创作，主要内容包括：本发明涉及部分均匀环境中的双子空间信号检测方法、系统及装置,首先构造信号左矩阵、信号右矩阵、待检测数据矩阵和训练样本矩阵；然后利用训练样本矩阵构造采样协方差矩阵和白化矩阵；再利用白化矩阵对数据进白化；接着构造正交投影矩阵的正交补；然后构造中间变量矩阵,并计算中间变量矩阵的非零特征值；再求解相关方程；接着利用白化后的待检测数据矩阵、中间变量矩阵和方程的解构造检测统计量；进而利用检测统计量和系统设定的虚警概率值确定检测门限；最后比较检测统计量与检测门限的大小,并判定目标是否存在。本发明提供的检测方法一体化实现了杂波抑制、信号积累和恒虚警特性,相比已有方法,提高了检测性能。(The invention relates to a method, a system and a device for detecting a double subspace signal in a partially uniform environment, which comprises the steps of firstly constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix; then constructing a sampling covariance matrix and a whitening matrix by using the training sample matrix; then, whitening the data by utilizing the whitening matrix; then constructing an orthogonal complement of the orthogonal projection matrix; then constructing an intermediate variable matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix; then solving a correlation equation; secondly, constructing detection statistics by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation; determining a detection threshold by using the detection statistic and the false alarm probability value set by the system; and finally, comparing the detection statistic with the detection threshold, and judging whether the target exists or not. The detection method provided by the invention integrally realizes clutter suppression, signal accumulation and constant false alarm characteristics, and improves the detection performance compared with the existing method.)

1. A method for detecting a dual subspace signal in a partially uniform environment, comprising:

step 1: constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix,

wherein the constructed signal left matrix, signal right matrix, data matrix to be detected and training sample matrix are respectively used as matrix、、Andrepresentation, matrix、、Andrespectively of dimension、、Andwherein, in the step (A),，，the extended dimension of the target is represented,represents the number of training samples and the number of training samples,representing the number of system channels;

step 2: constructing a sampling covariance matrix and a whitening matrix by using the training sample matrix, wherein the sampling covariance matrix and the whitening matrix constructed by using the training sample matrix are respectivelyAndwherein, in the step (A),andrespectively representing the sampling covariance matrix and whitening matrix, symbolsWhich represents the transpose of the conjugate,symbol ofThe inverse of the matrix is represented and,representing a diagonal matrix, the diagonal elements being respectively，For sampling covariance matrixThe decomposition of the characteristic value of (a),is composed ofIs determined by the characteristic matrix of (a),in the form of a diagonal matrix,is composed ofIs/are as followsA characteristic value;

and step 3: whitening the data matrix to be detected and the signal left matrix by using the whitening matrix to respectively obtain a whitened data matrix to be detected and a whitened signal left matrix;

and 4, step 4: constructing orthogonal complements of orthogonal projection matrixes by using the signal right matrix and the whitened signal left matrix respectively;

and 5: constructing an intermediate variable matrix by utilizing the whitening data matrix to be detected and the orthogonal complement of the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

step 6: solving a solution of a system dimension and the non-zero eigenvalue correlation equation;

and 7: constructing a detection statistic by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation;

and 8: determining a detection threshold by using the detection statistic and a preset false alarm probability value;

and step 9: and comparing the detection statistic with the detection threshold, and judging whether the target exists or not.

2. The method according to claim 1, wherein in step 3, the whitening matrix is used to whiten the data matrix to be detected and the signal left matrix, so as to obtain a whitened data matrix to be detected and a whitened signal left matrix, respectively, which are implemented by the following two equations:and；

wherein the content of the first and second substances,andrespectively representing a whitened to-be-detected data matrix and a whitened signal left matrix.

3. The method according to claim 2, wherein in step 4, the orthogonal complement of the orthogonal projection matrix constructed by the signal right matrix and the whitened signal left matrix is

Andwherein, in the step (A),andrespectively representing the orthogonal complements of orthogonal projection matrices respectively obtained using the signal right matrix and the whitened signal left matrix,andrespectively is dimension ofAndthe identity matrix of (2).

4. The method according to claim 3, wherein in step 5, the intermediate variable matrices constructed by orthogonal complement of the whitened data matrix to be detected and the orthogonal projection matrix are respectively the whitening matrix、、Andwherein, the matrixAndrespectively, the number of the non-zero eigenvalues of、、Andsymbol ofRepresenting the minimum of two real numbers, the matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue andthe non-zero eigenvalues are respectively recorded as、、Andwherein, in the step (A),、、and。

5. the method according to claim 4, wherein in step 6, the solution of the system dimension and the non-zero eigenvalue correlation equation is solved, wherein the system dimension and the non-zero eigenvalue correlation equation are the following two equations:

(6a)

(6b)

wherein, in equation (6a)As an unknown number, in equation (6b)For the unknowns, the solutions of equation (6a) and equation (6b) are written asAnd。

6. the method according to claim 5, wherein in step 7, the detection statistics constructed by the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation are:

wherein the content of the first and second substances,representing detection statistics, symbolsRepresenting the determinant of the matrix.

7. The method of claim 6, wherein in step 8, the detection threshold is determined by using the detection statistic and the preset false alarm probability value according to the following formula:

；

wherein the content of the first and second substances,，the number of monte carlo simulations is shown,a preset value of the false alarm probability is represented,it is shown that the rounding operation is performed,which is indicative of a detection threshold for the signal,representing a sequence

Arranged from large to smallA maximum value of at least one of, wherein,andare respectively an equation

(8a)

And

(8b)

the solution of (1);

in equation (8a)As an unknown number, in equation (8b)In order to be an unknown number,andare respectively a matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue anda non-zero eigenvalue, and、、andmatrix ofAndare respectively defined as

，

，

，

And

；

wherein the content of the first and second substances,indicating whitening of the data matrix to be detectedIn the second implementation, the first and second antennas are connected,representing a whitened signalFirst of the left matrixIn the second implementation, the first and second antennas are connected,second of orthogonal complement of orthogonal projection matrix representing left matrix of whitened signalIn the second implementation, the first and second antennas are connected,to represent the whitening matrixIn the second implementation, the first and second antennas are connected,is the first of the covariance matrix of the sampleThe sub-realized decomposition of the eigenvalues,representing the covariance matrix of the samplesIn the second implementation, the first and second antennas are connected,for training the sample matrixIn the second implementation, the first and second antennas are connected,is only composed ofData matrix to be detected of clutter and thermal noise componentsIn the second implementation, the first and second antennas are connected,。

8. the method according to any one of claims 1-7, wherein in step 9, the comparing the detection statistic with the detection threshold and determining whether the target exists is performed by:

if the detection statistic is greater than or equal to a detection threshold, judging that a target exists;

and if the detection statistic is smaller than the detection threshold, judging that the target does not exist.

9. A dual subspace signal detection system in a partially uniform environment is characterized by comprising a data matrix construction module, a sampling covariance matrix and whitening matrix construction module, a data whitening module, an orthogonal projection matrix construction module, an intermediate variable matrix and non-zero eigenvalue decomposition module, an equation solving module, a detection statistic construction module, a detection threshold calculation module and a target judgment module;

a data matrix construction module: the device is used for constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix;

a sampling covariance matrix and whitening matrix construction module: the whitening matrix is constructed by utilizing the sampling covariance matrix;

a data whitening module: the whitening matrix is used for whitening the data matrix to be detected and the signal left matrix to obtain a whitened data matrix to be detected and a whitened signal left matrix respectively;

an orthogonal projection matrix construction module: an orthogonal complement for constructing an orthogonal projection matrix using the signal right matrix and the whitened signal left matrix, respectively;

the intermediate variable matrix and non-zero eigenvalue decomposition module: the orthogonal complement is used for constructing an intermediate variable matrix by utilizing the whitened data matrix to be detected and the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

an equation solving module: a solution for solving an equation relating the system dimension to the non-zero eigenvalue;

a detection statistic construction module: the method is used for utilizing the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation to construct detection statistics;

a detection threshold calculation module: the detection threshold is determined by utilizing the detection statistic and the preset false alarm probability value;

a target determination module: for comparing the detection statistic with the detection threshold and determining whether a target exists.

10. An apparatus for detecting a dual subspace signal in a partially homogeneous environment, comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor and programmed to perform the method for dual subspace signal detection in a partially homogeneous environment of any of claims 1-8.

Technical Field

The invention relates to the technical field of signal detection, in particular to a method, a system and a device for detecting a double-subspace signal in a partial uniform environment.

Background

With the continuous increase of the number of channels in a radar space domain, the maturity of a pulse coherent processing technology and the improvement of the radar distance resolution, a radar can simultaneously acquire data through a plurality of channels and a plurality of dimensions, corresponding radar data presents a matrix form and has a specific structure, one data type is a double subspace signal, the data exists in the matrix form, and row elements and column elements of the signal are both located in a known subspace, but corresponding coordinates are unknown.

In addition, since the target is often located on a strong clutter background, the clutter power is several orders of magnitude higher than the target echo power. In order to successfully detect the target, a large number of training samples are typically required to estimate the clutter covariance matrix and suppress the clutter. Then, due to the influence of the distance variation of the environment, the number of radar channels and the like, the radar receiving data often presents non-uniform characteristics, and the number of training samples which are independently distributed in the same way is very limited. And the effective detection of the dual subspace signals under the strong clutter background is restricted.

In view of the above, overcoming the drawbacks of the prior art is an urgent problem in the art.

Disclosure of Invention

The invention aims to solve the problem of detecting the double subspace signals when training samples are insufficient in a part of uniform environments.

The invention adopts the following technical scheme:

in a first aspect, the present invention provides a method for detecting a dual subspace signal in a partially uniform environment, comprising:

step 1: constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix,

wherein the constructed signal left matrix, signal right matrix, data matrix to be detected and training sample matrix are respectively used as matrix、、Andrepresentation, matrix、、Andrespectively of dimension、、Andwherein, in the step (A),，，the extended dimension of the target is represented,represents the number of training samples and the number of training samples,representing the number of system channels;

step 2: constructing a sampling covariance matrix and a whitening matrix by using the training sample matrix, wherein the sampling covariance matrix and the whitening matrix constructed by using the training sample matrix are respectivelyAndwherein, in the step (A),andrespectively representing the sampling covariance matrix and whitening matrix, symbolsWhich represents the transpose of the conjugate,symbol ofThe inverse of the matrix is represented and,representing a diagonal matrix, the diagonal elements being respectively，For sampling covariance matrixThe decomposition of the characteristic value of (a),is composed ofIs determined by the characteristic matrix of (a),in the form of a diagonal matrix,is composed ofIs/are as followsA characteristic value;

and step 3: whitening the data matrix to be detected and the signal left matrix by using the whitening matrix to respectively obtain a whitened data matrix to be detected and a whitened signal left matrix;

and 4, step 4: constructing orthogonal complements of orthogonal projection matrixes by using the signal right matrix and the whitened signal left matrix respectively;

and 5: constructing an intermediate variable matrix by utilizing the whitening data matrix to be detected and the orthogonal complement of the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

step 6: solving a solution of a system dimension and the non-zero eigenvalue correlation equation;

and 7: constructing a detection statistic by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation;

and 8: determining a detection threshold by using the detection statistic and a preset false alarm probability value;

and step 9: and comparing the detection statistic with the detection threshold, and judging whether the target exists or not.

Preferably, in the step 3, the whitening matrix is used to whiten the data matrix to be detected and the signal left matrix, so as to obtain a whitened data matrix to be detected and a whitened signal left matrix respectivelyThe matrix is realized by the following two equations respectively:and；

wherein the content of the first and second substances,andrespectively representing a whitened to-be-detected data matrix and a whitened signal left matrix.

Preferably, in the step 4, the orthogonal complements of the orthogonal projection matrices constructed by using the signal right matrix and the whitened signal left matrix are respectively

Andwherein, in the step (A),andrespectively representing the orthogonal complements of orthogonal projection matrices respectively obtained using the signal right matrix and the whitened signal left matrix,andrespectively is dimension ofAndthe identity matrix of (2).

Preferably, in step 5, the intermediate variable matrices constructed by orthogonal complement of the whitened data matrix to be detected and the orthogonal projection matrix are respectively、、Andwherein, the matrixAndrespectively, the number of the non-zero eigenvalues of、、Andsymbol ofRepresenting the minimum of two real numbers, the matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue andthe non-zero eigenvalues are respectively recorded as、、Andwherein, in the step (A),、、and。

preferably, in step 6, a solution of the equation relating the system dimension and the non-zero eigenvalue is solved, wherein the equation relating the system dimension and the non-zero eigenvalue is the following two equations:

(6a)

(6b)

wherein, in equation (6a)As an unknown number, in equation (6b)For the unknowns, the solutions of equation (6a) and equation (6b) are written asAnd。

preferably, in step 7, the detection statistics constructed by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation are as follows:

wherein the content of the first and second substances,representing detection statistics, symbolsRepresenting the determinant of the matrix.

Preferably, in step 8, the formula for determining the detection threshold by using the detection statistic and the preset false alarm probability value is as follows:

；

wherein the content of the first and second substances,，the number of monte carlo simulations is shown,a preset value of the false alarm probability is represented,it is shown that the rounding operation is performed,which is indicative of a detection threshold for the signal,representing a sequence

Arranged from large to smallA maximum value of at least one of, wherein,andare respectively an equation

(8a)

And

(8b)

the solution of (1);

in equation (8a)As an unknown number, in equation (8b)In order to be an unknown number,andare respectively a matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue anda non-zero eigenvalue, and、、andmatrix ofAndare respectively defined as

，

，

，

And

；

wherein the content of the first and second substances,indicating whitening of the data matrix to be detectedIn the second implementation, the first and second antennas are connected,representing the left matrix of the whitened signalIn the second implementation, the first and second antennas are connected,orthogonal projection matrix representing left matrix of whitened signalOf quadrature complement ofIn the second implementation, the first and second antennas are connected,to represent the whitening matrixIn the second implementation, the first and second antennas are connected,is the first of the covariance matrix of the sampleThe sub-realized decomposition of the eigenvalues,representing the covariance matrix of the samplesIn the second implementation, the first and second antennas are connected,for training the sample matrixIn the second implementation, the first and second antennas are connected,for the data matrix to be detected containing only clutter and thermal noise componentsIn the second implementation, the first and second antennas are connected,。

preferably, in step 9, the comparing the detection statistic with the detection threshold and determining whether the target exists includes:

if the detection statistic is greater than or equal to a detection threshold, judging that a target exists;

and if the detection statistic is smaller than the detection threshold, judging that the target does not exist.

In a second aspect, the invention further provides a dual subspace signal detection system in a partially uniform environment, which comprises a data matrix construction module, a sampling covariance matrix and whitening matrix construction module, a data whitening module, an orthogonal projection matrix construction module, an intermediate variable matrix and non-zero eigenvalue decomposition module, an equation solving module, a detection statistic construction module, a detection threshold calculation module and a target judgment module;

a data matrix construction module: the device is used for constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix;

a sampling covariance matrix and whitening matrix construction module: the whitening matrix is constructed by utilizing the sampling covariance matrix;

a data whitening module: the whitening matrix is used for whitening the data matrix to be detected and the signal left matrix to obtain a whitened data matrix to be detected and a whitened signal left matrix respectively;

an orthogonal projection matrix construction module: an orthogonal complement for constructing an orthogonal projection matrix using the signal right matrix and the whitened signal left matrix, respectively;

the intermediate variable matrix and non-zero eigenvalue decomposition module: the orthogonal complement is used for constructing an intermediate variable matrix by utilizing the whitened data matrix to be detected and the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

an equation solving module: a solution for solving an equation relating the system dimension to the non-zero eigenvalue;

a detection statistic construction module: the method is used for utilizing the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation to construct detection statistics;

a detection threshold calculation module: the detection threshold is determined by utilizing the detection statistic and the preset false alarm probability value;

a target determination module: for comparing the detection statistic with the detection threshold and determining whether a target exists.

In a third aspect, the present invention also provides an apparatus for dual subspace signal detection in a partially homogeneous environment, comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor and programmed to perform the method of dual subspace signal detection in a partially homogeneous environment of the first aspect.

Compared with the prior art, the invention has the beneficial effects that:

(1) the invention constructs an intermediate variable matrix、、Andand non-zero eigenvalues of these matrices in combination with the data matrix to be detectedDetection statistics is organically constructed, the integration of clutter suppression, signal accumulation and constant false alarm processing is realized, and the signal detection flow is simplified;

(2) the detection method designed by the invention realizes the constant false alarm characteristic in partial uniform environment, namely: the detection threshold does not depend on the covariance structural matrix and the covariance mismatching quantity, so that an additional constant false alarm processing step is not needed, and the detection flow is simplified;

(3) compared with the existing detection method, the detection method designed by the invention has higher detection efficiency and detection probability.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

FIG. 1 is a flow chart of a method for detecting dual subspace signals in a partially homogeneous environment according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of a method for detecting dual subspace signals in a partially uniform environment according to an embodiment of the present invention;

FIG. 3 is a block diagram of a dual subspace signal detection system in a partially homogeneous environment, according to an embodiment of the present invention;

fig. 4 is a block diagram of an apparatus for detecting dual subspace signals in a partially uniform environment according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.

In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

By usingDimension matrixRepresenting the detected matrix value bi-subspace signal having the structure shown below

(1)

Wherein the content of the first and second substances,dimension matrixAnddimension matrixReferred to as the signal left matrix and the signal right matrix, respectively, which are column full rank and row full rank, respectively, and are known,dimension matrixUnknown, representing the coordinate information of the signal. By usingDimension matrixIndicating the data matrix to be detected, and when the data matrix to be detected contains the target signal, detecting the data matrix to be detectedCan be written as

(2)

Wherein the content of the first and second substances,dimension matrixRepresenting the noise component in the data to be detected, as the sum of clutter and thermal noise,each column of (a) is independently and identically distributed, subject to a mean of zero and a covariance matrix ofA gaussian distribution of (a). In practical applicationUnknown, a certain amount of training sample data is needed to estimate it. Suppose there isTraining samples containing only clutter and thermal noise componentsA training sample () Is composed of

(3)

Wherein the content of the first and second substances,is as followsThe sum of the clutter and thermal noise components in each training sample. For a partially homogeneous environment, the ambient temperature is,is recorded as the covariance matrixAnd the covariance matrix of the data to be detected has a relation:wherein, in the step (A),and representing the covariance matrix mismatch between the data to be detected and the training sample data for unknown numbers.

Example 1:

the present embodiment provides a method for detecting dual subspace signals in a partially uniform environment, as shown in fig. 1-2, including:

step 1: constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix,

wherein the constructed signal left matrix, signal right matrix, data matrix to be detected and training sample matrix are respectively used as matrix、、Andrepresentation, matrix、、Andrespectively of dimension、、Andwherein, in the step (A),，，the extended dimension of the target is represented,represents the number of training samples and the number of training samples,representing the number of system channels;

step 2: constructing a sampling covariance matrix and a whitening matrix by using the training sample matrix, wherein the sampling covariance matrix and the whitening matrix constructed by using the training sample matrix are respectivelyAndwherein, in the step (A),andrespectively representing the sampling covariance matrix and whitening matrix, symbolsWhich represents the transpose of the conjugate,symbol ofThe inverse of the matrix is represented and,representing a diagonal matrix, the diagonal elements being respectively，For sampling covariance matrixThe decomposition of the characteristic value of (a),is composed ofIs determined by the characteristic matrix of (a),in the form of a diagonal matrix,is composed ofIs/are as followsA characteristic value;

and step 3: whitening the data matrix to be detected and the signal left matrix by using the whitening matrix to respectively obtain a whitened data matrix to be detected and a whitened signal left matrix;

in the step 3, the whitening matrix is used to whiten the data matrix to be detected and the signal left matrix, so as to obtain a whitened data matrix to be detected and a whitened signal left matrix, which are respectively implemented by the following two equations:and(ii) a Wherein the content of the first and second substances,andrespectively representing a whitened to-be-detected data matrix and a whitened signal left matrix.

And 4, step 4: constructing orthogonal complements of orthogonal projection matrixes by using the signal right matrix and the whitened signal left matrix respectively;

in the step 4, the orthogonal complements of the orthogonal projection matrixes constructed by using the signal right matrix and the whitened signal left matrix are respectively

Andwherein, in the step (A),andrespectively representing the orthogonal complements of orthogonal projection matrices respectively obtained using the signal right matrix and the whitened signal left matrix,andrespectively is dimension ofAndthe identity matrix of (2).

And 5: constructing an intermediate variable matrix by utilizing the whitening data matrix to be detected and the orthogonal complement of the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

in the step 5, the intermediate variable matrixes constructed by the orthogonal complement of the whitened data matrix to be detected and the orthogonal projection matrix are respectively、、Andwherein, the matrixAndrespectively, the number of the non-zero eigenvalues of、、Andsymbol ofRepresenting the minimum of two real numbers, the matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue andthe non-zero eigenvalues are respectively recorded as、、Andwherein, in the step (A),、、and。

step 6: solving a solution of a system dimension and the non-zero eigenvalue correlation equation;

in the step 6, a solution of the system dimension and the non-zero eigenvalue correlation equation is solved, wherein the system dimension and the non-zero eigenvalue correlation equation are the following two equations:

(6a)

(6b)

wherein, in equation (6a)As an unknown number, in equation (6b)For the unknowns, the solutions of equation (6a) and equation (6b) are written asAnd。

and 7: constructing a detection statistic by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation;

in step 7, the detection statistics constructed by using the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation are as follows:

wherein the content of the first and second substances,representing detection statistics, symbolsRepresenting the determinant of the matrix.

And 8: determining a detection threshold by using the detection statistic and a preset false alarm probability value;

in step 8, the formula for determining the detection threshold by using the detection statistic and the preset false alarm probability value is as follows:

；

wherein the content of the first and second substances,，the number of monte carlo simulations is shown,a preset value of the false alarm probability is represented,it is shown that the rounding operation is performed,which is indicative of a detection threshold for the signal,representing a sequence

Arranged from large to smallA maximum value of at least one of, wherein,andare respectively an equation

(8a)

And

(8b)

the solution of (1);

in equation (8a)As an unknown number, in equation (8b)In order to be an unknown number,andare respectively a matrixAndto (1) aA non-zero eigenvalue, noA non-zero eigenvalue, noA non-zero eigenvalue anda non-zero eigenvalue, and、、andmatrix ofAndare respectively defined as

，

，

，

And

；

wherein the content of the first and second substances,indicating whitening of the data matrix to be detectedIn the second implementation, the first and second antennas are connected,representing the left matrix of the whitened signalIn the second implementation, the first and second antennas are connected,second of orthogonal complement of orthogonal projection matrix representing left matrix of whitened signalIn the second implementation, the first and second antennas are connected,to represent the whitening matrixIn the second implementation, the first and second antennas are connected,is the first of the covariance matrix of the sampleThe sub-realized decomposition of the eigenvalues,representing the covariance matrix of the samplesIn the second implementation, the first and second antennas are connected,for training the sample matrixIn the second implementation, the first and second antennas are connected,for the data matrix to be detected containing only clutter and thermal noise componentsIn the second implementation, the first and second antennas are connected,。[0031]and step 9: and comparing the detection statistic with the detection threshold, and judging whether the target exists or not.

In step 9, the comparing the detection statistic with the detection threshold and determining whether the target exists specifically includes:

if the detection statisticGreater than or equal to the detection thresholdIf yes, judging that the target exists;

if the detection statisticLess than the detection thresholdThen the target is determined to be absent.

The embodiment also provides a dual subspace signal detection system in a partially uniform environment, as shown in fig. 3, which includes a data matrix construction module, a sampling covariance matrix and whitening matrix construction module, a data whitening module, an orthogonal projection matrix construction module, an intermediate variable matrix and non-zero eigenvalue decomposition module, an equation solving module, a detection statistic construction module, a detection threshold calculation module, and a target judgment module;

a data matrix construction module: the device is used for constructing a signal left matrix, a signal right matrix, a data matrix to be detected and a training sample matrix;

a sampling covariance matrix and whitening matrix construction module: the whitening matrix is constructed by utilizing the sampling covariance matrix;

a data whitening module: the whitening matrix is used for whitening the data matrix to be detected and the signal left matrix to obtain a whitened data matrix to be detected and a whitened signal left matrix respectively;

an orthogonal projection matrix construction module: an orthogonal complement for constructing an orthogonal projection matrix using the signal right matrix and the whitened signal left matrix, respectively;

the intermediate variable matrix and non-zero eigenvalue decomposition module: the orthogonal complement is used for constructing an intermediate variable matrix by utilizing the whitened data matrix to be detected and the orthogonal projection matrix, and calculating a non-zero eigenvalue of the intermediate variable matrix;

an equation solving module: a solution for solving an equation relating the system dimension to the non-zero eigenvalue;

a detection statistic construction module: the method is used for utilizing the whitened data matrix to be detected, the intermediate variable matrix and the solution of the equation to construct detection statistics;

a detection threshold calculation module: the detection threshold is determined by utilizing the detection statistic and the preset false alarm probability value;

a target determination module: for comparing the detection statistic with the detection threshold and determining whether a target exists.

Example 2:

on the basis of the method for detecting a dual subspace signal in a partially uniform environment provided in embodiment 1, the present invention further provides a device for detecting a dual subspace signal in a partially uniform environment, which is capable of implementing the method described above, and as shown in fig. 4, the device is schematically illustrated in the device architecture of the embodiment of the present invention. The dual subspace signal detection apparatus in a partially homogeneous environment of the present embodiment comprises one or more processors 21 and a memory 22. In fig. 4, one processor 21 is taken as an example.

The processor 21 and the memory 22 may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.

The memory 22, as a non-volatile computer-readable storage medium for a dual subspace signal detection method in a partially homogeneous environment, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the dual subspace signal detection method in the partially homogeneous environment of embodiment 1. The processor 21 executes various functional applications and data processing of the dual subspace signal detection apparatus in the partially uniform environment by executing the nonvolatile software program, instructions and modules stored in the memory 22, that is, implements the dual subspace signal detection method in the partially uniform environment of embodiment 1.

The memory 22 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 22 may optionally include memory located remotely from the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The program instructions/modules are stored in the memory 22 and, when executed by the one or more processors 21, perform the dual subspace signal detection method in a partially homogeneous environment of embodiment 1 described above, e.g., perform the various steps illustrated in fig. 1 described above.

Those of ordinary skill in the art will appreciate that all or part of the steps of the various methods of the embodiments may be implemented by associated hardware as instructed by a program, which may be stored on a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.