CN111308431B - Two-dimensional two-pulse cancellation method based on estimation error - Google Patents

Two-dimensional two-pulse cancellation method based on estimation error Download PDF

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CN111308431B
CN111308431B CN202010126016.2A CN202010126016A CN111308431B CN 111308431 B CN111308431 B CN 111308431B CN 202010126016 A CN202010126016 A CN 202010126016A CN 111308431 B CN111308431 B CN 111308431B
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CN111308431A (en
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张万绪
邵启伦
周延
李艳艳
汪霖
姜博
陈晓璇
孟娜
刘成
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Northwest University
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Abstract

The invention discloses a two-dimensional two-pulse cancellation method based on estimation error, which comprises the following steps: obtaining an estimated normalized Doppler frequency according to an estimation error of the airborne radar system; obtaining a space-time steering vector dictionary according to the estimated normalized Doppler frequency; obtaining a guide vector most relevant to a distance unit to be detected according to the space-time guide vector dictionary; and obtaining a filter coefficient matrix according to the most relevant guide vector. The two-dimensional two-pulse cancellation method based on the estimation error applies the priori knowledge of the airborne radar system estimation error to the design of the filter coefficient matrix, and improves the filtering performance of the TDPC and the moving target detection capability of a subsequent non-adaptive detection algorithm or STAP algorithm.

Description

Two-dimensional two-pulse cancellation method based on estimation error
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a two-dimensional two-pulse cancellation method based on estimation errors.
Background
For a fast moving platform, clutter received by an airborne radar needs to be suppressed from a space-time joint domain where the clutter is separated from a moving target. Space-time Adaptive Processing (STAP) has been proposed by Brennan et al and has been considered as an effective tool for airborne radar clutter suppression and target detection. The computational load and the requirement of training samples are two major factors that limit their widespread application, which has prompted the development of suboptimal dimension-reduction and rank-reduction STAP algorithms.
At present, knowledge-aided (KA) radars which improve STAP performance under small sample training by using priori knowledge such as Digital Elevation Maps (DEM) and geospatial databases are receiving more and more attention of researchers. In KA radar, prediction of clutter distribution applies a variety of information. For example, if the platform velocity and yaw angle (from inertial navigation system and global positioning system) are known, in combination with radar operating parameters, the distribution of ground clutter in the azimuth-doppler domain can be determined in advance. The Two-Dimensional Two-Pulse cancellation method (TDPC) is generated based on the prior knowledge, is a non-adaptive clutter filter which actually utilizes radar working parameters and platform speed knowledge, and can effectively suppress airborne radar clutter.
However, in practice, although some radar parameters, such as the operating wavelength and the elevation angle of the range unit to be detected, can be accurately known, due to uncertain factors such as the complexity of the flight environment, errors exist in the estimation of another part of parameters, such as the platform speed and the yaw angle, which will cause the clutter suppression capability to deteriorate, and even affect the target detection, and the application of inaccurate parameters in the TDPC design will affect the clutter filtering performance thereof.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a two-dimensional two-pulse cancellation method based on estimation error. The technical problem to be solved by the invention is realized by the following technical scheme:
a two-dimensional two-pulse cancellation method based on estimation error comprises the following steps:
obtaining an estimated normalized Doppler frequency according to an estimation error of the airborne radar system;
obtaining a space-time steering vector dictionary according to the estimated normalized Doppler frequency;
obtaining a guide vector most relevant to a distance unit to be detected according to the space-time guide vector dictionary;
and obtaining a filter coefficient matrix according to the most relevant guide vector.
In one embodiment of the present invention, obtaining the estimated normalized doppler frequency from the estimation error of the airborne radar system includes:
and calculating the estimated normalized Doppler frequency according to the estimated platform speed error and the estimated yaw angle error of the airborne radar system.
In an embodiment of the present invention, the expression of the estimated normalized doppler frequency is:
Figure BDA0002394401390000021
wherein f isd_estRepresenting the estimated normalized Doppler frequency, fdRepresenting true Doppler frequency, Δ θcRepresenting yaw angle error, thetarA true yaw angle is represented and,
Figure BDA0002394401390000022
denotes the elevation angle of the clutter block, λ denotes the operating wavelength, frIndicating the radar operating frequency.
In an embodiment of the present invention, obtaining a space-time steering vector dictionary according to the estimated normalized doppler frequency includes:
uniformly dividing the estimated normalized Doppler frequency into a plurality of samples within a certain range;
a dictionary of steering vectors is defined based on the estimated normalized doppler frequency in each sample.
In an embodiment of the present invention, the space-time steering vector dictionary is:
Figure BDA0002394401390000031
wherein B represents a space-time steering vector dictionary, viRepresenting the velocity, theta, of the antenna arrayiIndicating azimuth, N', of a spur blockcRepresenting an assumed number, N, of clutter blocks uniformly distributed over the entire azimuth angledRepresenting the number of samples, f', of the estimated normalized Doppler frequency domaind,jRepresenting the estimated normalized doppler frequency in each sample.
In an embodiment of the present invention, obtaining a steering vector most relevant to a distance unit to be detected according to the space-time steering vector dictionary includes:
searching a guiding vector most relevant to the distance unit to be detected from the space-time guiding vector dictionary;
and repeating iterative search on the space-time guide vector dictionary until a termination condition is met or a preset maximum iteration number is reached so as to obtain the most relevant guide vector.
In one embodiment of the present invention, the termination condition is:
||BHeq-1||∞＜ε；
where ε represents a constant representing the threshold value, eq-1Representing the space-time snapshot of the qth iteration of the distance unit to be detected, | | | | | luminance∞The symbol represents an infinite norm.
In one embodiment of the invention, the most relevant steering vector is:
Figure BDA0002394401390000032
wherein, atRepresenting a space-domain steering vector, asRepresenting the time-domain steering vector and q the number of iterations.
In an embodiment of the present invention, obtaining a filter coefficient matrix according to the steering vector includes:
obtaining corresponding characteristic values and normalized characteristic vectors according to the guide vectors;
and obtaining a filter coefficient matrix according to the eigenvalue and the normalized eigenvector.
In one embodiment of the present invention, the filter coefficient matrix is:
Figure BDA0002394401390000041
and is
Figure BDA0002394401390000042
Wherein A isRTDPCRepresenting a matrix of filter coefficients, DRA spatial-domain phase matrix is represented,
Figure BDA0002394401390000043
representing a Doppler phase array, HRTDPCThe dimension is (K-1) NxKN, N represents the number of array elements, K represents the number of pulses, INRepresenting an identity matrix of dimension N.
The invention has the beneficial effects that:
the two-dimensional two-pulse cancellation method based on the estimation error, provided by the invention, applies the priori knowledge of the airborne radar system estimation error to the design of the filter coefficient matrix, and compared with the traditional TDPC, the clutter suppression performance is greatly improved; meanwhile, the moving target detection capability of a subsequent non-adaptive detection algorithm or STAP algorithm is improved, and the applicability of the TDPC in practical application is improved.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Fig. 1 is a schematic flowchart of a two-dimensional two-pulse cancellation method based on estimation error according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an airborne radar system provided by an embodiment of the invention;
FIG. 3 is a diagram illustrating a comparison of Doppler frequency error curves provided by an embodiment of the present invention;
FIGS. 4 a-4 c are comparison graphs of pre-filtered clutter spectra before and after providing a yaw angle of 0 degrees according to an embodiment of the present invention;
FIGS. 5 a-5 c are graphs comparing pre-filtered clutter spectra before and after a yaw angle of 30 degrees according to an embodiment of the present invention;
fig. 6 is a graph of signal-to-noise ratio loss for various algorithms provided by embodiments of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a two-dimensional two-pulse cancellation method based on estimation error according to an embodiment of the present invention, including:
s1: obtaining an estimated normalized Doppler frequency according to an estimation error of the airborne radar system;
in the present embodiment, an error-based Two-Dimensional Two-Pulse cancellation method (error-based Two-Dimensional Pulse-to-Pulse cancel, error-based TDPC) is proposed based on a Two-Dimensional Two-Pulse cancellation method (TDPC). The TDPC method will be described first.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an airborne radar system according to an embodiment of the present invention, wherein an antenna array is along a positive directionxDirection at a speed vaAnd (5) moving at a constant speed. The antenna array is composed of N array elements with the distance d, and under the condition of not considering the curvature of the earth, the measured distance unit is uniformly divided into NcAnd each independent clutter block. Azimuth angle theta of clutter blockiAnd elevation angle
Figure BDA0002394401390000051
The position of the ith clutter block is described. Suppose that the airborne radar system is in a CPI with a period TrAnd repeatedly sending K pulses, wherein the clutter vector of the distance unit to be detected received at the Kth pulse is as follows:
Figure BDA0002394401390000052
wherein
Figure BDA0002394401390000053
To normalize the Doppler frequency, θcIs yaw angle, αiIn order to be able to measure the amplitude of the echo,
Figure BDA0002394401390000061
is a spatial steering vector, which can be expressed as:
Figure BDA0002394401390000062
wherein
Figure BDA0002394401390000063
For normalizing spatial domain frequency, λ is working wavelength, (. degree)TIs the transpose of the vector. The clutter space-time snapshot may be expressed as:
Figure BDA0002394401390000064
at(fd,i)＝[1,exp(j2πfd,i),…,exp(j2π(K-1)fd,i)]Tin the form of a time-domain steering vector,nis a white noise vector. Re-expressing equation (1) as a matrix vector form:
x(k)＝DF(k)a (4)
wherein
Figure BDA0002394401390000065
Figure BDA0002394401390000066
Figure BDA0002394401390000067
As can be seen from equation (4), the TDPC filter coefficient matrix is calculated from D and f (k) determined from a priori knowledge. Although some radar parameters, such as the operating wavelength and the elevation angle of the range bin to be detected, may be accurately known, due to uncertainty factors, the estimation of another part of the parameters will have errors, such as platform speed and yaw angle. Inaccurate parameters applied in TDPC design must affect its clutter filtering performance.
Therefore, the embodiment provides a two-dimensional two-pulse cancellation method based on estimation errors, and prior knowledge of the estimated platform speed and the yaw angle errors is applied to the design of a filter coefficient matrix, so as to improve the performance loss of the TDPC under inaccurate prior knowledge.
Further, the estimated normalized Doppler frequency is calculated from an estimated platform velocity error and a yaw angle error of the airborne radar system.
First, assume that the errors in estimating the platform velocity and yaw angle are Δ v, respectivelyaAnd Δ θc. Then the estimated normalized doppler frequency can be expressed as:
Figure BDA0002394401390000071
wherein, thetar＝θ+θcRepresenting a true angle.
For the first two terms and the last two terms in equation (8), respectively, it can be further simplified as:
Figure BDA0002394401390000072
for a typical airborne radar system, the inertial navigation system and the global positioning system measure velocity and yaw angle with an accuracy of about 0.1m/s to 0.2m/s and 1 to 1.5 degrees, respectively. Generally, airborne radars are capable of speeds in excess of hundreds of meters per second. Therefore, the temperature of the molten metal is controlled,
Figure BDA0002394401390000073
can be ignored. At the same time, when Δ θcVery muchIn the course of an hour,
Figure BDA0002394401390000074
and sin Δ θc≈Δθc. Equation (8) can be further simplified as:
Figure BDA0002394401390000075
to this end, estimating the normalized doppler frequency is only related to estimating the yaw angle error. True Doppler frequency fdThe error from the estimated normalized Doppler frequency is
Figure BDA0002394401390000081
Further, please refer to fig. 3, fig. 3 is a schematic diagram illustrating a comparison of doppler frequency error curves according to an embodiment of the present invention; the methods using equations (8) and (11), Δ θ, are plotted in FIG. 3cMean Doppler frequency estimation error curves of 0.5 degrees, 1 degree and 1.5 degrees, respectively, (which are defined as
Figure BDA0002394401390000082
). As can be seen from fig. 3, the error between the normalized doppler frequency and the true doppler frequency estimated by equation (8) is substantially the same as the error between the normalized doppler frequency and the true doppler frequency estimated by equation (11), and the feasibility of equation (11) is verified.
Thus, the estimated normalized doppler frequency can be expressed as:
Figure BDA0002394401390000083
wherein f isd_estRepresenting the estimated normalized Doppler frequency, fdRepresenting true Doppler frequency, Δ θcRepresenting yaw angle error, thetarA true yaw angle is represented and,
Figure BDA0002394401390000088
denotes the elevation angle of the clutter block, λ denotes the operating wavelength, frIndicating radar operating frequency。
Further, from the above analysis, it can be seen that the yaw angle in the estimation of the normalized Doppler frequency is dominant, Δ fdWith Δ vaThe change in (c) is negligible. Therefore, when Δ θcWhen the determination is made, the user can select the specific part,
Figure BDA0002394401390000084
wherein
Figure BDA0002394401390000085
Is the maximum normalized doppler frequency.
S2: obtaining a space-time steering vector dictionary according to the estimated normalized Doppler frequency;
further, the estimated normalized Doppler frequency is evenly divided into a plurality of samples within a certain range; a dictionary of steering vectors is then defined based on the estimated normalized doppler frequency in each sample.
Specifically, let
Figure BDA0002394401390000086
Then the normalized Doppler frequency domain is estimated at fd-Δfd,max,fd+Δfd,max]In the range, it can be uniformly divided into NdIn one sample, is represented as
Figure BDA0002394401390000087
Then a space-time steering vector dictionary may be defined as
Figure BDA0002394401390000091
Wherein B represents a space-time steering vector dictionary, viRepresenting the velocity, theta, of the antenna arrayiIndicating azimuth, N', of a spur blockcRepresenting an assumed number, N, of clutter blocks uniformly distributed over the entire azimuth angledRepresenting the number of samples, f', of the estimated normalized Doppler frequency domaind,jRepresents the estimated normalized Doppler frequency in each sample, an
Figure BDA0002394401390000092
S3: obtaining a guide vector most relevant to a distance unit to be detected according to the space-time guide vector dictionary;
in the present embodiment, the space-time steering vector dictionary B and the distance unit to be detected are obtained when the space-time steering vector dictionary B and the distance unit to be detected are obtainedxAfter the snapshot, iteratively find the sum from BxThe most relevant steering vector.
Further, searching a guide vector most relevant to the distance unit to be detected from the space-time guide vector dictionary; and repeating iterative search on the space-time guide vector dictionary until a termination condition is met or a preset maximum iteration number is reached so as to obtain the most relevant guide vector.
Specifically, a first iteration is performed. Let e0X, the most relevant steering vector is selected as
Figure BDA0002394401390000093
The corresponding eigenvalue and normalized eigenvector can be expressed as:
Figure BDA0002394401390000094
Figure BDA0002394401390000095
thenxThe residual vector between the normalized feature vector at the first iteration is e1＝x-λ1u1。
In order to continuously improve the estimation precision of the clutter plus noise covariance matrix, the iterative search of the space-time guide vector dictionary B needs to be repeatedly carried out until a termination condition is met or the maximum iteration number q is reachedmax。
In the present embodiment, the termination condition is | | BHeq-1||∞< ε, wherein ε represents a constant representing a threshold value, eq-1Representing the space-time snapshot of the qth iteration of the distance unit to be detected, | | | | | luminance∞The symbol represents an infinite norm.
At the qth iteration, the most relevant steering vector is selected as:
Figure BDA0002394401390000101
wherein, atRepresenting a space-domain steering vector, asRepresenting a time domain steering vector.
S4: and obtaining a filter coefficient matrix according to the most relevant guide vector.
Further, obtaining a corresponding characteristic value and a normalized characteristic vector according to the guide vector; and obtaining a filter coefficient matrix according to the eigenvalue and the normalized eigenvector.
Specifically, according to the most relevant steering vector of the q-th iteration obtained in S3, the eigenvalue and normalized eigenvector of the q-th iteration are obtained as follows:
Figure BDA0002394401390000102
Figure BDA0002394401390000103
it should be noted here that since the tracking method is adopted for searching and Schmidt orthogonalization processing is performed in iteration, u isiAnd uj(i ≠ j) is mutually orthogonal. After obtaining the desired steering vector from B, we obtain:
Figure BDA0002394401390000104
Figure BDA0002394401390000105
further, the filter coefficient matrix may be expressed as
Figure BDA0002394401390000106
And is
Figure BDA0002394401390000111
Wherein A isRTDPCRepresenting a matrix of filter coefficients, DRA spatial-domain phase matrix is represented,
Figure BDA0002394401390000112
representing a Doppler phase array, HRTDPCThe dimension is (K-1) NxKN, N represents the number of array elements, K represents the number of pulses, INRepresenting an identity matrix of dimension N.
The two-dimensional two-pulse cancellation method based on the estimation error, provided by the invention, applies the priori knowledge of the airborne radar system estimation error to the design of the filter coefficient matrix, and compared with the traditional TDPC, the clutter suppression performance is greatly improved; meanwhile, the moving target detection capability of a subsequent non-adaptive detection algorithm or STAP algorithm is improved, and the applicability of the TDPC in practical application is improved.
Example two
Based on the first embodiment, the two-dimensional two-pulse cancellation method based on the estimation error provided in the first embodiment is used as a pre-filter before a subsequent Space-Time Matching (STM) or dimension-reduced STAP (Space-Time Matching) method, and performs the first-stage and filtering process on the clutter.
In particular, assume that the space-time steering vector of the target is represented as
Figure BDA0002394401390000113
Wherein a ist∈CK＝1And as∈CN＝1Respectively as time-domain and space-domain guide vectors, and performing clutter pre-filtering
Figure BDA0002394401390000114
Thereafter, the second stage may employ a cascade of STMs or dimensionality-reduced STAPs to detect moving objects.
Further, if the STM algorithm is subsequently adopted, the weight vector is:
Figure BDA0002394401390000115
wherein, is the Hadamard product between vectors, btAnd bsTime domain and space domain static window vectors for attenuating side lobes, respectively.
If the dimension reduction STAP algorithm is adopted subsequently, the dimension reduction matrix is assumed to beT∈Cr＝(K-1)NAnd performing dimensionality reduction operation on the pre-filtered data and the target guide vector to obtain:
Figure BDA0002394401390000125
the reduced-dimension space-time filter can be expressed as:
Figure BDA0002394401390000121
wherein, mu is a constant number,
Figure BDA0002394401390000122
for dimensionality reduction of data
Figure BDA0002394401390000123
In practical applications, it can be replaced by its own maximum likelihood estimate.
In the embodiment, the two-dimensional two-pulse cancellation method based on the estimation error is used as a prefilter and a subsequent non-adaptive detection algorithm or STAP algorithm, so that the moving target detection capability of the subsequent algorithm is improved.
EXAMPLE III
The beneficial effects of the present invention are further explained by simulation experiments below.
The simulation uses a uniform linear array with the array element number N being 8 and the array element spacing d being 0.1 m. In the case of one CPI, the CPI is,k-16 pulses at a pulse repetition frequency frTransmission 2800 Hz. Aircraft platform height haSpeed v 9Kma140m/s, the Noise to Noise Ratio (CNR) is 60 dB. The Doppler frequency of the moving target in the distance unit to be detected is fdr＝0.2fr. The errors of the estimated speed and the yaw angle are respectively set to Δ va0.5m/s and Δ θc＝1.5°。
The performance of the error-based TDPC is verified by using Minimum Variance Distortionless Response (MVDR) spectrum, which is defined as
Figure BDA0002394401390000124
Wherein, s (phi, f)d) As the target steering vector, R ═ Rc+n+RsThe clutter noise plus the target covariance matrix.
Simulation experiment I
Referring to fig. 4a to 4c, fig. 4a to 4c are comparison graphs of clutter spectra before and after pre-filtering when the yaw angle is 0 degree according to an embodiment of the present invention, in which fig. 4a is an original clutter spectrum before filtering, and a normalized doppler frequency f exists near a main clutter regiond/frTarget 0.2. The clutter ridges are distributed along the main diagonal of the angle-doppler plane. Targets to the right of the clutter ridge are much weaker than the strong clutter. Targets to the right of the clutter ridge are much weaker than the strong clutter. Fig. 4b is a clutter spectrum after filtering by the conventional TDPC, and fig. 4c is a clutter-based TDPC filtered clutter spectrum provided by the present invention. Experimental results show that when the estimated speed and the estimated yaw angle are inaccurate, the signal-to-noise ratio is greatly reduced after the existing TDPC is processed, and the target detection capability of the existing TDPC is seriously reduced. After the error-based TDPC provided by the invention is processed, the signal-to-noise ratio is almost the same as that of the signal-to-noise ratio shown in figure 4a, and the clutter is effectively inhibited.
Simulation experiment two
The simulation experiment verifies the robustness of the cancellation method under the non-positive side view condition. Referring to fig. 5a to 5c, fig. 5a to 5c are comparison graphs of clutter spectra before and after pre-filtering when the yaw angle is 30 degrees according to an embodiment of the present invention, where fig. 5a is an original clutter spectrum before filtering, fig. 5b is a clutter spectrum after filtering by a conventional TDPC, and fig. 5c is a clutter-based TDPC filtered clutter spectrum provided by the present invention. From the experimental results, it can be seen that due to the change of the yaw angle, the clutter ridges are distributed in a semi-elliptical shape on the angle-doppler plane, and the doppler frequency and the airspace frequency of the clutter ridges are in a nonlinear relationship. Under the condition, the error-based TDPC provided by the invention can still effectively inhibit clutter under the condition of keeping the signal-to-noise ratio unchanged basically. However, as in the front-looking scene, existing TDPCs can significantly reduce the signal-to-noise ratio when the prior knowledge is inaccurate. Therefore, when the speed and the yaw angle of the platform are used as priori knowledge and estimation is not accurate, the clutter suppression performance of the error-based TDPC provided by the invention is greatly improved compared with that of the traditional TDPC.
Simulation experiment III
The signal-to-noise ratio loss is defined as the ratio of the output signal-to-noise-and-noise ratio to the optimal signal-to-noise-and-noise ratio, and is an important index for measuring the detection performance of the STAP algorithm. The effect of the invention will be further illustrated by comparing the signal-to-noise ratio loss of different algorithms.
Referring to fig. 6, fig. 6 is a signal-to-noise ratio loss comparison diagram of various algorithms provided by the embodiment of the present invention, where fig. 6 specifically shows performance comparisons of a factor method (FA), an Extended factor method (EFA), a TDPC + FA, a TDPC + EFA, an error-based TDPC + FA, and an error-based TDPC + EFA, where TDPC + FA, TDPC + EFA, error-based TDPC + FA, and error-based TDPC + EFA represent cascaded FAs and EFAs with TDPC and error-based TDPC, respectively. As can be seen from FIG. 6, the SNR loss curves of TDPC + FA and error-based TDPC + FA are greatly improved compared with the original FA. Near the main clutter region, TDPC + FA and error-based TDPC + FA are at the normalized Doppler frequency fd/frThe loss of the signal-to-noise ratio at 0.1 is respectively 10dB and 25dB higher than that of the original FA, and the obvious improvement of the performance of the loss of the signal-to-noise ratio of the main clutter area is beneficial to the detection of the slow-speed moving target. At the same time, at the normalized Doppler frequency fd/frAt 0.3, their signal-to-noise ratio losses are about 20dB and 27dB higher than the original FA value, respectively. The signal-to-noise ratio loss of TDPC + EFA and error-based TDPC + EFA is also improved compared with the original EFA.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (1)

1. A two-dimensional two-pulse cancellation method based on estimation error is characterized by comprising the following steps:
calculating and estimating normalized Doppler frequency according to an estimated platform speed error and an estimated yaw angle error of the airborne radar system, wherein the calculation formula is as follows:
Figure FDA0003293606420000011
wherein f isd_estRepresenting the estimated normalized Doppler frequency, fdRepresenting true Doppler frequency, Δ θcRepresenting yaw angle error, thetarA true yaw angle is represented and,
Figure FDA0003293606420000013
denotes the elevation angle of the clutter block, λ denotes the operating wavelength, frRepresents the radar operating frequency;
obtaining a space-time steering vector dictionary according to the estimated normalized Doppler frequency, comprising:
uniformly dividing the estimated normalized Doppler frequency into a plurality of samples within a certain range;
defining a space-time steering vector dictionary from the estimated normalized Doppler frequency in each sample; wherein the space-time steering vector dictionary is:
Figure FDA0003293606420000012
wherein B represents a space-time steering vector dictionary, viRepresenting the velocity, theta, of the antenna arrayiRepresenting azimuth, N 'of clutter blocks'cRepresenting an assumed number, N, of clutter blocks uniformly distributed over the entire azimuth angledDenotes the number of samples, f 'estimated from the normalized Doppler frequency domain'd,jRepresenting the estimated normalized doppler frequency in each sample;
obtaining a guide vector most relevant to a distance unit to be detected according to the space-time guide vector dictionary, wherein the method comprises the following steps:
searching a guiding vector most relevant to the distance unit to be detected from the space-time guiding vector dictionary;
repeating iterative search on the space-time guide vector dictionary until a termination condition is met or a preset maximum iteration number is reached to obtain the most relevant guide vector; wherein the termination condition is:
||BHeq-1||∞＜ε；
where ε represents a constant representing the threshold value, eq-1Representing the space-time snapshot of the qth iteration of the distance unit to be detected, | | | | | luminance∞The symbol represents an infinite norm;
the most relevant steering vectors are:
Figure FDA0003293606420000021
wherein, atRepresenting a space-domain steering vector, asRepresenting a time domain steering vector, and q represents the number of iterations;
obtaining a filter coefficient matrix according to the most relevant steering vector, including:
obtaining corresponding characteristic values and normalized characteristic vectors according to the guide vectors;
obtaining a filter coefficient matrix according to the eigenvalue and the normalized eigenvector; the filter coefficient matrix is:
Figure FDA0003293606420000022
and is
Figure FDA0003293606420000023
Wherein A isRTDPCRepresenting a matrix of filter coefficients, DRA spatial-domain phase matrix is represented,
Figure FDA0003293606420000024
representing a Doppler phase array, HRTDPCThe dimension is (K-1) NxKN, N represents the number of array elements, K represents the number of pulses, INRepresenting an identity matrix of dimension N.
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