CN113933808A - Airborne radar moving target detection method, device, equipment and storage medium - Google Patents

Airborne radar moving target detection method, device, equipment and storage medium Download PDF

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CN113933808A
CN113933808A CN202111149989.9A CN202111149989A CN113933808A CN 113933808 A CN113933808 A CN 113933808A CN 202111149989 A CN202111149989 A CN 202111149989A CN 113933808 A CN113933808 A CN 113933808A
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clutter
condition
radar
target
covariance matrix
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李锐洋
杨政
旷生玉
梁璟
黄明军
李复名
徐才进
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CETC 29 Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/2813—Means providing a modification of the radiation pattern for cancelling noise, clutter or interfering signals, e.g. side lobe suppression, side lobe blanking, null-steering arrays

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Abstract

The invention discloses a method, a device, equipment and a storage medium for detecting a moving target of an airborne radar, wherein the method comprises the steps of receiving an echo signal of a single snapshot distance unit, acquiring a covariance matrix of the echo signal of the single snapshot distance unit, acquiring a radar emission signal parameter and acquiring a clutter base matrix according to the radar emission signal parameter; calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; calculating to obtain a generalized likelihood ratio detector; moving objects are detected by a generalized likelihood ratio detector. The invention relates to a radar target detection method based on generalized likelihood ratio detection, which is characterized in that clutter components in single snapshot echo signals are approximately expressed in a subspace vector weighted sum mode, clutter, noise and target amplitude are estimated by adopting an improved least square and maximum likelihood estimation method, and finally a detector suitable for detecting a moving target in a non-uniform environment by an airborne radar is obtained.

Description

Airborne radar moving target detection method, device, equipment and storage medium
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a method, a device, equipment and a storage medium for detecting a moving target of an airborne radar.
Background
The airborne radar looks down to the severe ground/sea clutter environment faced during detection, and no matter whether the clutter suppression is carried out by adopting the STAP technology or the target detection is directly carried out by adopting the GLRT detector, the covariance matrix of the current detection unit needs to be obtained. In the traditional method, a covariance matrix is estimated through independent and identically distributed samples of distance units around a unit to be detected, but the covariance matrix is limited by the number of samples and a non-uniform environment and is difficult to accurately obtain. In order to solve the estimation problem of the covariance matrix under a small number of distance samples, knowledge-based STAP methods such as maximum likelihood estimation, colored loading and the like, which utilize prior knowledge to perform covariance matrix auxiliary estimation, have appeared in recent years. In addition, a covariance matrix recovery calculation method based on characteristics such as array manifold information, clutter space-time power spectrum characteristics or covariance matrix subspace structures and the like is developed by utilizing the space-time two-dimensional coupling of airborne radar clutter in a small sample environment.
The final purpose of estimating the covariance matrix is target detection, and the traditional detection method is to regard clutter and noise as a whole, assume that the clutter and the noise meet specific statistical distribution, and further derive a series of detectors suitable for various clutter statistical characteristics based on binary hypothesis testing. However, the method does not consider clutter space-time coupling characteristics caused by an airborne motion platform, and a good detection effect is difficult to obtain under the condition of a single sample.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method, a device, equipment and a storage medium for detecting a moving target of an airborne radar.
The purpose of the invention is realized by the following technical scheme:
a method for detecting a moving target of an airborne radar is used for the airborne radar, the airborne radar comprises M equally-spaced antenna array elements and transmits N coherent pulses in a coherent processing interval, and the method comprises the following steps:
receiving an echo signal of a single snapshot distance unit, acquiring a covariance matrix of the echo signal of the single snapshot distance unit, acquiring a radar transmission signal parameter and acquiring a clutter basis matrix according to the radar transmission signal parameter;
calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; the first condition is that x is equal to c + n, the second condition is that x is equal to μ s + c + n, x is a space-time snapshot of a single distance unit, s represents a target signal vector, μ is a target amplitude, c is a clutter signal vector, and n is a noise vector;
calculating to obtain a generalized likelihood ratio detector according to the Gaussian white noise under the first condition, the Gaussian white noise under the second condition, the covariance matrix and the clutter base matrix;
detecting moving objects by said generalized likelihood ratio detector.
Further, the method for acquiring the echo signal of the single snapshot distance unit comprises the following steps:
receiving echoes scattered by a target;
performing down-conversion, AD sampling, pulse compression and coherent accumulation on echoes scattered by a target;
defining the data received by the mth array element and the nth pulse as xmnAnd arranging the received data into vectors to obtain the space-time snapshot x of a single distance unit.
Further, the generalized likelihood ratio detector obtained by calculating according to the white gaussian noise under the first condition, the white gaussian noise under the second condition, the covariance matrix and the clutter basis matrix specifically comprises the following steps:
defining the data received by the mth array element and the nth pulse as xmnSpace-time snapshots of a single range unit
Figure BDA0003286897480000031
Figure BDA0003286897480000032
A complex set representing dimensions MN x 1;
according to an integration method, clutter c of the airborne radar is divided into N on each distance unit according to equal anglescA clutter slice, the clutter echo may be represented as a sum of space-time responses of a plurality of scatterers:
Figure BDA0003286897480000033
wherein muiFor corresponding clutter block amplitude, fd，iAnd fs，iRespectively representing the Doppler frequency and the spatial frequency, a, corresponding to the ith clutter slicedRepresenting time-domain steering vectors of radar, asRepresenting a spatial steering vector of the radar;
defining an array manifold matrix
Figure BDA0003286897480000034
Magnitude vector
Figure BDA0003286897480000035
The clutter echo c is equal to A mu; wherein, A is obtained by the array manifold and the flight information of the aircraft,
Figure BDA0003286897480000036
a complex set representing dimensions MN × N;
let n be the variance of
Figure BDA0003286897480000037
White Gaussian noise, the echo data is in HiThe probability density function under the conditions is:
Figure BDA0003286897480000038
wherein, i is 0 or 1,
Figure BDA0003286897480000041
H0under the first condition, H1Is a second condition;
performing outer product on c, and expressing the covariance matrix as 2Nr-1 clutter base weighted sum,
Figure BDA0003286897480000042
wherein N isr＝int[M+β(N-1)]，NrIs the rank of the covariance matrix,
Figure BDA0003286897480000043
Figure BDA0003286897480000044
Figure BDA0003286897480000045
distributed discretely in [ -0.5,0.5 [)]Inner, Λ ═ diag (σ)c⊙σ′c)，Aμ≈Vσc，σcRepresents 2Nr-the amplitude of 1 clutter base;
according to
Figure BDA0003286897480000046
Calculating sigmacAnd is based on
Figure BDA0003286897480000047
And
Figure BDA0003286897480000048
to obtain sigmacLeast squares estimation of
Figure BDA0003286897480000049
Wherein the content of the first and second substances,
Figure BDA00032868974800000414
it is shown that the pseudo-inverse operation,
Figure BDA00032868974800000410
at H1Conditional on taking logarithmic derivative of f (x) and relating to
Figure BDA00032868974800000411
The maximization is obtained, and the maximum likelihood estimation of the noise power under the conditions of A, mu and mu is obtained
Figure BDA00032868974800000412
Will be provided with
Figure BDA00032868974800000413
Bringing in
Figure BDA0003286897480000051
Maximizing the likelihood function with respect to the residual clutter parameters is obtained
Figure BDA0003286897480000052
Wherein y is x- μ s;
maximum likelihood estimation for mu
Figure BDA0003286897480000053
Will be provided with
Figure BDA0003286897480000054
And
Figure BDA0003286897480000055
substitution into
Figure BDA0003286897480000056
To obtain
Figure BDA0003286897480000057
At H0Under the same way to obtain
Figure BDA0003286897480000058
According to
Figure BDA0003286897480000059
Substitution into
Figure BDA00032868974800000510
And
Figure BDA00032868974800000511
generalized likelihood ratio detector
Figure BDA00032868974800000512
In another aspect, the present application provides an airborne radar moving-target detection device, the device includes:
the signal acquisition module is used for receiving the echo signals of the single snapshot distance unit, acquiring a covariance matrix of the echo signals of the single snapshot distance unit, acquiring radar emission signal parameters and acquiring a clutter base matrix according to the radar emission signal parameters;
the noise calculation module is used for calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; the first condition is that x is equal to c + n, the second condition is that x is equal to μ s + c + n, x is a space-time snapshot of a single distance unit, s represents a target signal vector, μ is a target amplitude, c is a clutter signal vector, and n is a noise vector;
the detector construction module is used for calculating to obtain a generalized likelihood ratio detector according to the Gaussian white noise under the first condition, the Gaussian white noise under the second condition, the covariance matrix and the clutter basis matrix;
and the target detection module is used for detecting the moving target through the generalized likelihood ratio detector.
In another aspect, the present application provides a computer device comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded by the processor and executed to implement any one of the above-mentioned onboard radar moving-target detection methods.
In another aspect, the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is loaded and executed by a processor to implement any one of the above-mentioned onboard radar moving-target detection methods.
The invention has the beneficial effects that:
the invention adopts the improved least square and maximum likelihood estimation method to estimate the clutter, noise and target amplitude by approximately representing the clutter component in the single snapshot echo signal into the form of subspace vector weighted sum, and finally obtains the detector suitable for detecting the moving target in the non-uniform environment by the airborne radar. And then, the moving target is detected according to the detector, and higher estimation precision than that of the traditional detector can be obtained when the clutter is stronger. Meanwhile, the moving target detector constructed by the method utilizes the structural characteristics of the clutter covariance matrix to estimate the relevant parameters steadily, the detection performance of not less than 16 training samples is still kept when no training sample exists, the method has the performance equivalent to that of the method adopting the training samples under the same false alarm rate, and even the performance is better when the CNR is larger.
Drawings
Fig. 1 is a schematic flow chart of a moving target detection method of an airborne radar provided in embodiment 1 of the present invention;
FIG. 2 is a graph comparing performance of the airborne radar moving target detection method provided in embodiment 1 of the present invention;
fig. 3 is an ROC curve of the airborne radar moving target detection method and other detection algorithms provided in embodiment 1 of the present invention when the noise-to-noise ratio is 0 db;
fig. 4 is an ROC curve of the airborne radar moving target detection method and other detection algorithms provided in embodiment 1 of the present invention when the noise-to-noise ratio is 15 db;
fig. 5 is a block diagram of a moving target detection device of an airborne radar provided in embodiment 2 of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
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.
Example 1
The final purpose of estimating the covariance matrix is target detection, and the traditional detection method is to regard clutter and noise as a whole, assume that the clutter and the noise meet specific statistical distribution, and further derive a series of detectors suitable for various clutter statistical characteristics based on binary hypothesis testing. However, the method does not consider clutter space-time coupling characteristics caused by an airborne motion platform, and a good detection effect is difficult to obtain under the condition of a single sample. The embodiment provides a method for detecting a moving target of an airborne radar, which aims to overcome the defects, clutter components in single snapshot echo signals are approximately expressed in a subspace vector weighted sum mode, clutter, noise and target amplitude are estimated by adopting an improved least square and maximum likelihood estimation method, and finally a detector suitable for detecting the moving target of the airborne radar in a non-uniform environment is obtained. And then, the moving target is detected according to the detector, and higher estimation precision than that of the traditional detector can be obtained when the clutter is stronger.
The generalized likelihood ratio detector for airborne radar is derived as follows:
the airborne radar comprises M equally spaced antenna array elements and a coherent partN coherent pulses are transmitted in a physical interval, and data received by an m array element and an N pulse are defined as x after echo scattered by a target is subjected to down-conversion, AD sampling, pulse compression and coherent accumulationmnSpace-time snapshots arranged as data vectors to obtain a single distance unit
Figure BDA0003286897480000081
Figure BDA0003286897480000082
A complex set representing dimensions MN x 1. The moving object detection problem can then be described as a binary hypothesis testing problem as follows
Figure BDA0003286897480000083
Where s represents a target signal vector, μ is the target amplitude, c is the clutter signal vector, and n is the noise vector.
In the airborne radar clutter model, clutter c of the airborne radar is divided into N on each range unit according to an equiangular transformation methodcA clutter plate, the clutter echo can be expressed as the sum of the space-time responses of multiple scatterers
Figure BDA0003286897480000084
Wherein muiFor corresponding clutter block amplitude, fd，iAnd fs，iRespectively representing the Doppler frequency and the spatial frequency, a, corresponding to the ith clutter slicedRepresenting time-domain steering vectors of radar, asRepresenting the spatial steering vector of the radar. Defining an array manifold matrix
Figure BDA0003286897480000091
Figure BDA0003286897480000092
Complex sets, magnitude vectors, representing dimensions MN x N
Figure BDA0003286897480000093
The clutter echo can be reduced to
c＝Aμ (3)
Let n be the variance of
Figure BDA0003286897480000094
White Gaussian noise, the echo data is in HiUnder the assumption that the probability density function is
Figure BDA0003286897480000095
The array manifold matrix A can be obtained through the array manifold and the flight information of the aircraft, and is substituted into the residual unknown variables, and the generalized likelihood ratio test is a formula (5). It can be seen that to implement the detector, the detection needs to be performed for mu, and,
Figure BDA0003286897480000096
The parameter estimation is performed, and the detailed steps are described below.
Figure BDA0003286897480000097
First, subspace-based covariance matrix estimation. For the airborne positive side-view uniform linear array radar, the rank of the covariance matrix is known from the Brennan criterion
Nr＝int[M+β(N-1)] (6)
Where int (·) denotes rounding up, the clutter energy is concentrated on the ridge with slope β. Clutter covariance matrix front N at this timerThe eigenvectors corresponding to the large eigenvalues form a clutter subspace, and the covariance matrix can be expressed as 2N by utilizing the low-rank characteristic and the space-time redundancy of the clutterr-1 clutter base weighted sum, in particular:
Figure BDA0003286897480000098
wherein
Figure BDA0003286897480000099
Figure BDA00032868974800000910
Distributed discretely in [ -0.5,0.5 [)]Inner, Λ ═ diag (σ)c⊙σ′c) Then A μ ≈ V σc. Defining the covariance matrix of the unit to be detected as Rcut＝E[xxH]，σcCan solve the problem by solving the problem
Figure BDA0003286897480000101
Obtaining, equivalently, an unconstrained generalized least squares problem
Figure BDA0003286897480000102
Wherein
Figure BDA0003286897480000103
Definition of
Figure BDA00032868974800001011
Representing a pseudo-inverse operation, thencIs estimated as
Figure BDA0003286897480000104
Secondly, is to
Figure BDA0003286897480000105
Maximum likelihood estimation of (1).
At H1Assuming that f (x) is logarithmically derived and related
Figure BDA0003286897480000106
The maximization is obtained, and the maximum likelihood estimation of the noise power under the conditions of A, mu and mu is obtained
Figure BDA0003286897480000107
Obtained
Figure BDA0003286897480000108
Substituting equation (4) to maximize the likelihood function with respect to the remaining clutter parameters
Figure BDA0003286897480000109
Where y is x-mus, the above equation is a non-linear parameter estimation problem and the result of the previous step will be used
Figure BDA00032868974800001010
And substituting for calculation.
Finally, the detector derivation is carried out.
Substituting equation (9) into equation (10), and obtaining the maximum likelihood estimation about mu
Figure BDA0003286897480000111
Brought back to the (10) type to obtain
Figure BDA0003286897480000112
An estimate of (d). Similarly, H0Suppose that the next several amplitude parameters are estimated as
Figure BDA0003286897480000113
Finally, will
Figure BDA0003286897480000114
And
Figure BDA0003286897480000115
substituting the estimated value of (2) into the formula (5) to obtain the generalized formThe likelihood ratio detector is
Figure BDA0003286897480000116
And detecting the moving target according to the obtained generalized likelihood ratio detector, wherein the moving target exists when the detection result is greater than the detector threshold, and the moving target does not exist if the detection result is less than the detector threshold.
In a specific embodiment, a numerical simulation test is performed on an algorithm by using Matlab, referring to fig. 1, as shown in fig. 1, a flow diagram of the airborne radar moving-target detection method provided in this embodiment is shown.
The simulation assumes that the carrier flies at 150M/s in the horizontal direction, and comprises 4 antenna elements, a single CPI transmits 4 coherent pulses with N, the pulse repetition frequency is 2400Hz, the clutter rank is calculated to be 7, the target is located in the direction of 30 ° from the front side and has a relative speed of 90M/s with respect to the carrier, the noise-to-noise ratio (CNR) is set to 10dB, and the output signal-to-noise ratio (SCNR) is defined as
SCNR＝|μ|2sHR-1s (15)
The performance of the NMF detector based on different numbers of sample covariance matrices and the performance of the detection method based on the unit sample to be detected in the text are verified in a simulation mode on the premise that the target speed is known. The black line in the figure represents the detection performance of the known covariance matrix, and L represents the number of training samples. Referring to fig. 2, it can be seen from fig. 2 that as the number of samples increases, the performance of the detector estimated with SCM improves, and the required SCNR for the same detection probability of L32 is about 5dB less than that of L16, similar to the case of detecting a stationary target; the method utilizes the structural characteristics of the clutter covariance matrix to estimate the relevant parameters steadily, still keeps the detection performance which is not weaker than 16 training samples when no training sample exists, and has better performance when the SCNR is smaller.
Referring to fig. 3, as shown in fig. 3, it is shown that ROC curves of several detection algorithms when noise-to-noise ratios (CNRs) are 0dB and 15dB, respectively, it can be seen that the performance of the method is equivalent to that of the method using training samples under the same false alarm rate, and even the performance is better when the CNR is larger, because the covariance matrix estimation mainly ignores noise according to the space-time redundancy of the noise, so that higher estimation accuracy can be obtained when the noise is stronger.
According to the method, under the condition that the airborne radar antenna and the pulse are uniformly arranged, the better moving target detection performance can be realized only by depending on the echo signal of the single-snapshot distance unit.
In the embodiment, clutter components in the single snapshot echo signals are approximately expressed in a subspace vector weighted sum form, clutter, noise and target amplitude are estimated by adopting an improved least square and maximum likelihood estimation method, and finally, the detector suitable for detecting the moving target in the non-uniform environment by the airborne radar is obtained. And then, the moving target is detected according to the detector, and higher estimation precision than that of the traditional detector can be obtained when the clutter is stronger. Meanwhile, the moving target detector constructed by the method utilizes the structural characteristics of the clutter covariance matrix to estimate the relevant parameters steadily, the detection performance of not less than 16 training samples is still kept when no training sample exists, the method has the performance equivalent to that of the method adopting the training samples under the same false alarm rate, and even the performance is better when the noise-to-noise ratio (CNR) is larger.
Example 2
Referring to fig. 5, as shown in fig. 5, a structural block diagram of the airborne radar moving-target detection apparatus provided in this embodiment is shown.
The device specifically includes:
the signal acquisition module is used for receiving the echo signals of the single snapshot distance unit, acquiring a covariance matrix of the echo signals of the single snapshot distance unit, acquiring radar emission signal parameters and acquiring a clutter base matrix according to the radar emission signal parameters;
the noise calculation module is used for calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; the first condition is that x is equal to c + n, the second condition is that x is equal to μ s + c + n, x is a space-time snapshot of a single distance unit, s represents a target signal vector, μ is a target amplitude, c is a clutter signal vector, and n is a noise vector;
the detector construction module is used for calculating to obtain a generalized likelihood ratio detector according to the Gaussian white noise under the first condition, the Gaussian white noise under the second condition, the covariance matrix and the clutter basis matrix;
and the target detection module is used for detecting the moving target through the generalized likelihood ratio detector.
The airborne radar moving target detection device provided by the embodiment adopts the mode that clutter components in single snapshot echo signals are approximately expressed into subspace vector weighted sum, and then adopts improved least square and maximum likelihood estimation methods to estimate clutter, noise and target amplitude, so as to finally obtain the detector suitable for detecting the moving target of the airborne radar in the non-uniform environment. And then, the moving target is detected according to the detector, and higher estimation precision than that of the traditional detector can be obtained when the clutter is stronger.
Example 3
The preferred embodiment provides a computer device, which can implement the steps in any embodiment of the airborne radar moving target detection method provided in the embodiment of the present application, and therefore, the beneficial effects of the airborne radar moving target detection method provided in the embodiment of the present application can be achieved, for details, see the foregoing embodiment, and are not described herein again.
Example 4
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor. To this end, the present invention provides a storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps of any one of the airborne radar moving-target detection methods provided by the embodiments of the present invention.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Because the instructions stored in the storage medium may execute the steps in any airborne radar moving-target detection method provided in the embodiment of the present invention, the beneficial effects that can be achieved by any airborne radar moving-target detection method provided in the embodiment of the present invention may be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. A method for detecting a moving target of an airborne radar is used for the airborne radar, the airborne radar comprises M equally spaced antenna array elements and transmits N coherent pulses in a coherent processing interval, and the method is characterized by comprising the following steps:
receiving an echo signal of a single snapshot distance unit, acquiring a covariance matrix of the echo signal of the single snapshot distance unit, acquiring a radar transmission signal parameter and acquiring a clutter basis matrix according to the radar transmission signal parameter;
calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; the first condition is that x is equal to c + n, the second condition is that x is equal to μ s + c + n, x is a space-time snapshot of a single distance unit, s represents a target signal vector, μ is a target amplitude, c is a clutter signal vector, and n is a noise vector;
calculating to obtain a generalized likelihood ratio detector according to the Gaussian white noise under the first condition, the Gaussian white noise under the second condition, the covariance matrix and the clutter base matrix;
detecting moving objects by said generalized likelihood ratio detector.
2. The method for detecting the moving target of the airborne radar according to claim 1, wherein the method for acquiring the echo signal of the single snapshot distance unit comprises the following steps:
receiving echoes scattered by a target;
performing down-conversion, AD sampling, pulse compression and coherent accumulation on echoes scattered by a target;
defining the data received by the mth array element and the nth pulse as xmnAnd arranging the received data into vectors to obtain the space-time snapshot x of a single distance unit.
3. The method for detecting moving targets of airborne radar according to claim 1, wherein the step of calculating the generalized likelihood ratio detector according to the white gaussian noise under the first condition, the white gaussian noise under the second condition, the covariance matrix and the clutter basis matrix comprises the following steps:
defining the data received by the mth array element and the nth pulse as xmnSpace-time snapshots of a single range unit
Figure FDA0003286897470000021
Figure FDA0003286897470000022
A complex set representing dimensions MN x 1;
according to an integration method, clutter c of the airborne radar is divided into N on each distance unit according to equal anglescA clutter slice, the clutter echo may be represented as a sum of space-time responses of a plurality of scatterers:
Figure FDA0003286897470000023
wherein muiFor corresponding clutter block amplitude, fd,iAnd fs,iRespectively representing the Doppler frequency and the spatial frequency, a, corresponding to the ith clutter slicedRepresenting time-domain steering vectors of radar, asRepresenting a spatial steering vector of the radar;
defining an array manifold matrix
Figure FDA0003286897470000024
Magnitude vector
Figure FDA0003286897470000025
The clutter echo c is equal to A mu; wherein, A is obtained by the array manifold and the flight information of the aircraft,
Figure FDA0003286897470000026
a complex set representing dimensions MN × N;
let n be the variance of
Figure FDA0003286897470000027
White Gaussian noise, the echo data is in HiThe probability density function under the conditions is:
Figure FDA0003286897470000028
wherein, i is 0 or 1,
Figure FDA0003286897470000029
H0under the first condition, H1Is a second condition;
performing outer product on c, and expressing the covariance matrix as 2Nr-1 clutter base weighted sum,
Figure FDA0003286897470000031
wherein N isr＝int[M+β(N-1)]，NrIs the rank of the covariance matrix,
Figure FDA0003286897470000032
Figure FDA00032868974700000315
Figure FDA0003286897470000033
distributed discretely in [ -0.5,0.5 [)]Inner, Λ ═ diag (σ)c⊙σ′c)，Aμ≈Vσc，σcRepresents 2Nr-the amplitude of 1 clutter base;
according to
Figure FDA0003286897470000034
Calculating sigmacAnd is based on
Figure FDA0003286897470000035
And
Figure FDA0003286897470000036
to obtain sigmacLeast squares estimation of
Figure FDA0003286897470000037
Wherein the content of the first and second substances,
Figure FDA0003286897470000038
it is shown that the pseudo-inverse operation,
Figure FDA0003286897470000039
at H1Conditional on taking logarithmic derivative of f (x) and relating to
Figure FDA00032868974700000310
The maximization is obtained, and the maximum likelihood estimation of the noise power under the conditions of A, mu and mu is obtained
Figure FDA00032868974700000311
Will be provided with
Figure FDA00032868974700000312
Bringing in
Figure FDA00032868974700000313
Make likelihoodThe clutter parameter maximization of the function with respect to the residual is obtained
Figure FDA00032868974700000314
Wherein y is x- μ s;
maximum likelihood estimation for mu
Figure FDA0003286897470000041
Will be provided with
Figure FDA0003286897470000042
And
Figure FDA0003286897470000043
substitution into
Figure FDA0003286897470000044
To obtain
Figure FDA0003286897470000045
At H0Under the same way to obtain
Figure FDA0003286897470000046
According to
Figure FDA0003286897470000047
Substitution into
Figure FDA0003286897470000048
And
Figure FDA0003286897470000049
generalized likelihood ratio detector
Figure FDA00032868974700000410
4. An airborne radar moving target detection apparatus, the apparatus comprising:
the signal acquisition module is used for receiving the echo signals of the single snapshot distance unit, acquiring a covariance matrix of the echo signals of the single snapshot distance unit, acquiring radar emission signal parameters and acquiring a clutter base matrix according to the radar emission signal parameters;
the noise calculation module is used for calculating Gaussian white noise under a first condition and Gaussian white noise under a second condition according to the covariance matrix and the clutter basis matrix; the first condition is that x is equal to c + n, the second condition is that x is equal to μ s + c + n, x is a space-time snapshot of a single distance unit, s represents a target signal vector, μ is a target amplitude, c is a clutter signal vector, and n is a noise vector;
the detector construction module is used for calculating to obtain a generalized likelihood ratio detector according to the Gaussian white noise under the first condition, the Gaussian white noise under the second condition, the covariance matrix and the clutter basis matrix;
and the target detection module is used for detecting the moving target through the generalized likelihood ratio detector.
5. A computer device, characterized in that it comprises a processor and a memory, in which a computer program is stored, which is loaded and executed by the processor to implement the airborne radar moving-target detection method according to any of claims 1 to 3.
6. A computer-readable storage medium, in which a computer program is stored, which is loaded and executed by a processor to implement the airborne radar moving-target detection method according to any one of claims 1 to 3.
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