CN106970358B - Optimization method for angular Doppler registration of clutter spectrum of non-normal side-looking array radar - Google Patents
Optimization method for angular Doppler registration of clutter spectrum of non-normal side-looking array radar Download PDFInfo
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Abstract
The invention belongs to the technical field of radars, and discloses an optimization method for angular Doppler registration of clutter spectrum of a non-front side-view array radar, which comprises the following steps: obtaining clutter data of each range gate received by the non-front side view array radar, determining a space Doppler frequency position where a maximum power point of each training sample unit is located, determining a clutter spectrum center, calculating a correction matrix corresponding to the clutter data of each training sample unit by taking the Doppler frequency and the space frequency of a distance unit to be detected as references, and calculating a corrected clutter covariance matrix of the distance unit to be detected, so that a filtering weight vector is obtained, and clutter suppression is performed on the clutter data of the non-front side view array radar, so that clutter of the non-front side view array radar is more uniform after the clutter spectrum processing.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to an optimization method for angular Doppler registration of a clutter spectrum of a non-front side-view array radar, which is suitable for performing ideal compensation on the radar clutter spectrum when errors exist in known radar configuration parameters.
Background
The airborne radar has the functions of air warning, reconnaissance, control, weapon guidance and the like, and plays an important role in national defense construction in China. When the airborne radar works in a downward view, the moving target is completely covered and cannot be identified due to wide ground clutter distribution range and high intensity. How to effectively suppress ground clutter is an important content of airborne radar signal processing. A space-time adaptive processing (STAP) method is an effective means for inhibiting ground clutter, compensating clutter spectrum broadening caused by platform motion and detecting a ground slow target. The Clutter Covariance Matrix (CCM) is a decisive factor for the Clutter suppression performance of the space-time adaptive processing method. In practical application, a clutter covariance Matrix of a unit to be detected is estimated by using a neighboring training Sample unit which is Independently and Identically Distributed (IID) with the unit to be detected through a Sample covariance Inversion (SMI) method, so as to realize adaptive clutter suppression, and in order to make the loss of an output signal-to-noise ratio of a space-time filter less than 3dB, the number of training samples which are independently and identically distributed and used for estimating the clutter covariance Matrix needs to be more than twice of the degree of freedom of a system. For a non-side looking array, the ground clutter Doppler frequency has serious distance dependence, and especially under a short-range condition, the distance dependence is more obvious, so that clutter distribution does not meet an independent same distribution condition, a clutter covariance matrix of a unit to be detected cannot be accurately estimated by using a training sample, and the clutter suppression performance of the space-time adaptive processing method is reduced.
Currently, there are many methods for compensating for the distance dependence, including Doppler Warping (DW) method, Angle Doppler Compensation (ADC) method, Adaptive Angle Doppler Compensation (A2 DC) method, and Registration Based Compensation (RBC) method. In the DW method, the difference of the Doppler frequency of the clutter between the training sample and the detection distance unit caused by different pitch angles is essentially compensated, so that the clutter in the training sample is approximately stable. In addition to compensating for Doppler differences between the training samples and the range cells to be detected, the ADC method and the A2DC method also compensate for spatial frequency differences caused by different pitch angles. The RBC method utilizes time domain smoothing sub-snapshots to extract radar clutter spectrum peaks, can realize complete compensation of radar clutter, and can obtain good radar clutter suppression performance under ideal conditions.
However, in the above compensation method, the DW method performs the mainlobe clutter spectrum center compensation only in the doppler domain, and the obtained compensation effect is not particularly preferable. The ADC method registers clutter characteristics of each training sample unit and a sample unit to be detected through compensation in a space angle domain and a Doppler domain, and calculates a clutter conversion matrix, so that clutter distance dependency is inhibited to a certain extent, and the compensation effect of the method is more obvious for mainlobe clutter. However, in practical applications, the clutter conversion matrix of each training sample needs to be calculated by using a clutter space-time coupling relationship according to flight configuration parameters (such as a pitch angle, an azimuth angle, a yaw angle, a speed, and the like) provided by the aircraft platform, and therefore, an error of any configuration parameter may have a certain influence on the clutter distance dependency compensation performance of the method. Although the method A2DC does not need to know configuration parameters and the radar clutter suppression performance is good under ideal conditions, in practice, the covariance matrix of radar clutter of each range cell is extremely unstable, and the radar clutter suppression performance is greatly reduced. The RBC method utilizes time-domain smoothing sub-snapshots to extract radar clutter spectrum peaks, so that the estimation accuracy of the radar clutter spectrum peaks and the robustness of the RBC method can influence the use of STAP technical performance.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide an optimization method for angular Doppler registration of a clutter spectrum of a non-front side view array radar, so that the clutter of the processed clutter spectrum of the non-front side view array radar is more uniform.
The invention provides an optimization method for angular Doppler registration of a clutter spectrum of a non-front side-view array radar. The method estimates the clutter spectrum of the non-positive side view array radar through an Adaptive Iterative method (IAA), obtains a clutter power approximate value of each training sample, determines the Doppler frequency position and the space frequency position of the maximum power point of each training sample, determines the clutter spectrum center of the training sample, then combines an Angle Doppler Compensation (ADC) method to perform two-dimensional compensation of an airspace and a Doppler domain, obtains training sample data consistent with the clutter statistical characteristics of a unit to be detected, enables the clutter of the non-positive side view array radar to be more uniform after the clutter spectrum processing, and further improves the clutter performance of a space-time Adaptive processing (STAP) technology in the application of the non-positive side view array radar.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
A method for optimizing the angular Doppler registration of the clutter spectrum of a non-forward side-looking array radar comprises the following steps:
step 1, acquiring clutter data of L distance units received by a non-front side view array radar, setting a kth distance unit as a distance unit to be detected, and taking other L-1 distance units except the distance unit to be detected as training sample units; l represents the total number of distance units contained in clutter data received by the non-front side view array radar;
step 2, setting a normalized spatial frequency interval [ -1,1] of the non-frontal side view array radar and a normalized Doppler frequency interval [ -1,1] of the non-frontal side view array radar; the normalized space frequency interval of the non-front side view array radar and the normalized Doppler frequency interval of the non-front side view array radar form a space-time two-dimensional plane of the non-front side view array radar;
step 3, let L equal to 1, L ∈ {1,2, …, L }, L ≠ k, L represents the L-th training sample unit;
step 4, sampling clutter data of the ith training sample unit on a space-time two-dimensional plane of the non-side view array radar to obtain Ks×KtSampling points; ksThe number of sampling points, K, of the clutter data of the ith training sample unit in the normalized space frequency interval of the non-side view array radartThe number of sampling points of clutter data of the ith training sample unit in a normalized Doppler frequency interval of the non-side view array radar is represented;
step 5, for the (p, q) th sampling point of the clutter data of the ith training sample unit on the space-time two-dimensional plane of the non-positive side view array radar, obtaining the space-time steering vector at the (p, q) th sampling point, wherein p belongs to {1,2, …, K ∈ [ ]t}，q∈{1，2，…，Ks}; thereby obtaining all K of clutter data of the first training sample unit on the space-time two-dimensional plane of the non-front side view array radars×KtSpace-time steering vectors at each sampling point;
step 6, calculating the initial power at the (p, q) th sampling point according to the clutter data of the l training sample unit and the space-time guide vector at the (p, q) th sampling pointp∈{1，2，…，Kt}，q∈{1，2，…，Ks}; and determining a clutter power spectrum P of clutter data of the ith training sample unit according to the initial power at each sampling point(0)；
Setting the initial value of the iteration times n as 1;
step 7, according to the power at the (p, q) th sampling pointAnd the space-time steering vector at the (p, q) th sampling point constructs a covariance matrix at the (p, q) th sampling pointp∈{1，2，…，Kt}，q∈{1，2，…，Ks}；
Step 8, according to the clutter data of the ith training sample unit, the space-time guide vector at the (p, q) th sampling point and the covariance matrix at the (p, q) th sampling pointCalculating the power at the (p, q) th sampling point after the nth iterationp∈{1，2，…，Kt}，q∈{1，2，…，Ks}; and according to the power at each sampling point after the nth iterationDetermining a clutter power spectrum P after nth iteration of clutter data of the ith training sample unit(n)；
Step 9, ifAnd 0＜If n is less than num, adding 1 to the value of n, and repeatedly executing the step 7 and the step 8; num represents the maximum value of the set number of iterations,expression solutionA norm;
if it isOr n is more than num, stopping iteration and calculating the power of each sampling point after the nth iterationThe final power of each sampling point in the clutter data of the ith training sample unit is used;
step 10, determining the maximum value of the final power at each sampling point in the clutter data of the first training sample unit, acquiring the normalized doppler frequency and the normalized spatial frequency at the sampling point corresponding to the maximum value, and respectively using the normalized doppler frequency and the normalized spatial frequency as the normalized doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit;
step 11, determining a normalized Doppler frequency and a normalized spatial frequency corresponding to a clutter power spectrum of clutter data of a distance unit to be detected; calculating a corrected Doppler frequency and a corrected spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit according to the normalized Doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit and the normalized Doppler frequency and the normalized spatial frequency corresponding to the clutter power spectrum of the clutter data of the distance unit to be detected;
step 12, calculating a Doppler domain conversion matrix and a spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit according to the corrected Doppler frequency and the corrected spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit, and further calculating a power spectrum corresponding correction matrix of the clutter data of the ith training sample unit according to the Doppler domain conversion matrix and the spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit;
obtaining clutter data of the first training sample unit after correction according to the clutter data of the first training sample unit and the power spectrum corresponding correction matrix of the clutter data of the first training sample unit;
step 13, adding 1 to the value of L, and repeatedly executing the steps 4 to 12 to obtain L-1 corrected clutter data, so as to calculate and obtain a covariance matrix of the clutter data of the distance unit to be detected according to the L-1 corrected clutter data;
and acquiring a space-time guide vector of the distance unit to be detected, so as to obtain a filtering weight vector according to the space-time guide vector of the distance unit to be detected and a covariance matrix of clutter data of the distance unit to be detected, and respectively carrying out angle Doppler registration on the clutter data of the L distance units according to the filtering weight vector.
The invention has the beneficial effects that: the method estimates the clutter spectrum of the non-front side view array radar by using an IAA method under the condition of single snapshot to obtain the clutter power approximate value of each training sample, determines the clutter spectrum center of each training sample, and then performs two-dimensional compensation of an airspace and a Doppler domain by combining an ADC method, so that the clutter of the non-front side view array radar is more uniform after the clutter spectrum is processed, and the clutter performance of a space-time adaptive processing (STAP) technology on the non-front side view array radar is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an optimization method for angular doppler registration of a clutter spectrum of a non-front side view array radar according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an optimal clutter spectrum under a non-front side view array radar;
FIG. 3 is a schematic diagram of a clutter spectrum obtained without compensation under a non-front side view array radar;
FIG. 4 is a schematic diagram of clutter spectrum obtained by using a conventional A2DC method under a non-front side view array radar;
FIG. 5 is a schematic diagram of a clutter spectrum obtained by using the method of the present invention under a non-forward looking array radar;
fig. 6 is a schematic diagram showing the comparison of clutter suppression improvement factors of the non-front side view array radar in the four cases shown in fig. 2, fig. 3, fig. 4, and fig. 5.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The embodiment of the invention provides an optimization method for angular Doppler registration of a clutter spectrum of a non-front side view array radar, as shown in figure 1, the method comprises the following steps:
step 1, acquiring clutter data of L distance units received by a non-front side view array radar, setting a kth distance unit as a distance unit to be detected, and taking other L-1 distance units except the distance unit to be detected as training sample units; and L represents the total number of the distance units contained in the clutter data received by the non-front side view array radar.
Generally, when clutter data of L distance units received by a non-front side view array radar includes target data, the distance unit where the target is located is used as a distance unit to be detected.
In step 1, obtaining clutter data of L distance units received by a non-front side view array radar specifically comprises:
setting an antenna of the non-front side view array radar to be a linear array consisting of N array elements which are uniformly distributed, wherein the non-front side view array radar transmits M pulses in a coherent processing interval;
sampling at a distance of L times on each pulse and each array element channel respectively, so that actually received clutter data is three-dimensional data with the length of N multiplied by M multiplied by L; recording clutter data received by the mth pulse of the nth array element of the ith distance unit as xnmlIf the array element of the nth distance unit receives the clutter dataIs marked as xnl＝[xn1l，xn2l，…，xnMl]TArranging clutter data received by N array elements of the first distance unit into NM multiplied by 1 dimension column vector XlSo as to obtain clutter data Xl ═ x of the l-th distance unit1l T，x2l T，…，xNl T]TWhere L ∈ {1,2, …, L }.
Generally, the number of training sample units is greater than 2 NM.
Step 2, setting a normalized spatial frequency interval [ -1,1] of the non-frontal side view array radar and a normalized Doppler frequency interval [ -1,1] of the non-frontal side view array radar; and the normalized space frequency interval of the non-front side view array radar and the normalized Doppler frequency interval of the non-front side view array radar form a space-time two-dimensional plane of the non-front side view array radar.
Clutter data of a certain distance unit is data on a two-dimensional plane constructed by array element number and pulse number, and is the most original acquired echo data; the space-time two-dimensional plane is a two-dimensional plane constructed by the normalized angle and Doppler frequency, and the angle and Doppler frequency are calculated according to known configuration parameters.
And step 3, letting L be 1, L be {1,2, …, L }, L ≠ k, and L represents the ith training sample unit.
Step 4, sampling clutter data of the ith training sample unit on a space-time two-dimensional plane of the non-side view array radar to obtain Ks×KtSampling points; ksThe number of sampling points, K, of the clutter data of the ith training sample unit in the normalized space frequency interval of the non-side view array radartAnd the number of sampling points of the clutter data of the ith training sample unit in the normalized Doppler frequency interval of the non-positive side view array radar is represented.
In step 4, Ks＝10N，kt＝10M；
Wherein, N represents the number of array elements contained in the antenna of the non-front side view array radar, and M represents the coherent processing of the non-front side view array radarNumber of pulses transmitted in intervals, KsThe number of sampling points, K, of the clutter data of the ith training sample unit in the normalized space frequency interval of the non-side view array radartAnd the number of sampling points of the clutter data of the ith training sample unit in the normalized Doppler frequency interval of the non-positive side view array radar is represented.
For a more accurate reconstruction of the covariance matrix, the spatial domain of the space-time plane is divided into K for each timesSeveral grid points, Doppler domain divided into KtA grid point, the normalized spatial frequency corresponding to each grid point can be represented as fs，q，q∈{1，2，…，KsThe normalized Doppler frequency can be expressed as fd，p，p∈{1，2，…，KtAccording to the lower bound theory of Cramer-Rao, the number of grid distributions is infinitely increased without obviously improving the performance of spectrum estimation, and K is selected for the purposes＝10N，ktIn this case, the space-time plane may be divided into K-K, 10MsKtOne grid point, 100NM grid points, clutter data for the current training sample may be represented by angular doppler data at the grid sample point. For the normalized spatial frequency interval [ -1,1 [)]Carrying out uniform sampling to obtain KsSampling points; for the normalized Doppler frequency interval [ -1,1 [)]Carrying out uniform sampling to obtain KtAnd (4) sampling points.
Step 5, for the (p, q) th sampling point of the clutter data of the ith training sample unit on the space-time two-dimensional plane of the non-positive side view array radar, obtaining the space-time steering vector at the (p, q) th sampling point, wherein p belongs to {1,2, …, K ∈ [ ]t}，q∈{1，2，…，Ks}; thereby obtaining all K of clutter data of the first training sample unit on the space-time two-dimensional plane of the non-front side view array radars×KtSpace-time steering vectors at each sampling point.
In step 5, obtaining the space-time steering vector at the (p, q) th sampling point specifically includes:
acquiring a steering vector of the (p, q) th sampling point on a normalized space frequency interval of the non-front side view array radar
Acquiring a steering vector of the (p, q) th sampling point on a normalized Doppler frequency interval of the non-normal side view array radar
Thus, the space-time steering vector at the (p, q) -th sampling point
Wherein f isd，pIndicating the normalized Doppler frequency, f, of the p-th sampling point in the normalized Doppler frequency interval of the non-frontal view array radars，qThe normalized spatial frequency of the q sampling point on the normalized spatial frequency interval of the non-front side view array radar is represented;represents kronecker product (.)TIndicating transposition.
Step 6, calculating the initial power at the (p, q) th sampling point according to the clutter data of the l training sample unit and the space-time guide vector at the (p, q) th sampling pointp∈{1，2，…，Kt}，q∈{1，2，…，Ks}; and determining a clutter power spectrum P of clutter data of the ith training sample unit according to the initial power at each sampling point(0)；
And the initial value of the iteration number n is set to 1.
In step 6, the initial power at the (p, q) th sampling point is calculated by using the following formula
Wherein, S (f)d，p，fs，q) Representing the space-time steering vector at the (p, q) -th sampling point, XlClutter data representing the ith training sample cell, (-)HRepresenting conjugate transposition, |, representing absolute value operation;
determining a clutter power spectrum P of clutter data of the ith training sample unit according to the initial power at each sampling point(0)Is composed ofdiag (-) denotes a diagonal matrix, i.e. the diagonal element isThe diagonal matrix of (a).
Step 7, according to the power at the (p, q) th sampling pointAnd the space-time steering vector at the (p, q) th sampling point constructs a covariance matrix at the (p, q) th sampling pointp∈{1，2，…，Kt}，q∈{1，2，…，Ks}。
In step 7, according to the power at the (p, q) th sampling pointAnd the space-time steering vector at the (p, q) th sampling point constructs a covariance matrix at the (p, q) th sampling pointThe following were used:
wherein, S (f)d，p，fs，q) Represents the (p, q) th sample pointThe space-time steering vector of (a).
Step 8, according to the clutter data of the ith training sample unit, the space-time guide vector at the (p, q) th sampling point and the covariance matrix at the (p, q) th sampling pointCalculating the power at the (p, q) th sampling point after the nth iterationp∈{1，2，…，Kt}，q∈{1，2，…，Ks}; and according to the power at each sampling point after the nth iterationDetermining a clutter power spectrum P after nth iteration of clutter data of the ith training sample unit(n)。
In step 8, according to the clutter data of the ith training sample unit, the space-time steering vector at the (p, q) th sampling point and the covariance matrix at the (p, q) th sampling pointCalculating the power at the (p, q) th sampling point after the nth iterationThe following were used:
wherein, S (f)d，p，fs，q) Representing the space-time steering vector at the (p, q) -th sampling point, XlClutter data representing the ith training sample cell, (-)HRepresenting conjugate transpose, | representing absolute value operation, (-)-1Representing matrix inversion;
according to the power at each sampling point after the nth iterationDetermining a clutter power spectrum P after nth iteration of clutter data of the ith training sample unit(n)The following were used:
wherein diag (-) denotes a diagonal matrix, i.e. the diagonal elements areThe diagonal matrix of (a).
Step 9, ifAnd 0＜If n is less than num, adding 1 to the value of n, and repeatedly executing the step 7 and the step 8; num represents the maximum value of the set number of iterations,expression solutionA norm;
if it isOr n is more than num, stopping iteration and calculating the power of each sampling point after the nth iterationAs the final power at each sampling point in the clutter data of the ith training sample unit.
Step 10, determining the maximum value of the final power at each sampling point in the clutter data of the ith training sample unit, obtaining the normalized doppler frequency and the normalized spatial frequency at the sampling point corresponding to the maximum value, and respectively using the normalized doppler frequency and the normalized spatial frequency as the normalized doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit.
Step 11, determining a normalized Doppler frequency and a normalized spatial frequency corresponding to a clutter power spectrum of clutter data of a distance unit to be detected; and calculating the corrected Doppler frequency and the corrected spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit according to the normalized Doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit and the normalized Doppler frequency and the normalized spatial frequency corresponding to the clutter power spectrum of the clutter data of the distance unit to be detected.
In the step 11: determining a normalized Doppler frequency f corresponding to a clutter power spectrum of clutter data of a distance unit to be detectedd，kAnd normalized spatial frequency fs，k：
Wherein, the lambda is the working wavelength of the non-front side view array radar,for the array element spacing of the uniform linear array, the carrier flies along the x axis at a speed v, and the included angle between the plane where the uniform linear array is positioned and the x axis and the plane vertical to the x-y plane isθkRepresenting the pitch angle corresponding to the clutter data of the distance unit to be detected,indicating the azimuth angle corresponding to the clutter data of the distance unit to be detected relative to the uniform linear array, wherein the array element number is N, and the pulse repetition frequency is fr；
Calculating the corrected Doppler frequency delta f corresponding to the power spectrum of the clutter data of the ith training sample unitd，l＝fd，l-fd，kAnd correcting the spatial frequency Δ fs，l＝fs，l-fs，k；
Wherein f isd，lNormalized Doppler frequency, f, corresponding to the power spectrum of the clutter data representing the ith training sample units，lNormalized spatial frequency, f, corresponding to the power spectrum of the clutter data representing the ith training sample unitd，kNormalized Doppler frequency, f, corresponding to a clutter power spectrum of clutter data representing a range cell to be detecteds，kAnd representing the normalized spatial frequency corresponding to the clutter power spectrum of the clutter data of the distance unit to be detected.
Step 12, calculating a Doppler domain conversion matrix and a spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit according to the corrected Doppler frequency and the corrected spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit, and further calculating a power spectrum corresponding correction matrix of the clutter data of the ith training sample unit according to the Doppler domain conversion matrix and the spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit;
and obtaining the clutter data of the corrected training sample unit according to the clutter data of the ith training sample unit and the power spectrum corresponding correction matrix of the clutter data of the ith training sample unit.
In step 12, calculating a power spectrum corresponding Doppler domain transformation matrix T of clutter data of the ith training sample unitd，lAnd spatial domain transformation matrix Ts，l：
Calculating to obtain a power spectrum corresponding correction matrix T of clutter data of the first training sample unitIAA-ADC：
Obtaining the clutter data of the first training sample unit after correction according to the clutter data of the first training sample unit and the power spectrum corresponding correction matrix of the clutter data of the first training sample unit
Wherein, Δ fd，lModified Doppler frequency, Δ f, corresponding to the power spectrum of clutter data representing the ith training sample cells，lRepresents the modified spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit,the kronecker product is expressed, N represents the number of array elements contained in the antenna of the non-front side view array radar, and M represents the number of pulses transmitted by the non-front side view array radar in one coherent processing interval.
Step 13, adding 1 to the value of L, and repeatedly executing the steps 4 to 12 to obtain L-1 corrected clutter data, so as to calculate and obtain a covariance matrix of the clutter data of the distance unit to be detected according to the L-1 corrected clutter data;
and acquiring a space-time guide vector of the distance unit to be detected, so as to obtain a filtering weight vector according to the space-time guide vector of the distance unit to be detected and a covariance matrix of clutter data of the distance unit to be detected, and respectively carrying out angle Doppler registration on the clutter data of the L distance units according to the filtering weight vector.
In step 13, calculating to obtain a covariance matrix of clutter data of the distance unit to be detected according to the L-1 corrected clutter data
Wherein the content of the first and second substances,representing corrected training sample unitsClutter data;
obtaining space-time guiding vector S (f) of distance unit to be detectedd，k，fs，k) According to the space-time steering vector S (f) of the distance unit to be detectedd，k，fs，k) And covariance matrix of clutter data of distance unit to be detectedDetermining a filter weight vectorNamely, it is
Wherein the content of the first and second substances,is a normalization constant.
And finally, clutter data of L distance units received by the non-front side view array radar are subjected to clutter suppression through the filtering weight vector respectively to obtain a uniform clutter spectrum.
It is necessary to supplement that the clutter suppression improvement factor IF (f) of the non-forward looking array radar is calculateddt) And the method is used for measuring the angle Doppler compensation effect of the clutter spectrum of the non-positive side view array radar.
Specifically, the expression is:
wherein, PCIndicating the set clutter input power, P, of a non-positive side view array radarNAnd representing the set non-front side view array radar noise input power.
The effects of the present invention can be further illustrated by the following simulation experiments.
Description of simulation experiment data
When the inhibition performance of clutter of the non-front side view array radar is evaluated, in order to compare with the traditional adaptive angle Doppler compensation method, the method disclosed by the invention adopts a uniform linear array in a simulation mode; and selecting proper repetition frequency, not considering the distance ambiguity problem, and processing the No. 60 distance unit when the noise-to-noise ratio of the non-front side view array radar array element level is 60 dB. The part simulates clutter power spectrums estimated by a sampling covariance inversion method after uncompensated, compensated by an angle Doppler compensation method and compensated by the method, and compares the clutter power spectrums with the optimal clutter power spectrums. Clutter power spectra of different methods all adopt Minimum Variance Distortionless Response (MVDR) spectra. Simulation parameters of the non-front side view array radar are shown in table 1.
TABLE 1
(II) simulation results and analysis
Simulation results of the present invention are shown in fig. 2 to 6; fig. 2 is a schematic diagram of an optimal clutter spectrum of a distance unit No. 60 under a non-front side view array radar, fig. 3 is a schematic diagram of a clutter spectrum obtained by uncompensating the distance unit No. 60 under the non-front side view array radar, fig. 4 is a schematic diagram of a clutter spectrum obtained by the distance unit No. 60 under the non-front side view array radar by using an A2DC method, and fig. 5 is a schematic diagram of a clutter spectrum obtained by the distance unit No. 60 under the non-front side view array radar by using an IAA-ADC; FIG. 6 is a comparison of uncompensated, A2DC method, IAA-ADC method clutter suppression improvement factor and optimal clutter suppression improvement factor.
Due to the fact that the number of samples is not enough in a non-uniform scene, uncompensated radar clutter spectrum is high in side lobe and poor in resolution; the spectrum with higher resolution can be obtained by both the A2DC method and the IAA-ADC method. However, the covariance matrix of radar clutter of each range unit is extremely unstable in practice by the method A2DC, so that the radar clutter suppression performance is greatly reduced; the IAA-ADC method used by the invention reconstructs the covariance matrix through an iteration method, so that the clutter spectrum of the radar of the non-front side view array is more uniform after being processed, and the performance of spectrum estimation is improved.
In conclusion, the simulation experiment verifies the correctness, the effectiveness and the reliability of the method.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for optimizing the angular Doppler registration of the clutter spectrum of a non-forward side-looking array radar is characterized by comprising the following steps:
step 1, acquiring clutter data of L distance units received by a non-front side view array radar, setting a kth distance unit as a distance unit to be detected, and setting other L-1 distance units except the distance unit to be detected as training sample units; l represents the total number of distance units contained in clutter data received by the non-front side view array radar;
step 2, setting a normalized spatial frequency interval [ -1,1] of the non-obverse side view array radar and a normalized Doppler frequency interval [ -1,1] of the non-obverse side view array radar; the normalized space frequency interval of the non-front side view array radar and the normalized Doppler frequency interval of the non-front side view array radar form a space-time two-dimensional plane of the non-front side view array radar;
step 3, let L be 1, L be e {1,2, …, L }, L be not equal to k, and L represent the sequence number of the training sample unit;
step 4, sampling clutter data of the ith training sample unit on a space-time two-dimensional plane of the non-side view array radar to obtain Ks×KtSampling points; ksArray radar for expressing clutter data of I training sample unit in non-positive side viewNormalized spatial frequency interval of (1) number of sampling points, KtThe number of sampling points of clutter data of the ith training sample unit in a normalized Doppler frequency interval of the non-side view array radar is represented;
step 5, for the (p, q) th sampling point of the clutter data of the ith training sample unit on the space-time two-dimensional plane of the non-positive side view array radar, obtaining the space-time steering vector at the (p, q) th sampling point, wherein p belongs to {1,2, …, K ∈ [ ]t}，q∈{1,2,…,Ks}; thereby obtaining all K of clutter data of the first training sample unit on the space-time two-dimensional plane of the non-front side view array radars×KtSpace-time steering vectors at each sampling point;
step 6, calculating the initial power at the (p, q) th sampling point according to the clutter data of the l training sample unit and the space-time guide vector at the (p, q) th sampling pointp∈{1,2,…,Kt}，q∈{1,2,…,Ks}; and determining a clutter power spectrum P of clutter data of the ith training sample unit according to the initial power at each sampling point(0)；
Setting the initial value of the iteration times i as 1;
step 7, according to the power at the (p, q) th sampling pointAnd the space-time steering vector at the (p, q) th sampling point constructs a covariance matrix at the (p, q) th sampling pointp∈{1,2,…,Kt}，q∈{1,2,…,Ks}；
Step 8, according to the clutter data of the ith training sample unit, the space-time guide vector at the (p, q) th sampling point and the covariance matrix at the (p, q) th sampling pointCalculating the power at the (p, q) th sampling point after the ith iterationp∈{1,2,…,Kt}，q∈{1,2,…,Ks}; and according to the power at the (p, q) th sampling point after the ith iterationDetermining clutter power spectrum P after ith iteration of clutter data of ith training sample unit(i)；
Step 9, ifAnd 0<i<num, adding 1 to the value of i, and repeatedly executing the step 7 and the step 8; num represents the maximum value of the set number of iterations,expression solutionA norm;
if it isOr i>num, stopping iteration and calculating the power at the (p, q) th sampling point after the ith iterationThe final power at the (p, q) th sampling point in the clutter data of the ith training sample unit;
step 10, determining the maximum value of the final power at each sampling point in the clutter data of the first training sample unit, acquiring the normalized doppler frequency and the normalized spatial frequency at the sampling point corresponding to the maximum value, and respectively using the normalized doppler frequency and the normalized spatial frequency as the normalized doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit;
step 11, determining a normalized Doppler frequency and a normalized spatial frequency corresponding to a clutter power spectrum of clutter data of a distance unit to be detected; calculating a corrected Doppler frequency and a corrected spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit according to the normalized Doppler frequency and the normalized spatial frequency corresponding to the power spectrum of the clutter data of the first training sample unit and the normalized Doppler frequency and the normalized spatial frequency corresponding to the clutter power spectrum of the clutter data of the distance unit to be detected;
step 12, calculating a Doppler domain conversion matrix and a spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit according to the corrected Doppler frequency and the corrected spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit, and further calculating a power spectrum corresponding correction matrix of the clutter data of the ith training sample unit according to the Doppler domain conversion matrix and the spatial domain conversion matrix corresponding to the power spectrum of the clutter data of the ith training sample unit;
obtaining clutter data of the first training sample unit after correction according to the clutter data of the first training sample unit and the power spectrum corresponding correction matrix of the clutter data of the first training sample unit;
step 13, adding 1 to the value of L, and repeatedly executing the steps 4 to 12 to obtain L-1 corrected clutter data, so as to calculate and obtain a covariance matrix of the clutter data of the distance unit to be detected according to the L-1 corrected clutter data;
and acquiring a space-time guide vector of the distance unit to be detected, so as to obtain a filtering weight vector according to the space-time guide vector of the distance unit to be detected and a covariance matrix of clutter data of the distance unit to be detected, and respectively carrying out angle Doppler registration on the clutter data of the L distance units according to the filtering weight vector to obtain a result after the clutter data of the L distance units are subjected to angle Doppler registration.
2. The method according to claim 1, wherein in step 1, clutter data of L range units received by the non-forward looking array radar is obtained, specifically:
setting an antenna of the non-front side view array radar to be a linear array consisting of N array elements which are uniformly distributed, wherein the non-front side view array radar transmits M pulses in a coherent processing interval;
sampling at a distance of L times on each pulse and each array element channel respectively, so that the actually received clutter data is three-dimensional data with dimensions of NxMxL; recording the clutter data received by the mth pulse of the nth array element of the z-th distance unit as xnmzIf the array element is located in the z-th range unit, the clutter data received by the nth array element is marked as xnz＝[xn1z,xn2z,…,xnMz]TArranging clutter data received by N array elements of the z-th distance unit into NM multiplied by 1 dimension column vector XzSo as to obtain clutter data X of z-th distance unitz＝[x1z T,x2z T,…,xNz T]TWherein z ∈ {1,2, …, L }, (·)TRepresenting a transpose; n-1, 2, …, N, M-1, 2, …, M; n represents the number of array elements contained in the antenna of the non-front side view array radar, and M represents the number of pulses transmitted by the non-front side view array radar in one coherent processing interval.
3. The method for optimizing the angular Doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein K is determined in step 4s＝10N，Kt＝10M；
Wherein N represents the number of array elements contained in the antenna of the non-front side view array radar, M represents the number of pulses transmitted by the non-front side view array radar in a coherent processing interval, and KsThe number of sampling points, K, of the clutter data of the ith training sample unit in the normalized space frequency interval of the non-side view array radartAnd the number of sampling points of the clutter data of the ith training sample unit in the normalized Doppler frequency interval of the non-positive side view array radar is represented.
4. The method according to claim 1, wherein in step 5, the space-time steering vector at the (p, q) th sampling point is obtained, specifically:
acquiring a steering vector of the (p, q) th sampling point on a normalized space frequency interval of the non-front side view array radar
Acquiring a steering vector of the (p, q) th sampling point on a normalized Doppler frequency interval of the non-normal side view array radar
Thus, the space-time steering vector at the (p, q) -th sampling point
Wherein f isd,pIndicating the normalized Doppler frequency, f, of the p-th sampling point in the normalized Doppler frequency interval of the non-frontal view array radars,qThe normalized spatial frequency of the q sampling point on the normalized spatial frequency interval of the non-front side view array radar is represented;represents kronecker product (.)TRepresenting a transpose; n represents the number of array elements contained in the antenna of the non-front side view array radar, and M represents the number of pulses transmitted by the non-front side view array radar in one coherent processing interval.
5. The method for optimizing the angular Doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 6,
calculating the initial power at the (p, q) th sampling point by using the following formula
Wherein, S (f)d,p,fs,q) Representing the space-time steering vector at the (p, q) -th sampling point, XlClutter data representing the ith training sample cell, (-)HRepresenting conjugate transposition, |, representing absolute value operation; f. ofd,pIndicating the normalized Doppler frequency, f, of the p-th sampling point in the normalized Doppler frequency interval of the non-frontal view array radars,qThe normalized spatial frequency of the q sampling point on the normalized spatial frequency interval of the non-front side view array radar is represented;
determining a clutter power spectrum P of clutter data of the ith training sample unit according to the initial power at each sampling point(0)，diag (-) denotes a diagonal matrix, i.e. the diagonal element isThe diagonal matrix of (a).
6. The method for optimizing the angular Doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 7,
according to the power at the (p, q) th sampling pointAnd the space-time steering vector at the (p, q) th sampling point constructs a covariance matrix at the (p, q) th sampling pointThe following were used:
wherein, S (f)d,p,fs,q) Representing the space-time steering vector at the (p, q) -th sampling point, fd,pIndicating the normalized Doppler frequency, f, of the p-th sampling point in the normalized Doppler frequency interval of the non-frontal view array radars,qAnd the normalized spatial frequency of the q sampling point on the normalized spatial frequency interval of the non-front side view array radar is represented.
7. The method for optimizing the angular Doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 8,
clutter data according to the ith training sample unit, a space-time steering vector at the (p, q) th sampling point and a covariance matrix at the (p, q) th sampling pointCalculating the power at the (p, q) th sampling point after the ith iterationThe following were used:
wherein, S (f)d,p,fs,q) Representing the space-time steering vector at the (p, q) -th sampling point, XlClutter data representing the ith training sample cell, (-)HRepresenting conjugate transpose, | representing absolute value operation, (-)-1Representing matrix inversion; f. ofd,pIndicating the normalized Doppler frequency, f, of the p-th sampling point in the normalized Doppler frequency interval of the non-frontal view array radars,qThe normalized spatial frequency of the q sampling point on the normalized spatial frequency interval of the non-front side view array radar is represented;
according to the (p, q) th sampling point after the ith iterationOf (2) isDetermining clutter power spectrum P after ith iteration of clutter data of ith training sample unit(i)The following were used:
wherein diag (-) denotes a diagonal matrix, i.e. the diagonal elements areThe diagonal matrix of (a).
8. The method for optimizing the angular doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 11:
determining a normalized Doppler frequency f corresponding to a clutter power spectrum of clutter data of a distance unit to be detectedd,kAnd normalized spatial frequency fs,k：
Wherein, the lambda is the working wavelength of the non-front side view array radar,the array element spacing of the uniform linear arrays is determined, the carrier where the non-positive side-view array radar is located flies along the x axis at a speed v, the plane where the uniform linear arrays are located is vertical to the x-y plane, and the plane where the uniform linear arrays are located and the x axis form an included angleθkIndicating a range to be measuredThe pitch angle corresponding to the clutter data from the unit,indicating the azimuth angle f corresponding to the clutter data of the distance unit to be detected relative to the uniform linear arrayrIs the pulse repetition frequency;
calculating the corrected Doppler frequency Deltaf corresponding to the power spectrum of the clutter data of the ith training sample unitd,l＝fd,l-fd,kAnd correcting the spatial frequency Δ fs,l＝fs,l-fs,k；
Wherein f isd,lNormalized Doppler frequency, f, corresponding to the power spectrum of the clutter data representing the ith training sample units,lNormalized spatial frequency, f, corresponding to the power spectrum of the clutter data representing the ith training sample unitd,kNormalized Doppler frequency, f, corresponding to a clutter power spectrum of clutter data representing a range cell to be detecteds,kAnd representing the normalized spatial frequency corresponding to the clutter power spectrum of the clutter data of the distance unit to be detected.
9. The method for optimizing the angular Doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 12,
calculating a power spectrum corresponding Doppler domain conversion matrix T of clutter data of the ith training sample unitd,lAnd spatial domain transformation matrix Ts,l：
Calculating to obtain a power spectrum corresponding correction matrix T of clutter data of the first training sample unitIAA-ADC：
Obtaining the clutter data of the first training sample unit after correction according to the clutter data of the first training sample unit and the power spectrum corresponding correction matrix of the clutter data of the first training sample unit
Wherein, XlRepresents clutter data,. DELTA.f, of the ith training sample unitd,lA modified Doppler frequency,. DELTA.f, corresponding to the power spectrum of the clutter data representing the ith training sample cells,lRepresents the modified spatial frequency corresponding to the power spectrum of the clutter data of the ith training sample unit,the kronecker product is expressed, N represents the number of array elements contained in the antenna of the non-front side view array radar, and M represents the number of pulses transmitted by the non-front side view array radar in one coherent processing interval.
10. The method for optimizing the angular doppler registration of the clutter spectrum of the non-forward looking array radar according to claim 1, wherein in step 13,
calculating to obtain a covariance matrix of clutter data of a distance unit to be detected according to the L-1 corrected clutter data
Wherein the content of the first and second substances,representing clutter data of the corrected I training sample unit;
obtaining space-time guiding vector S (f) of distance unit to be detectedd,k,fs,k) According to the space-time steering vector S (f) of the distance unit to be detectedd，k,fs，k) To be provided withAnd covariance matrix of clutter data of distance unit to be detectedCalculating a filter weight vector of a distance unit to be detected
Wherein f isd,kNormalized Doppler frequency, f, corresponding to a clutter power spectrum of clutter data representing a range cell to be detecteds,kA normalized spatial frequency corresponding to a clutter power spectrum of clutter data representing a range bin to be detected,is a normalization constant.
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