CN111157956A - Radar signal mismatch sensitivity detection method and system under non-Gaussian background - Google Patents
Radar signal mismatch sensitivity detection method and system under non-Gaussian background Download PDFInfo
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Abstract
The embodiment of the invention provides a radar signal mismatch sensitivity detection method and system under a non-Gaussian background. The method comprises the following steps: expressing the detection problem of the distance unit data to be detected by adopting binary hypothesis test to obtain a first expression and a second expression; respectively calculating probability density functions of the distance unit data to be detected; expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression; calculating the integral of the texture component by the probability density function and the maximum likelihood estimation of the first amplitude parameter and the second amplitude parameter; substituting the values into a two-step generalized likelihood ratio criterion expression to obtain an inspection expression under the non-uniform environment when the calculation covariance matrix is known; and substituting the maximum likelihood estimation of the covariance matrix into a test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists. The embodiment of the invention effectively improves the identification performance of the mismatch signal.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a radar signal mismatch sensitivity detection method and system under a non-Gaussian background.
Background
In the field of radar signal processing, detecting signals in a noise environment where the covariance matrix is unknown is the most fundamental problem in the field.
In recent years, scholars at home and abroad propose a series of self-adaptive detection methods such as a generalized likelihood ratio detection method, a second-order generalized likelihood ratio detection method, a Rao detection method and the like, wherein the detection problems are represented by binary hypothesis test, and whether a target exists in data to be detected is judged according to different decision criteria. Since the noise covariance matrix is unknown, sufficient training samples are usually required to estimate the covariance matrix of the noise, and these training samples exist in the range unit adjacent to the data to be detected and have the same noise distribution as the data to be detected.
A great deal of research is carried out on the target adaptive detection problem under the background of Gaussian noise, however, the assumption of the Gaussian noise is difficult to satisfy in the actual radar scene, and particularly in the dense target environment, the land-water junction and other scenes, the Gaussian noise model fails. The complex gaussian model is a widely used non-gaussian model. In this model, the noise is represented as the product of a slowly varying texture component and a rapidly varying speckle component. In a conventional adaptive detection method, detection is usually performed under the condition that a steering vector of a target is completely known, however, in an actual scene, the steering vector of the target is uncertain, and the existence of beam pointing errors, antenna calibration errors and the like causes a deviation between a preset steering vector and a real steering vector, that is, a signal mismatch is generated. At this time, the detection using the conventional adaptive detection method may cause a serious performance loss.
Disclosure of Invention
The embodiment of the invention provides a radar signal mismatch sensitivity detection method and a radar signal mismatch sensitivity detection system under a non-Gaussian background, which are used for solving the problem of mismatch between a target actual steering vector and a preset steering vector caused by beam pointing errors, antenna calibration errors and the like in the prior art.
In a first aspect, an embodiment of the present invention provides a method for detecting a radar signal mismatch sensitivity under a non-gaussian background, including:
obtaining data of a distance unit to be detected received by a radar, representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis;
respectively calculating a first probability density function of the to-be-detected distance unit data under the assumption that the to-be-detected distance unit data does not contain a target signal and a second probability density function under the assumption that the to-be-detected distance unit data contains the target signal based on the first expression and the second expression;
extracting texture components of noise components in the first expression and the second expression, and expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression;
calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate for a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate for a second magnitude parameter in the second expression;
substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression under a non-uniform environment when the calculation covariance matrix is known;
and calculating the maximum likelihood estimation of the covariance matrix by using a training sample, substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists.
Preferably, the acquiring distance unit data to be detected received by the radar, and representing the detection problem of the distance unit data to be detected by adopting binary hypothesis testing to obtain a first expression without a target signal hypothesis and a second expression with the target signal hypothesis specifically include:
expressing the distance unit data to be detected by using a plurality of complex Gaussian vectors, and acquiring the training sample;
and obtaining the first expression and the second expression by adopting the binary hypothesis test representation based on the complex Gaussian vectors and the training samples, wherein noise components in the first expression and the second expression are represented as speckle components and texture components by a composite Gaussian model, the first expression comprises a virtual interference signal, the second expression comprises a preset guide vector, and the virtual interference signal and the guide vector are orthogonal in a real whitening space.
Preferably, the calculating a first probability density function of the to-be-detected distance unit data under the assumption that no target signal is contained and a second probability density function under the assumption that the target signal is contained based on the first expression and the second expression respectively specifically includes:
under the assumption that no target signal is contained, calculating to obtain the first probability density function of the distance unit data to be detected;
and under the assumption of containing a target signal, calculating to obtain the second probability density function of the distance unit data to be detected.
Preferably, the extracting texture components of the noise components in the first expression and the second expression, and representing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression specifically includes:
supposing that the texture component is a random variable and obeys inverse gamma distribution, and calculating to obtain a texture variable probability density function;
and obtaining the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function.
Preferably, the calculating the integral of the first probability density function to the texture component and the maximum likelihood estimate of the first amplitude parameter in the first expression, the calculating the integral of the second probability density function to the texture component and the maximum likelihood estimate of the second amplitude parameter in the second expression specifically includes:
calculating to obtain an integral of the texture component by the first probability density function based on the texture variable probability density function to obtain a first integral expression, and calculating a first derivative of a first amplitude parameter in the first expression from the first integral expression, and making the first derivative equal to 0 to obtain a maximum likelihood estimation of the first amplitude parameter;
and calculating to obtain the integral of the second probability density function to the texture component based on the texture variable probability density function to obtain a second integral expression, solving a second derivative related to a second amplitude parameter in the second expression from the second integral expression, and enabling the second derivative to be equal to 0 to obtain the maximum likelihood estimation of the second amplitude parameter.
Preferably, the substituting the first probability function, the second probability density function, the maximum likelihood estimate of the first magnitude parameter, and the maximum likelihood estimate of the second magnitude parameter into the two-step generalized likelihood ratio criterion expression to obtain the checking expression in the non-uniform environment when the computation covariance matrix is known specifically includes:
and substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function, and simplifying under the condition that a real whitening space is orthogonal by using a virtual interference signal and a preset guide vector to obtain the test expression.
Preferably, the calculating a maximum likelihood estimate of the covariance matrix by using a training sample, substituting the maximum likelihood estimate of the covariance matrix into the check expression to obtain a check result, and comparing the check result with a detection threshold to determine whether the target signal exists, specifically includes:
calculating the maximum likelihood estimation of the covariance matrix by using a training sample, and substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain the test result;
and comparing the detection result with the detection threshold, if the detection result is greater than the detection threshold, judging that the target signal exists in the distance unit to be detected, and otherwise, judging that the target signal does not exist in the distance unit to be detected.
In a second aspect, an embodiment of the present invention provides a radar signal mismatch sensitivity detection system in a non-gaussian background, including:
the acquisition and representation module is used for acquiring the data of the distance unit to be detected received by the radar, and representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test to obtain a first expression without a target signal hypothesis and a second expression with the target signal hypothesis;
a first calculating module, configured to calculate, based on the first expression and the second expression, a first probability density function of the distance unit data to be detected under an assumption that the distance unit data does not contain a target signal and a second probability density function under an assumption that the distance unit data to be detected contains the target signal, respectively;
the extraction module is used for extracting texture components of the noise components in the first expression and the second expression, expressing the texture components by inverse gamma distribution and obtaining a two-step generalized likelihood ratio criterion expression;
a second computation module for computing an integral of the first probability density function over the texture component and a maximum likelihood estimate of a first magnitude parameter in the first expression, computing an integral of the second probability density function over the texture component and a maximum likelihood estimate of a second magnitude parameter in the second expression;
a substitution module, configured to substitute the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter, and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression in a non-uniform environment when a computation covariance matrix is known;
and the checking module is used for calculating the maximum likelihood estimation of the covariance matrix by utilizing a training sample, substituting the maximum likelihood estimation of the covariance matrix into the checking expression to obtain a checking result, and comparing the checking result with a detection threshold to judge whether the target signal exists or not.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of any radar signal mismatch sensitivity detection method under the non-Gaussian background when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements any of the steps of the radar signal mismatch sensitivity detection method in the non-gaussian background.
According to the radar signal mismatch sensitivity detection method and system under the non-Gaussian background provided by the embodiment of the invention, by aiming at the mismatch problem existing between the target actual guide vector and the preset guide vector caused by beam pointing error, antenna calibration error and the like, the detection problem is represented by binary hypothesis test, a virtual interference signal is added under the hypothesis without a target, and then a two-step generalized likelihood ratio test criterion is adopted to design the mismatch sensitivity detection method under the non-uniform environment. Compared with the traditional self-adaptive detection method, the mismatch sensitive detection method in the non-uniform environment can effectively improve the identification performance of mismatched signals under the condition of signal mismatch.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a radar signal mismatch sensitivity detection method under a non-gaussian background according to an embodiment of the present invention;
fig. 2 is a comparison graph of detection probability results obtained under different signal-to-noise ratios when there is no signal mismatch with the conventional adaptive detection method according to the embodiment of the present invention;
fig. 3 is a comparison graph of detection probability results obtained by the conventional adaptive detection method under different signal-to-noise ratios when the square of mismatch cosine is 0.4547 according to the embodiment of the present invention;
fig. 4 is a structural diagram of a radar signal mismatch sensitivity detection system under a non-gaussian background according to an embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the problems in the prior art, the embodiment of the invention discloses a radar signal mismatch sensitivity detection method under a non-Gaussian background, which has the following steps: the radar is used for receiving the data z of the distance unit to be detectedl∈CN×11, the detection problem of L is represented by a binary hypothesis test, where H is H0Denotes zl∈CN×1L1.. so, L is the only assumption of noise, H1Denotes zl∈CN×11.. the assumption that there is target and noise in L. And modeling the noise by adopting a composite Gaussian model, wherein the texture component follows inverse gamma distribution. To increase the sensitivity of the detection method to mismatched signals, assume H0Calculating probability density function of to-be-detected distance unit data under two assumptions and integral of the probability density function to texture component, and calculating maximum likelihood estimation of target amplitude α and virtual interference amplitude β according to integral resultAnd obtaining a final detection expression T of the radar signal mismatch sensitivity detection method under the non-Gaussian background by using the generalized likelihood ratio expression when the difference is known. And setting a detection threshold, comparing the detection threshold with a threshold value, judging that the target exists if T is greater than the threshold, and otherwise, judging that the target does not exist.
Fig. 1 is a flowchart of a radar signal mismatch sensitivity detection method under a non-gaussian background according to an embodiment of the present invention, as shown in fig. 1, including:
s1, obtaining data of a distance unit to be detected received by a radar, representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis;
s2, respectively calculating a first probability density function of the distance unit data to be detected under the assumption that the distance unit data to be detected does not contain a target signal and a second probability density function under the assumption that the distance unit data to be detected contains the target signal based on the first expression and the second expression;
s3, extracting texture components of the noise components in the first expression and the second expression, and expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression;
s4, calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate of a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate of a second magnitude parameter in the second expression;
s5, substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain an inspection expression under the non-uniform environment when the calculation covariance matrix is known;
s6, calculating the maximum likelihood estimation of the covariance matrix by using the training sample, substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists.
Specifically, in step S1, after obtaining the range bin data to be detected received by the radar, representing the range bin data by using binary hypothesis testing, where the binary representation includes two cases including no target signal hypothesis and including the target signal hypothesis, and obtaining a first expression including no target signal hypothesis and a second expression including the target signal hypothesis, respectively;
in step S2, further calculating a probability density function of the distance unit data to be detected under the assumption that the distance unit data is not included in the target signal, and recording the probability density function as a first probability density function, and a probability density function of the unit data in the library to be detected under the assumption that the distance unit data is included in the target signal, and recording the probability density function as a second probability density function;
in step S3, extracting texture components from the noise components included in both the first expression and the second expression obtained in step S1, assuming that the texture components are random variables and obey inverse gamma distribution, and solving the detection problem by using a two-step generalized likelihood ratio criterion to obtain a two-step generalized likelihood ratio criterion expression;
in step S4, using the probability density function of the texture component, calculating the integral of the texture component by using the first probability density function, calculating the integral of the texture component by using the second probability density function, and calculating the maximum likelihood estimation of the first amplitude parameter in the first expression and the maximum likelihood estimation of the second amplitude parameter in the second expression according to the integral result;
in step S5, substituting the two maximum likelihood estimation values obtained in step S4 into a two-step generalized likelihood ratio criterion expression to obtain a test expression of the mismatch sensitivity detection method in the non-uniform environment when the calculated covariance matrix is known;
in step S6, the maximum likelihood estimation of the covariance matrix is calculated by using the training samples and is substituted into the check expression obtained in step S5 to obtain a final check result, and finally the check result is compared with a preset check threshold to determine whether the target signal exists.
The embodiment of the invention expresses the detection problem by using binary hypothesis test, adds the virtual interference signal under the hypothesis without a target, and then designs the mismatch sensitive detection method under the non-uniform environment by adopting a two-step generalized likelihood ratio test criterion. Compared with the traditional self-adaptive detection method, the mismatch sensitive detection method in the non-uniform environment can effectively improve the identification performance of mismatched signals under the condition of signal mismatch.
Based on the above embodiment, the obtaining of the distance unit data to be detected received by the radar, and representing the detection problem of the distance unit data to be detected by using binary hypothesis testing to obtain a first expression without a target signal hypothesis and a second expression with the target signal hypothesis specifically include:
expressing the distance unit data to be detected by using a plurality of complex Gaussian vectors, and acquiring the training sample;
and obtaining the first expression and the second expression by adopting the binary hypothesis test representation based on the complex Gaussian vectors and the training samples, wherein noise components in the first expression and the second expression are represented as speckle components and texture components by a composite Gaussian model, the first expression comprises a virtual interference signal, the second expression comprises a preset guide vector, and the virtual interference signal and the guide vector are orthogonal in a real whitening space.
Specifically, the complex Gaussian vector z is adopted for the distance unit data to be detected received by the radarl∈CN×1L is 1, …, L denotes the number of range bins occupied by the target, z denotesl∈CN×1L +1, …, L + K indicating training samples, and K indicating the number of training samples. We want to judge whether the distance unit to be detected contains the target signal, and the detection problem is represented by binary hypothesis test:
wherein H0To indicate a waitDetection of the assumption that the range cell data does not contain a target, H1Indicating the assumption that the range cell data to be detected contains a target. p is epsilon to CN×1Indicating a preset steering vector, αls, L1, L is a definite unknown parameter representing the amplitude, vector nl∈CN×1L + K denotes noise, which obeys a complex gaussian model, i.e. nlCan be expressed as
Where g is the complex circular Gaussian vector with zero mean covariance matrix as sigma representing the speckle component, τlL + K denotes a texture component βlq is a virtual interference signal added to increase the mismatch sensitivity of the detection method, wherein βlRepresenting coordinates, where a virtual interference signal q ∈ C is assumedN×1And a preset steering vector p ∈ CN×1Orthogonalizing in the true whitening space
Is represented by
The space formed by the spread of the columns,
is represented by
The columns of (a) open up as orthogonal complements of space.
Based on any of the above embodiments, the calculating, based on the first expression and the second expression, a first probability density function of the distance unit data to be detected under the assumption that the distance unit data does not contain a target signal and a second probability density function under the assumption that the distance unit data to be detected contains the target signal respectively specifically includes:
under the assumption that no target signal is contained, calculating to obtain the first probability density function of the distance unit data to be detected;
and under the assumption of containing a target signal, calculating to obtain the second probability density function of the distance unit data to be detected.
Specifically, according to the assumption of the above embodiment, H is assumed0Distance unit data z to be detected under the condition1...,zLProbability density function f (z)1...,zL|β，τ,∑,H0) The expression of (a) is:
suppose H1Distance unit data z to be detected under the condition1...,zLProbability density function f (z)1...,zL|α，τ,∑,H1) The expression of (a) is:
wherein, (.)HDenotes the conjugate transpose, det (-) denotes the determinant.
Based on any of the above embodiments, the calculating, based on the first expression and the second expression, a first probability density function of the distance unit data to be detected under the assumption that the distance unit data does not contain a target signal and a second probability density function under the assumption that the distance unit data to be detected contains the target signal respectively specifically includes:
under the assumption that no target signal is contained, calculating to obtain the first probability density function of the distance unit data to be detected;
and under the assumption of containing a target signal, calculating to obtain the second probability density function of the distance unit data to be detected.
In particular, assume that the texture component τlL + K is a random variable and follows an inverse gamma distribution, the probability density function of which is f (τ)l) The expression of (a) is:
wherein Γ (·) represents a gamma function, vlDenotes a shape parameter, ulThe scale parameter is indicated. The detection problem (1) is solved by adopting a two-step generalized likelihood ratio criterion, wherein the expression of the two-step generalized likelihood ratio criterion is as follows:
based on any of the above embodiments, the calculating the integral of the first probability density function to the texture component and the maximum likelihood estimation of the first amplitude parameter in the first expression, and the calculating the integral of the second probability density function to the texture component and the maximum likelihood estimation of the second amplitude parameter in the second expression specifically include:
calculating to obtain an integral of the texture component by the first probability density function based on the texture variable probability density function to obtain a first integral expression, and calculating a first derivative of a first amplitude parameter in the first expression from the first integral expression, and making the first derivative equal to 0 to obtain a maximum likelihood estimation of the first amplitude parameter;
and calculating to obtain the integral of the second probability density function to the texture component based on the texture variable probability density function to obtain a second integral expression, solving a second derivative related to a second amplitude parameter in the second expression from the second integral expression, and enabling the second derivative to be equal to 0 to obtain the maximum likelihood estimation of the second amplitude parameter.
In particular, the texture component τ is utilizedlProbability density function of L1, …, L + K and hypothesis H0Distance unit data z to be detected under the condition1…,zLProbability density function f (z)1…,zL|β，τ,∑,H0) Calculating the integral of the probability density function to the texture component, wherein the obtained integral result expression is as follows:
derivative about β for the integration result (6) and let the derivative be 0 to get a maximum likelihood estimate of β:
using texture components taul1, a, probability density function of L + K and hypothesis H1Distance unit data z to be detected under the condition1...,zLProbability density function f (z)1...,zL|α，τ,∑,H1) The probability density function is integrated for the texture component and the derivative is calculated for α for the integration result, making the derivative 0 yields a maximum likelihood estimate of α:
based on any of the above embodiments, substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter, and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain the check expression in the non-uniform environment when the computed covariance matrix is known specifically includes:
and substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function, and simplifying under the condition that a real whitening space is orthogonal by using a virtual interference signal and a preset guide vector to obtain the test expression.
Preferably, the calculating a maximum likelihood estimate of the covariance matrix by using a training sample, substituting the maximum likelihood estimate of the covariance matrix into the check expression to obtain a check result, and comparing the check result with a detection threshold to determine whether the target signal exists, specifically includes:
calculating the maximum likelihood estimation of the covariance matrix by using a training sample, and substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain the test result;
and comparing the detection result with the detection threshold, if the detection result is greater than the detection threshold, judging that the target signal exists in the distance unit to be detected, and otherwise, judging that the target signal does not exist in the distance unit to be detected.
In particular, the texture component τ is divided intol1.. the probability density function of L + K, assuming H0Distance unit data z to be detected under the condition1...,zLProbability density function f (z)1...,zL|β，τ,∑,H0) Suppose H1Distance unit data z to be detected under the condition1...,zLProbability density function f (z)1...,zL|α，τ,∑,H1) Maximum likelihood estimation of parameter β, maximum likelihood estimation of parameter α, and substitution into (5) with condition q ∈ CN×1And a preset steering vector p ∈ CN×1Orthogonalizing in the true whitening space
Simplifying the checking expression to obtain the checking expression of the mismatch sensitivity detection method under the non-uniform environment when the calculation covariance matrix sigma is known:
based on any of the above embodiments, the calculating a maximum likelihood estimate of the covariance matrix using a training sample, substituting the maximum likelihood estimate of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to determine whether the target signal exists specifically includes:
calculating the maximum likelihood estimation of the covariance matrix by using a training sample, and substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain the test result;
and comparing the detection result with the detection threshold, if the detection result is greater than the detection threshold, judging that the target signal exists in the distance unit to be detected, and otherwise, judging that the target signal does not exist in the distance unit to be detected.
In particular, using training samples z1∈CN×1T + L + 1.. the maximum likelihood estimate of the covariance matrix ∑ is solved by L + K
And substituting the maximum likelihood estimation S of the covariance matrix into a test expression (9) to obtain a final test expression of the mismatch sensitive detection method under the non-uniform environment.
And comparing the value of the mismatch sensitivity detection expression T in the non-uniform environment with a detection threshold η of the set mismatch sensitivity detection method in the non-uniform environment, wherein if the value of the mismatch sensitivity detection expression T in the non-uniform environment is greater than the detection threshold η, the target signal is judged to exist in the distance unit to be detected, and otherwise, the target signal does not exist in the distance unit to be detected.
Based on any of the above embodiments, the effects of the embodiments of the present invention are further verified and explained by the following simulation experiments.
Firstly, determining the experimental environment and the specific content:
the experimental environment adopts MATLAB R2010b, Intel (R) Pentium (R)2CPU 2.7GHz and Window 7 flagship edition; the experimental contents are that in a non-Gaussian noise environment, the method of the embodiment of the invention is applied to detect the target, and the threshold and the detection probability both adopt 105Obtained by a sub-Monte Carlo experiment, the signal-to-noise ratio being defined as
Sigma denotes the covariance matrix of the noise, αlTo show the eyesAnd the index amplitude, p represents a guide vector of radar receiving data, the index H represents conjugate transposition, and the index-1 represents inversion operation.
Then, looking again at the specific experimental results:
when the radar echo dimension is 8, the number of training samples without targets is 16, the shape parameter is 4, and the proportion parameter is 3, the detection method and the traditional two-step generalized likelihood ratio detection method are applied to detection under the conditions of different signal-to-noise ratios, and the obtained detection probability result comparison graph is shown in fig. 2 and 3, fig. 2 is a detection probability result graph obtained under the conditions of no signal mismatch by the detection method and the traditional two-step generalized likelihood ratio detection method, and fig. 3 is a detection probability result graph obtained under the conditions of different signal-to-noise ratios by the detection method and the traditional two-step generalized likelihood ratio detection method when the mismatching angle cosine square is 0.4547.
As can be seen from fig. 2, when there is no signal mismatch, the method of the present invention has comparable detection performance to the conventional two-step generalized likelihood ratio test method. It can be seen from fig. 3 that when there is a mismatch in the signal, the method of the present invention is more sensitive to the mismatch signal, i.e. the method of the present invention has a greater ability to reject the mismatch signal.
In conclusion, the simulation experiment verifies the correctness, the effectiveness and the reliability of the method.
Fig. 4 is a structural diagram of a radar signal mismatch sensitivity detection system under a non-gaussian background according to an embodiment of the present invention, as shown in fig. 4, including: an acquisition representation module 41, a first calculation module 42, an extraction module 43, a second calculation module 44, a substitution module 45 and a verification module 46; wherein:
the obtaining and representing module 41 is configured to obtain data of a to-be-detected distance unit received by a radar, and represent a detection problem of the to-be-detected distance unit data by using binary hypothesis testing to obtain a first expression without a target signal hypothesis and a second expression with the target signal hypothesis; the first calculating module 42 is configured to calculate, based on the first expression and the second expression, a first probability density function of the distance unit data to be detected under the assumption that the target signal is not included and a second probability density function under the assumption that the target signal is included, respectively; the extracting module 43 is configured to extract texture components of the noise components in the first expression and the second expression, and express the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression; a second calculation module 44 for calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate of a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate of a second magnitude parameter in the second expression; a substitution module 45 is configured to substitute the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter, and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression in a non-uniform environment when the computation covariance matrix is known; the checking module 46 is configured to calculate a maximum likelihood estimate of the covariance matrix using a training sample, substitute the maximum likelihood estimate of the covariance matrix into the check expression to obtain a check result, and compare the check result with a detection threshold to determine whether the target signal exists.
The system provided by the embodiment of the present invention is used for executing the corresponding method, the specific implementation manner of the system is consistent with the implementation manner of the method, and the related algorithm flow is the same as the algorithm flow of the corresponding method, which is not described herein again.
The embodiment of the invention expresses the detection problem by using binary hypothesis test, adds the virtual interference signal under the hypothesis without a target, and then designs the mismatch sensitive detection method under the non-uniform environment by adopting a two-step generalized likelihood ratio test criterion. Compared with the traditional self-adaptive detection method, the mismatch sensitive detection method in the non-uniform environment can effectively improve the identification performance of mismatched signals under the condition of signal mismatch.
According to any of the above embodiments, the obtaining and representing module 41 includes a obtaining sub-module 411 and a representing sub-module 412; wherein:
the obtaining submodule 411 is configured to represent the distance unit data to be detected by using a plurality of complex gaussian vectors, and obtain the training sample; the representing sub-module 412 is configured to obtain the first expression and the second expression by using the binary hypothesis testing algorithm to represent based on the plurality of complex gaussian vectors and the training samples, where noise components in the first expression and the second expression are represented by a composite gaussian model as speckle components and texture components, the first expression includes a virtual interference signal, the second expression includes a preset guide vector, and the virtual interference signal and the guide vector are orthogonal in a real whitening space.
According to any of the above embodiments, the first computation module 42 includes a first computation submodule 421 and a second computation submodule 422; wherein:
the first calculating submodule 421 is configured to calculate the first probability density function of the to-be-detected distance unit data under the assumption that no target signal is contained; the second calculating submodule 422 is configured to calculate the second probability density function of the to-be-detected distance unit data under the assumption that the to-be-detected distance unit data includes a target signal.
Based on any of the above embodiments, the extraction module 43 includes a first extraction submodule 431 and a second extraction submodule 432; wherein:
the first extraction submodule 431 is configured to assume that the texture component is a random variable and obey inverse gamma distribution, and calculate to obtain a texture variable probability density function; the second extraction submodule 432 is configured to obtain the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function.
According to any of the above embodiments, the second calculation module 44 includes a third calculation submodule 441 and a fourth calculation submodule 442; wherein:
the third computation submodule 441 is configured to compute, based on the texture variable probability density function, an integral of the first probability density function with respect to the texture component to obtain a first integral expression, and obtain a first derivative of the first integral expression with respect to a first amplitude parameter in the first expression, and make the first derivative equal to 0 to obtain a maximum likelihood estimate of the first amplitude parameter; the fourth calculating sub-module 442 is configured to calculate, based on the texture variable probability density function, an integral of the second probability density function with respect to the texture component to obtain a second integral expression, solve, for the second integral expression, a second derivative related to a second amplitude parameter in the second expression, and make the second derivative equal to 0 to obtain a maximum likelihood estimate of the second amplitude parameter.
Based on any of the above embodiments, the substitution module 45 is specifically configured to substitute the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter, and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function, and perform reduction under the condition that the virtual interference signal and the preset guide vector are orthogonal in the real whitening space, so as to obtain the check expression.
In any of the above embodiments, the verification module 46 includes a verification sub-module 461 and a comparison sub-module 462; wherein:
the test submodule 461 is configured to calculate a maximum likelihood estimate of the covariance matrix by using a training sample, and substitute the maximum likelihood estimate of the covariance matrix into the test expression to obtain the test result; the comparison sub-module 462 is configured to compare the detection result with the detection threshold, determine that the target signal exists in the distance unit to be detected if the detection result is greater than the detection threshold, and determine that the target signal does not exist in the distance unit to be detected if the detection result is not greater than the detection threshold.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. Processor 510 may call logic instructions in memory 530 to perform the following method: obtaining data of a distance unit to be detected received by a radar, representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis; respectively calculating a first probability density function of the to-be-detected distance unit data under the assumption that the to-be-detected distance unit data does not contain a target signal and a second probability density function under the assumption that the to-be-detected distance unit data contains the target signal based on the first expression and the second expression; extracting texture components of noise components in the first expression and the second expression, and expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression; calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate for a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate for a second magnitude parameter in the second expression; substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression under a non-uniform environment when the calculation covariance matrix is known; and calculating the maximum likelihood estimation of the covariance matrix by using a training sample, substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists.
Furthermore, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: obtaining data of a distance unit to be detected received by a radar, representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis; respectively calculating a first probability density function of the to-be-detected distance unit data under the assumption that the to-be-detected distance unit data does not contain a target signal and a second probability density function under the assumption that the to-be-detected distance unit data contains the target signal based on the first expression and the second expression; extracting texture components of noise components in the first expression and the second expression, and expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression; calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate for a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate for a second magnitude parameter in the second expression; substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression under a non-uniform environment when the calculation covariance matrix is known; and calculating the maximum likelihood estimation of the covariance matrix by using a training sample, substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A radar signal mismatch sensitivity detection method under a non-Gaussian background is characterized by comprising the following steps:
obtaining data of a distance unit to be detected received by a radar, representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis;
respectively calculating a first probability density function of the to-be-detected distance unit data under the assumption that the to-be-detected distance unit data does not contain a target signal and a second probability density function under the assumption that the to-be-detected distance unit data contains the target signal based on the first expression and the second expression;
extracting texture components of noise components in the first expression and the second expression, and expressing the texture components by inverse gamma distribution to obtain a two-step generalized likelihood ratio criterion expression;
calculating an integral of the first probability density function over the texture component and a maximum likelihood estimate for a first magnitude parameter in the first expression, calculating an integral of the second probability density function over the texture component and a maximum likelihood estimate for a second magnitude parameter in the second expression;
substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression under a non-uniform environment when the calculation covariance matrix is known;
and calculating the maximum likelihood estimation of the covariance matrix by using a training sample, substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to judge whether the target signal exists.
2. The method according to claim 1, wherein the obtaining of the data of the range unit to be detected received by the radar, the representing the detection problem of the data of the range unit to be detected by using binary hypothesis testing, and obtaining a first expression without a target signal hypothesis and a second expression with the target signal hypothesis specifically comprises:
expressing the distance unit data to be detected by using a plurality of complex Gaussian vectors, and acquiring the training sample;
and obtaining the first expression and the second expression by adopting the binary hypothesis test representation based on the complex Gaussian vectors and the training samples, wherein noise components in the first expression and the second expression are represented as speckle components and texture components by a composite Gaussian model, the first expression comprises a virtual interference signal, the second expression comprises a preset guide vector, and the virtual interference signal and the guide vector are orthogonal in a real whitening space.
3. The method according to claim 1 or 2, wherein the calculating a first probability density function of the range bin data to be detected under the assumption that no target signal is contained and a second probability density function under the assumption that the target signal is contained based on the first expression and the second expression respectively includes:
under the assumption that no target signal is contained, calculating to obtain the first probability density function of the distance unit data to be detected;
and under the assumption of containing a target signal, calculating to obtain the second probability density function of the distance unit data to be detected.
4. The method according to claim 1, wherein the extracting texture components of noise components in the first expression and the second expression, and representing the texture components by inverse gamma distribution, obtain a two-step generalized likelihood ratio criterion expression specifically includes:
supposing that the texture component is a random variable and obeys inverse gamma distribution, and calculating to obtain a texture variable probability density function;
and obtaining the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function.
5. The method of claim 1, wherein the calculating the integral of the first probability density function to the texture component and the maximum likelihood estimate of the first amplitude parameter in the first expression, the calculating the integral of the second probability density function to the texture component and the maximum likelihood estimate of the second amplitude parameter in the second expression specifically comprises:
calculating to obtain an integral of the texture component by the first probability density function based on the texture variable probability density function to obtain a first integral expression, and calculating a first derivative of a first amplitude parameter in the first expression from the first integral expression, and making the first derivative equal to 0 to obtain a maximum likelihood estimation of the first amplitude parameter;
and calculating to obtain the integral of the second probability density function to the texture component based on the texture variable probability density function to obtain a second integral expression, solving a second derivative related to a second amplitude parameter in the second expression from the second integral expression, and enabling the second derivative to be equal to 0 to obtain the maximum likelihood estimation of the second amplitude parameter.
6. The method according to claim 1, wherein the step of substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain the checking expression in the non-uniform environment when the calculated covariance matrix is known includes:
and substituting the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression based on the texture variable probability density function, and simplifying under the condition that a real whitening space is orthogonal by using a virtual interference signal and a preset guide vector to obtain the test expression.
7. The method of claim 1, wherein the calculating a maximum likelihood estimate of the covariance matrix using a training sample, substituting the maximum likelihood estimate of the covariance matrix into the test expression to obtain a test result, and comparing the test result with a detection threshold to determine whether the target signal exists specifically comprises:
calculating the maximum likelihood estimation of the covariance matrix by using a training sample, and substituting the maximum likelihood estimation of the covariance matrix into the test expression to obtain the test result;
and comparing the detection result with the detection threshold, if the detection result is greater than the detection threshold, judging that the target signal exists in the distance unit to be detected, and otherwise, judging that the target signal does not exist in the distance unit to be detected.
8. A radar signal mismatch sensitive detection system in a non-gaussian background, comprising:
the acquisition and representation module is used for acquiring the data of the distance unit to be detected received by the radar, and representing the detection problem of the data of the distance unit to be detected by adopting binary hypothesis test to obtain a first expression without a target signal hypothesis and a second expression with the target signal hypothesis;
a first calculating module, configured to calculate, based on the first expression and the second expression, a first probability density function of the distance unit data to be detected under an assumption that the distance unit data does not contain a target signal and a second probability density function under an assumption that the distance unit data to be detected contains the target signal, respectively;
the extraction module is used for extracting texture components of the noise components in the first expression and the second expression, expressing the texture components by inverse gamma distribution and obtaining a two-step generalized likelihood ratio criterion expression;
a second computation module for computing an integral of the first probability density function over the texture component and a maximum likelihood estimate of a first magnitude parameter in the first expression, computing an integral of the second probability density function over the texture component and a maximum likelihood estimate of a second magnitude parameter in the second expression;
a substitution module, configured to substitute the first probability function, the second probability density function, the maximum likelihood estimation of the first amplitude parameter, and the maximum likelihood estimation of the second amplitude parameter into the two-step generalized likelihood ratio criterion expression to obtain a check expression in a non-uniform environment when a computation covariance matrix is known;
and the checking module is used for calculating the maximum likelihood estimation of the covariance matrix by utilizing a training sample, substituting the maximum likelihood estimation of the covariance matrix into the checking expression to obtain a checking result, and comparing the checking result with a detection threshold to judge whether the target signal exists or not.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for radar signal mismatch sensitive detection in a non-gaussian background as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, performs the steps of the method for radar signal mismatch sensitivity detection in a non-gaussian background as recited in any one of claims 1 to 7.
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