CN104535973A - Target detection method by use of airborne early warning radar - Google Patents
Target detection method by use of airborne early warning radar Download PDFInfo
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- 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
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- G—PHYSICS
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- 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
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Abstract
The invention belongs to the technical field of radar clutter inhibition and in particular relates to a target detection method by use of airborne early warning radar. The target detection method comprises the following specific steps: receiving space-time two-dimensional echo data by use of airborne early warning radar; determining normalized space-time two-dimensional frequency points corresponding to the positions of a clutter ridge curve related to the space-time two-dimensional echo data; constructing a clutter ridge steering vector basis matrix Psi according to the normalized space-time two-dimensional frequency points corresponding to the positions of the clutter ridge curve; constructing a matrix element error vector Es and a new clutter ridge steering vector basis matrix Psi', and performing iterative fitting on the data of the first unit to be tested in distance under the least square constraint by use of the new clutter ridge steering vector basis matrix Psi', thereby obtaining the fitted residual data of the first unit to be tested in distance; performing unit average constant false alarm rate detection on the fitted residual data of the first unit to be tested in distance, thereby obtaining a target detection result.
Description
Technical Field
The invention belongs to the technical field of radar clutter suppression, and particularly relates to a target detection method for an airborne early warning radar.
Background
The main task of the airborne early warning radar is to detect a target in a complex clutter background and position and track the target, and the effective suppression of the clutter is a core means for improving the working performance of the early warning radar. Space-time adaptive processing (STAP) technology makes full use of space domain and time domain information, and ground clutter is filtered through space-time adaptive processing while coherent accumulation is carried out on target signals, so that effective detection of an airborne radar on a target is realized. Such as the E2-D airborne early warning radar in the united states. In practical application, the STAP technology mainly has the following two problems: firstly, in a non-uniform clutter environment, it is very difficult to obtain enough Independent and Independent Distributed (IID) training samples for estimating a covariance matrix; secondly, even if the requirements of the training samples are met, the problem of excessive calculation amount of full space-time processing can cause that the real-time performance is difficult to guarantee. To solve the above problems and to promote the practical use of STAP technology, many improvements or methods have been proposed.
The invention patent of invention application of Qinghua university, "space-time adaptive processing method under heterogeneous clutter environment" (patent application No. 201010129723.3, publication No. CN 101819269 a) discloses a super-complete sparse representation method for super-resolution estimation of clutter space-time two-dimensional spectrum in heterogeneous clutter environment. The method realizes the estimation of the clutter covariance matrix by using the single-frame training sample under the condition of insufficient independent and identically distributed samples, thereby avoiding the influence of the strong inhomogeneous clutter environment on the self-adaptive processing effect. However, the main disadvantages of this method are still: the number of overcomplete bases for sparse representation of the clutter spectrum is uncertain, but is far greater than the system degree of freedom, and in practice, the system degree of freedom is usually thousands of degrees, so that the computation amount required in the process of reconstructing the covariance matrix of each distance unit sample is very large, the real-time processing is not facilitated, and the actual engineering application effect of the clutter spectrum is influenced.
The invention patent of Beijing university of Physician university "a dimension reduction space-time adaptive processing method based on covariance matrix weighting" (patent application No. 201210251589.3, publication No. CN 102778669A) discloses a method for estimating clutter covariance matrix by using covariance matrix weighting technique. The method realizes self-adaptive widening of the clutter notch to adapt to the clutter ridge in the actual environment, so that when clutter leakage exists, the clutter can be effectively inhibited through the STAP method, and the robustness of the STAP in the actual application is improved. However, the method still has the following defects: the notch width of the filter designed for suppressing clutter is artificially set, the clutter ridge condition in actual data cannot be sensed in a self-adaptive manner, and when the broadening amount is too large, the minimum detectable speed is increased, which is very unfavorable for the detection of a low-speed small target.
Disclosure of Invention
The invention aims to provide an airborne early warning radar target detection method which can improve clutter suppression performance of adaptive signal processing and improve detection probability of a moving target.
In order to achieve the technical purpose, the invention is realized by adopting the following technical scheme.
The airborne early warning radar target detection method comprises the following steps:
step 1, receiving space-time two-dimensional echo data by using an airborne early warning radar antenna;
step 2, determining normalized space-time two-dimensional frequency points corresponding to clutter ridge curve positions related to space-time two-dimensional echo data;
step 3, constructing a clutter ridge guide vector base matrix psi according to the normalized space-time two-dimensional frequency points corresponding to the clutter ridge curve positions;
step 4, constructing an array element error vector Es，Wherein, the indication indicates a Hadamard product; 1NColumn vector representing N rows, 1NAll the elements in (1); p represents the array element amplitude error vector,representing an array element phase error vector; constructing a new clutter ridge guide vector base matrix psi':
wherein,represents the kronecker product, 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing by vectorsA diagonal matrix formed by the main diagonal elements of (1);
step 5, carrying out iterative fitting on the data of the ith distance unit to be detected under the least square constraint by using a new clutter ridge guide vector basis matrix psi' to obtain the fitted residual data of the ith distance unit to be detected, wherein L is 1,2,. L and is the number of distance units to be detected of the airborne early warning radar;
and 6, carrying out unit average constant false alarm detection on the fitted residual data of the first distance unit to be detected to obtain a target detection result.
The invention is characterized by further improvement:
the step 1 specifically comprises the following substeps:
1a) setting the airborne early warning radar to work in a front side view mode;
1b) utilizing an airborne early warning radar to emit signals outwards; the receiving array of the airborne early warning radar is an even linear array consisting of N array elements, and the receiving array of the airborne early warning radar is used for receiving the space-time two-dimensional echo data of L distance units reflected by the ground within the coherent accumulation time of M pulses.
The step 2 specifically comprises the following substeps:
2a) in the interval [0,1]The uniform discretization is carried out to obtain a plurality of discrete points, and the value of each discrete point is in the interval [0,1 ]]Inner, the number of discrete points is expressed as num, and normalized spatial frequency is setNormalized spatial frequencyThe value of each discrete point after discretization;
2b) according toDetermining corresponding normalized Doppler frequenciesβ＝2vTrWhere v is the speed of the carrier, TrThe pulse repetition period of the signal transmitted by the airborne early warning radar is d, and the antenna array element spacing of the receiving array of the airborne early warning radar is d;
2c) will normalize the Doppler frequencyAnd normalized spatial frequencyDetermined as corresponding to the position of the ridge curve of the clutterThe normalized space-time two-dimensional frequency points.
The step 3 specifically comprises the following substeps:
3a) obtaining corresponding time domain guide vector according to normalized space-time two-dimensional frequency point corresponding to clutter ridge curve positionAnd space domain steering vector
Wherein,representing a time domain steering vector, which is a column vector of M rows;representing a space-domain steering vector, which is a column vector of N rows;in order to normalize the doppler frequency of the doppler,for normalizing spatial frequency, (.)TRepresenting a transpose operation; n represents the array element number of the airborne early warning radar receiving array, and M represents the accumulated pulse number of the airborne early warning radar receiving signals;
3b) according to the coupling relation of the space-time two-dimensional echo data in the space-time two-dimension, the space-time two-dimensional guide vector of the corresponding space-time frequency point is obtained <math>
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</math> Wherein,represents the kronecker product;
3c) space-time two-dimensional guide vector corresponding to num discrete pointsCombined together, the clutter ridge steering vector basis matrix ψ, ψ is a matrix of size MN × num.
The step 5 specifically includes the following substeps:
5a) establishing the following 2-norm optimization model for the fitting coefficient vector:
wherein | · | purple sweet2Representing 2 norm, x of an evaluation vector or matrixlIs the data x of the first distance unit to be detected in the space-time two-dimensional echo datalIs the column vector of the MN row; alpha is alphalFor the fitting coefficient vector, alpha, of the l-th distance unit to be detected to be solved under the given basis matrixlIs a column vector of num rows; 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing a kronecker product operation;
array element error vector Es＝1N，1NColumn vector representing N rows, 1NAll the elements in (1);
5b) will Es＝1NSubstituting the model into the 2 norm optimization model of the fitting coefficient vector to obtain the fitting coefficient vector alpha of the l-th distance unit to be detected under the given base matrixl；
5c) The fitting coefficient vector α resulting from substep 5b)lEstablishing the following optimization model about the array element error vector:
solving the optimization model about the array element error vector to obtain an updated array element error vector Es；
5d) Let the iteration difference <math>
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</math> Let the data norm zlComprises the following steps:
5e) if z islGreater than sigmalAnd islGreater than 0.01 sigmalThen return to substep 5b), σlIs a set noise level; otherwise, jumping to substep 5 f);
5f) will present EsSubstituting the value of the clutter ridge guiding vector matrix phi 'into a calculation formula of a new clutter ridge guiding vector base matrix phi' to obtain a fitted clutter ridge guiding vector base matrix psi 'of a matrix phi'*Psi'; obtain the current EsThe fitting coefficient vector alpha corresponding to the value oflCurrent E is addedsThe fitting coefficient vector alpha corresponding to the value oflBest fitting coefficient of basis matrix recorded as ith distance unit to be detectedObtaining the fitted residual data x of the first distance unit to be detectedl-ψ′*αl *。
The invention has the beneficial effects that:
1) the invention relates to an airborne radar space-time self-adaptive processing method based on a clutter ridge guide vector basis matrix, which is characterized in that space-time guide vectors corresponding to space-time frequency points on a clutter ridge curve position are selected according to a radar array flow pattern and system parameters, data are constructed to be used for representing the clutter ridge guide vector basis matrix, and iterative fitting processing is carried out on radar echo data, so that full fitting and inhibition on strong ground clutter are realized, and the detection capability of a moving target under a complex background is greatly improved.
2) The clutter ridge guide vector basis matrix used in the invention is determined according to the working mode and system parameters of the radar, when the radar works in the positive side array mode, clutter in echo data can be represented by a determined number of guide vectors, and the number is far less than the degree of freedom of the system. The problem dimension of the optimization algorithm is far smaller than that required in the existing sparse recovery method by constructing the basis matrix by using the group of guide vectors, so that the algorithm complexity is greatly reduced, and the real-time processing of data is facilitated.
3) According to the invention, the mode of performing iterative least square estimation on the array element error and the fitting coefficient is used, so that the non-uniform clutter can be well fitted, the non-uniform clutter is fully inhibited, and the moving target detection performance is improved. Meanwhile, after the array element error is considered, the method has good robustness to the array element error.
Drawings
FIG. 1 is a flow chart of a method for detecting an airborne early warning radar target according to the present invention;
FIG. 2 is a diagram illustrating a Pulse Doppler (PD) processing result of original space-time two-dimensional echo data in a simulation experiment;
FIG. 3 is a schematic diagram of the Pulse Doppler (PD) processing results of fitting data obtained by the present invention in a simulation experiment;
FIG. 4a is a diagram showing the comparison between the amplitude error of the array element estimated by the iterative optimization of the present invention and the amplitude error of the real array element in a simulation experiment;
FIG. 4b is a diagram showing the comparison between the phase error of the array element estimated by the iterative optimization of the present invention and the phase error of the real array element in the simulation experiment;
FIG. 5 is a graph of target detection probability curves obtained by the present invention, the GIP-EFA algorithm, and the GIP-JDL algorithm, respectively, in a simulation experiment.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
referring to fig. 1, the invention is a flowchart of a method for detecting an airborne early warning radar target, the method is used for processing an airborne early warning radar signal, and the specific implementation steps are as follows:
step 1, receiving space-time two-dimensional echo data by using an airborne early warning radar antenna.
The step 1 specifically comprises the following substeps:
1a) and setting the airborne early warning radar to work in a front side-looking mode.
1b) Utilizing an airborne early warning radar to emit signals outwards; the receiving array of the airborne early warning radar is an even linear array consisting of N array elements, and the receiving array of the airborne early warning radar is used for receiving the space-time two-dimensional echo data of L distance units reflected by the ground within the coherent accumulation time of M pulses.
And 2, determining normalized space-time two-dimensional frequency points corresponding to clutter ridge curve positions related to space-time two-dimensional echo data.
The step 2 specifically comprises the following substeps:
2a) in the interval [0,1]The uniform discretization is carried out to obtain a plurality of discrete points, and the value of each discrete point is in the interval [0,1 ]]Inner, the number of discrete points is expressed as num, and normalized spatial frequency is setNormalized spatial frequencyThe value of each discrete point after discretization; the number of discrete points num is calculated according to the following formula:
num＝N+β·(M-1)
wherein, N represents the array element number of airborne early warning radar receiving array, and M represents the accumulated pulse number of airborne early warning radar received signal, and beta represents the slope of clutter ridge curve, and the slope beta of clutter ridge curve calculates as follows:
β＝2vTr/d
where v is the speed of the carrier, TrIs the pulse of the signal emitted by the airborne early warning radarAnd d is the antenna array element spacing of the receiving array of the airborne early warning radar.
2b) Normalized Doppler frequencyAnd normalized spatial frequencyIn a front side view uniform line radar, there is a linear relationship of:
determining the corresponding normalized Doppler frequency according to the linear relationship
2c) Will normalize the Doppler frequencyAnd normalized spatial frequencyAnd determining normalized space-time two-dimensional frequency points corresponding to the positions of the clutter ridge curves, wherein the number of discrete points is num, and the number of the normalized space-time two-dimensional frequency points is num.
And 3, constructing a clutter ridge guide vector base matrix psi according to the normalized space-time two-dimensional frequency points corresponding to the clutter ridge curve positions.
The step 3 specifically comprises the following substeps:
3a) according to clutter ridgesThe normalized space-time two-dimensional frequency point corresponding to the curve position obtains the corresponding time domain guide vectorAnd space domain steering vectorThe following formula:
wherein,representing a time domain steering vector, which is a column vector of M rows;representing a space-domain steering vector, which is a column vector of N rows;in order to normalize the doppler frequency of the doppler,for normalizing spatial frequency, (.)TRepresenting a transpose operation; n represents the array element number of the airborne early warning radar receiving array, and M represents the accumulated pulse number of the airborne early warning radar receiving signals.
3b) According to the coupling relation of the space-time two-dimensional echo data in the space-time two-dimension, the space-time two-dimensional guide vector of the corresponding space-time frequency point is obtained
Wherein,representing a kronecker product operation; it can be seen that the space-time two-dimensional steering vectorIs a column vector of MN rows.
3c) Space-time two-dimensional guide vector corresponding to num discrete pointsAnd the clutter ridge guide vector basis matrix psi is formed by combining the clutter ridge guide vector basis matrix psi and the clutter ridge guide vector basis matrix psi.
The clutter ridge guide vector base matrix psi is composed of space-time two-dimensional guide vectors corresponding to num space-time frequency points at clutter ridge curve positions, and the space-time two-dimensional guide vectorsIs MN × 1, it is understood that ψ is a matrix of size MN × num. The clutter ridge guide vector basis matrix can be completely determined by system parameters, a training sample is not needed, and the clutter ridge guide vector basis matrix does not change along with the change of a distance unit to be detected, so that the clutter ridge guide vector basis matrix is convenient for practical application.
And 4, constructing a new base matrix psi' by considering the array element error condition.
The step 4 specifically comprises the following substeps:
4a) obtaining an array element error vector E according to the influence of the error on the array antennas：
Wherein, an indication indicates a Hadamard product operation; 1NColumn vector representing N rows, 1NAll the elements in (1); p represents the array element amplitude error vector, p is the column vector of N rows,the phase error vector of the array element is shown,is a column vector of N rows; rho ═ rho1 ρ2 … ρN]T，ρnRepresenting the amplitude error when the nth array element in the receiving array of the airborne early warning radar receives the signal;and the phase error is shown when the nth array element in the receiving array of the airborne early warning radar receives the signal, and N is 1, 2.
4b) Constructing a new clutter ridge oriented vector basis matrix psi' according to the array element error model:
wherein,denotes the kronecker operation, 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing by vectorsIs a diagonal matrix (vector) composed of principal diagonal elementsAccording to its vectorArranged on the main diagonal of the diagonal matrix).
And 5, carrying out iterative fitting on the data of the ith distance unit to be detected under the least square constraint by using the new clutter ridge guide vector basis matrix psi' to obtain the fitted residual data of the ith distance unit to be detected, wherein L is 1, 2.
In the step 5, iterative fitting is carried out on data of a distance unit to be detected by using a new clutter ridge guide vector basis matrix psi' under the least square constraint, joint estimation is carried out on array element errors and fitting coefficients, and the data of the distance unit to be detected are sequentially taken from the space-time two-dimensional echo data received in the step 1; the fitting formula is as follows,
wherein | · | purple sweet2Representing 2 norm, x of an evaluation vector or matrixlIs the data x of the first distance unit to be detected in the space-time two-dimensional echo datalIs the column vector of the MN row; alpha is alphalFor the fitting coefficient vector, alpha, of the l-th distance unit to be detected to be solved under the given basis matrixlIs a column vector of num rows; y islFor the fitting error norm of the first distance unit to be detected, L represents the serial number of the distance unit to be detected, and L is 1, 2.
The step 5 specifically comprises the following substeps:
5a) establishing the following 2-norm optimization model for the fitting coefficient vector:
wherein | · | purple sweet2Representing 2 norm, x of an evaluation vector or matrixlIs the data x of the first distance unit to be detected in the space-time two-dimensional echo datalIs the column vector of the MN row; alpha is alphalFor the fitting coefficient vector, alpha, of the l-th distance unit to be detected to be solved under the given basis matrixlIs a column vector of num rows; 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing a kronecker product operation.
Initializing parameters to make array element error vector Es＝1N，1NColumn vector representing N rows, 1NAll the elements in (1); let the data norm zl＝||xl||2Let the iteration differencel＝σl，σlIs the set noise level.
5b) Will Es＝1NSubstituting the model into the 2 norm optimization model of the fitting coefficient vector to obtain the fitting coefficient vector alpha of the l-th distance unit to be detected under the given base matrixl。
5c) The fitting coefficient vector α resulting from substep 5b)lEstablishing the following optimization model about the array element error vector:
solving the optimization model about the array element error vector to obtain an updated array element error vector Es。
5d) Let the iteration difference <math>
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</math> Let the data norm zlComprises the following steps:
5e) if z islGreater than sigmalAnd islGreater than 0.01 sigmalThen return to sub-step 5 b); otherwise, go to substep 5 f).
5f) Will present EsSubstituting the value of the clutter ridge guiding vector matrix phi 'into a calculation formula of a new clutter ridge guiding vector base matrix phi' to obtain a fitted clutter ridge guiding vector base matrix psi 'of a matrix phi'*Psi'; obtain the current EsThe fitting coefficient vector alpha corresponding to the value oflCurrent E is addedsThe fitting coefficient vector alpha corresponding to the value oflBest fitting coefficient of basis matrix recorded as ith distance unit to be detected
According to a matrixψ′*And the optimal fitting coefficient of the basic matrix of the l distance unit to be detectedObtaining the fitted residual data of the first distance unit to be detected, wherein the fitted residual data of the first distance unit to be detected is xl-ψ′*αl *；xlThe data of the first distance unit to be detected.
In step 5, the new basis matrix ψ' has a size of MN × num, and there is MN>num, and thus ψ 'is not complete in the complex data vector space of dimension MN × 1, i.e. all column vectors of the new basis matrix ψ' cannot fully represent all complex vectors of size MN × 1. The basis matrix is composed of two parts, one part is a clutter ridge guide vector basis matrix which is composed of space-time guide vectors at clutter ridge curve positions, and the other part is the influence of array element errors introduced in an iteration process. Therefore, the least squares iterative fitting can be used for completely fitting and representing clutter components in the data of the distance unit to be detected, and cannot sufficiently represent a target signal which is not on a clutter ridge. When the target signal exists in the distance unit data to be detected, the error norm ylThere will be a larger output, i.e. a larger fitting error exists. The main clutter component of the residual data has been determined by the basis matrix psi' and the coefficient alphalThe representation is sufficiently fitted so that clutter components are effectively filtered out in the residual data.
And 6, carrying out unit average constant false alarm detection on the fitted residual data of the first distance unit to be detected to obtain a target detection result.
Fitting residual data of the first distance unit to be detectedPerforming Cell-averaging constant false alarm rate (CA-CFAR), i.e. fitting the residual dataComparing the average value of the residual data after fitting with the data of the reference distance units around the ith distance unit to be detected, and obtaining the residual data after fitting according to the ith distance unit to be detectedAnd judging whether a target exists or not according to the ratio of the target to the average value, and finally outputting whether the target exists or not.
The effect of the present invention will be further explained with the simulation experiment.
1) Simulation conditions are as follows:
the simulation experiment of the invention is carried out under MATLAB 7.11 software. In the simulation experiment of the invention, the set airborne early warning radar works in a front side view mode, a receiving array of the airborne early warning radar is a linear array with 10 array elements which are uniformly arranged, and the spacing between the array elements is half wavelength. In the simulation experiment of the invention, the echo data used is generated by simulation according to a clutter model proposed by the forest and coln laboratories j.ward, and the detailed system parameters refer to the following table.
2) Comparison of simulation results
Referring to fig. 2, a schematic diagram of a Pulse Doppler (PD) processing result of original space-time two-dimensional echo data in a simulation experiment is shown. In fig. 2, the abscissa represents the doppler channel, the ordinate represents the range cell (range gate), and different gray levels represent different amplitudes of echo data. As can be seen from fig. 2, in the simulated space-time two-dimensional echo data, the energy distribution of the clutter on the range-doppler diagram is not only related to the antenna pattern, but also modulated by the range change, so that the actual data situation can be reflected more truly.
Referring to FIG. 3, it is a general simulation experimentThe invention is used for obtaining a schematic diagram of the processing result of the Pulse Doppler (PD) of the fitting data. In fig. 3, the abscissa represents the doppler channel, the ordinate represents the range cell (range gate), and different gray levels represent different amplitudes of echo data. Here, the fitting data obtained by the present invention is data ψ 'obtained in step 5'*αl *(fitting data of the l-th distance unit to be detected). As can be seen from fig. 3, the fitting data is very similar to the space-time two-dimensional echo data in fig. 2, and substantially completely reflects the clutter component in the space-time two-dimensional echo data. The invention can well fit the non-uniform echo data in the simulation scene, thereby fully inhibiting the non-uniform echo data.
Referring to fig. 4a, a comparison graph of the array element amplitude error estimated by iterative optimization according to the present invention in a simulation experiment and a real array element amplitude error is shown. In fig. 4a, the horizontal axis represents the serial number of the array element, the vertical axis represents the amplitude error of the array element, the asterisk represents the amplitude error of the real array element, and the circle represents the amplitude error of the array element estimated by the iterative optimization of the invention. Referring to fig. 4b, the graph is a comparison graph of the phase error of the array element estimated by iterative optimization of the invention and the phase error of the real array element in a simulation experiment, in fig. 4b, the horizontal axis represents the serial number of the array element, the vertical axis represents the phase error of the array element, the unit is degree, the asterisk represents the phase error of the real array element, and the circle represents the phase error of the array element estimated by iterative optimization of the invention. It can be seen from fig. 4 that the amplitude and phase of the estimated array element error almost completely coincide with the actual array element error, and the robustness of the invention to the array element error can be verified.
Referring to fig. 5, a graph of target detection probability obtained by the present invention, the GIP-EFA (generated injected-extended influenced approach) algorithm, and the GIP-JDL (generated injected-joint domain localized) algorithm, respectively, in a simulation experiment is shown. In fig. 5, the horizontal axis represents signal-to-noise ratio in dB, and the vertical axis represents target detection probability, and the algorithm herein represents the present invention. As can be seen from FIG. 5, the detection performance of the GIP-EFA algorithm and the GIP-JDL algorithm are consistent, but the target detection probability of the present invention is higher than that of the other two algorithms, for example, when the detection probability is 0.8, the present invention has a performance advantage over 3 dB.
In summary, the conventional space-time adaptive processing method (STAP) is usually performed in a high-dimensional data space, which increases the computational complexity and the number of required training samples, and in order to obtain better performance, the present invention is mainly used for solving the problems of huge space-time processing computation and strict requirement on the number of training samples in the high-dimensional space.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (5)
1. An airborne early warning radar target detection method is characterized by comprising the following steps:
step 1, receiving space-time two-dimensional echo data by using an airborne early warning radar antenna;
step 2, determining normalized space-time two-dimensional frequency points corresponding to clutter ridge curve positions related to space-time two-dimensional echo data;
step 3, constructing a clutter ridge guide vector base matrix psi according to the normalized space-time two-dimensional frequency points corresponding to the clutter ridge curve positions;
step 4, constructing array element errorsDifference vector Es，Wherein, the indication indicates a Hadamard product; 1NColumn vector representing N rows, 1NAll the elements in (1); p represents the array element amplitude error vector,representing an array element phase error vector; constructing a new clutter ridge guide vector base matrix psi':
wherein,represents the kronecker product, 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing by vectorsA diagonal matrix formed by the main diagonal elements of (1);
step 5, carrying out iterative fitting on the data of the ith distance unit to be detected under the least square constraint by using a new clutter ridge guide vector basis matrix psi' to obtain the fitted residual data of the ith distance unit to be detected, wherein L is 1,2,. L and is the number of distance units to be detected of the airborne early warning radar;
and 6, carrying out unit average constant false alarm detection on the fitted residual data of the first distance unit to be detected to obtain a target detection result.
2. The method for detecting the target of the airborne early warning radar according to claim 1, wherein the step 1 specifically comprises the following substeps:
1a) setting the airborne early warning radar to work in a front side view mode;
1b) utilizing an airborne early warning radar to emit signals outwards; the receiving array of the airborne early warning radar is an even linear array consisting of N array elements, and the receiving array of the airborne early warning radar is used for receiving the space-time two-dimensional echo data of L distance units reflected by the ground within the coherent accumulation time of M pulses.
3. The method for detecting the target of the airborne early warning radar according to claim 1, wherein the step 2 specifically comprises the following substeps:
2a) in the interval [0,1]The uniform discretization is carried out to obtain a plurality of discrete points, and the value of each discrete point is in the interval [0,1 ]]Inner, the number of discrete points is expressed as num, and normalized spatial frequency is setNormalized spatial frequencyThe value of each discrete point after discretization;
2b) according toDetermining corresponding normalized Doppler frequenciesβ＝2vTrWhere v is the speed of the carrier, TrThe pulse repetition period of the signal transmitted by the airborne early warning radar is d, and the antenna array element spacing of the receiving array of the airborne early warning radar is d;
2c) will normalize the Doppler frequencyAnd normalized spatial frequencyAnd determining normalized space-time two-dimensional frequency points corresponding to the positions of the clutter ridge curves.
4. The method for detecting the target of the airborne early warning radar according to claim 1, wherein the step 3 specifically comprises the following substeps:
3a) obtaining corresponding time domain guide vector according to normalized space-time two-dimensional frequency point corresponding to clutter ridge curve positionAnd space domain steering vector
Wherein,representing a time domain steering vector, which is a column vector of M rows;representing spatial domain pilotA vector, which is a column vector of N rows;in order to normalize the doppler frequency of the doppler,for normalizing spatial frequency, (.)TRepresenting a transpose operation; n represents the array element number of the airborne early warning radar receiving array, and M represents the accumulated pulse number of the airborne early warning radar receiving signals;
3b) according to the coupling relation of the space-time two-dimensional echo data in the space-time two-dimension, the space-time two-dimensional guide vector of the corresponding space-time frequency point is obtained Wherein,represents the kronecker product;
3c) space-time two-dimensional guide vector corresponding to num discrete pointsCombined together, the clutter ridge steering vector basis matrix ψ, ψ is a matrix of size MN × num.
5. The method for detecting the target of the airborne early warning radar according to claim 1, wherein the step 5 specifically comprises the following substeps:
5a) establishing the following 2-norm optimization model for the fitting coefficient vector:
wherein | · | purple sweet2Representing 2 norm, x of an evaluation vector or matrixlIs the data x of the first distance unit to be detected in the space-time two-dimensional echo datalIs the column vector of the MN row; alpha is alphalFor the fitting coefficient vector, alpha, of the l-th distance unit to be detected to be solved under the given basis matrixlIs a column vector of num rows; 1MColumn vector representing M rows, 1MThe elements in the (A) are all 1,representing a kronecker product operation;
array element error vector Es＝1N，1NColumn vector representing N rows, 1NAll the elements in (1);
5b) will Es＝1NSubstituting the model into the 2 norm optimization model of the fitting coefficient vector to obtain the fitting coefficient vector alpha of the l-th distance unit to be detected under the given base matrixl；
5c) The fitting coefficient vector α resulting from substep 5b)lEstablishing the following optimization model about the array element error vector:
solving the optimization model about the array element error vector to obtain an updated array element error vector Es；
5d) Let the iteration difference <math>
<mrow>
<msub>
<mi>δ</mi>
<mi>l</mi>
</msub>
<mo>=</mo>
<msub>
<mi>z</mi>
<mi>l</mi>
</msub>
<mo>-</mo>
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<mrow>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>x</mi>
<mi>l</mi>
</msub>
<mo>-</mo>
<mi>diag</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mi>s</mi>
</msub>
<mo>⊗</mo>
<msub>
<mn>1</mn>
<mi>M</mi>
</msub>
<mo>)</mo>
</mrow>
<mi>ψ</mi>
<msub>
<mi>a</mi>
<mi>l</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
</mrow>
<mn>2</mn>
</msub>
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</mrow>
</math> Let the data norm zlComprises the following steps:
5e) if z islGreater than sigmalAnd islGreater than 0.01 sigmalThen return to substep 5b), σlIs a set noise level; otherwise, jumping to substep 5 f);
5f) will present EsSubstituting the value of the clutter ridge guiding vector matrix phi 'into a calculation formula of a new clutter ridge guiding vector base matrix phi' to obtain a fitted clutter ridge guiding vector base matrix psi 'of a matrix phi'*Psi'; obtain the current EsThe fitting coefficient vector alpha corresponding to the value oflCurrent E is addedsThe fitting coefficient vector alpha corresponding to the value oflBest fitting coefficient of basis matrix recorded as ith distance unit to be detectedObtaining the fitted residual data x of the first distance unit to be detectedl-ψ′*αl *。
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| CN112255595A (en) * | 2020-10-17 | 2021-01-22 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Sea clutter data preprocessing method based on simulated airborne measurement |
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| CN105044691A (en) * | 2015-06-03 | 2015-11-11 | 西安电子科技大学 | Rapid radar performance assessment method in sea cluster background |
| CN105022040A (en) * | 2015-07-08 | 2015-11-04 | 西安电子科技大学 | Array element error estimation method based on clutter data combined fitting |
| CN106896345A (en) * | 2017-02-14 | 2017-06-27 | 佛山市三水区希望火炬教育科技有限公司 | A kind of strategic balloon radar for having conduct monitoring at all levels |
| CN108226920A (en) * | 2018-01-09 | 2018-06-29 | 电子科技大学 | A kind of maneuvering target tracking system and method based on predicted value processing Doppler measurements |
| CN108387884A (en) * | 2018-05-25 | 2018-08-10 | 西安电子科技大学 | Knowledge based assists the airborne radar clutter suppression method of sparse progressive minimum variance |
| CN108387884B (en) * | 2018-05-25 | 2022-01-07 | 西安电子科技大学 | Airborne radar clutter suppression method based on knowledge-assisted sparse progressive minimum variance |
| CN108761417A (en) * | 2018-05-31 | 2018-11-06 | 西安电子科技大学 | Knowledge based assists the airborne radar clutter suppression method of maximum likelihood |
| CN108761417B (en) * | 2018-05-31 | 2021-12-10 | 西安电子科技大学 | Airborne radar clutter suppression method based on knowledge-aided maximum likelihood |
| CN112255595A (en) * | 2020-10-17 | 2021-01-22 | 中国电波传播研究所（中国电子科技集团公司第二十二研究所） | Sea clutter data preprocessing method based on simulated airborne measurement |
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