CN103913724A - Clutter rejection method based on prior terrain coverage data - Google Patents

Abstract

The invention belongs to the technical field of radar clutter rejection, and particularly relates to a clutter rejection method based on prior terrain coverage data. The clutter rejection method based on the prior terrain coverage data comprises the following steps that echo data are acquired through an aircraft radar, and L original training samples are acquired; Lout1 original training samples with the maximum target matching coefficient are removed, and training samples obtained after primary removing are obtained; training samples obtained after secondary removing are obtained from the training samples obtained after primary removing according to the similarity between the covariance matrix of each training sample and R0, the self-adaptation weight is worked out according to the expectation of the covariance matrix of the training samples obtained after secondary removing, and the filtering is carried out on echo data of distance doors to be processed according to the self-adaptation weight.

Description

Based on the clutter suppression method of priori landform cover data

Technical field

The invention belongs to radar clutter and suppress technical field, the particularly clutter suppression method based on priori landform cover data, for carrying out clutter inhibition at non-homogeneous clutter environment to the echo data of radar.

Background technology

In modern Airborne Pulse Doppler Radar, space-time two-dimensional Adaptive Signal Processing technology can become possibility by prompt phased array antenna and digital signal samples, the treatment technology becoming because of extensive choice for use wave beam.This technology can be by utilizing clutter statistical characteristics to produce the oblique recess mating with clutter, effectively improve the detection performance of airborne radar to moving-target, both can be applied in airborne radar, and also can be applied in battlefield perception radar and airborne fire control radar and detect ground at a slow speed when target.

Because clutter statistical characteristics is with the non-homogeneous clutter environment of change of distance, airborne radar is difficult to obtain independent identically distributed clutter sample data.The clutter that heterogeneity shows as different distance ring has different clutter spectrum, and one of its reason is the difference of the reflection characteristic of ground scatter body.Another important aspect is the interference of some the isolated clutters including echo signal, and these clutters just appear at other apart from range gate, and the disturbance that weight vector is calculated is clearly.Space-time adaptive processing need to be estimated with independent identically distributed training sample the statistical property of the clutter plus noise of pending range gate.In the time that clutter plus noise sample is non-homogeneous, the statistical property of training sample can not meet independent same distribution.This will cause the covariance matrix of being estimated by training sample different with the statistical property of the background clutter plus noise of pending range gate, make effectively Background suppression clutter plus noise of space-time adaptive processing, even can make the power of target decline, cause radar detedtion probability not high.

For the heterogeneity of above-mentioned clutter, develop a lot of methods and avoided or weaken.Such as hypothesis clutter data are the staging treating of local uniform, sliding window method, sliding hole method, recursive algorithm etc. in little distance range.Also usefully select the sample architecture covariance matrix that clutter power is larger and form weight vector, can form darker depression along two-dimentional clutter spectrum like this, the adaptive faculty of enhancing system to clutter.And for the situation that comprises echo signal and some isolated clutters in clutter, conventional non-homogeneous detection method has broad sense inner product (GIP) method and adaptive power residue (APR) method.Broad sense inner product approach can only be picked out the Uniform Sample in training sample, in the time that the background clutter plus noise of pending range gate and the clutter plus noise of most of training samples are not independent same distribution, the training sample of selecting by broad sense inner product can not be estimated the statistical property of the background of pending range gate, causes the handling property of space-time adaptive processing to decline.Adaptive power residual basis is more rough, can not choose very accurately moving-target, has false dismissal and false-alarm, need to repeat to select, and calculated amount is large.These the two kinds methods of selecting training sample are only utilized original training sample statistical property, and do not use the data characteristic of pending range gate, select Uniform Sample is not immediate with the statistical property of the background clutter plus noise of pending range gate, so can not estimate well the Background statistic characteristic of pending range gate, cause the clutter residue of space-time adaptive processing larger, this can cause false-alarm on the one hand, can raise on the other hand constant false alarm rate detection threshold, reduces detection probability.

Summary of the invention

The object of the invention is to propose the clutter suppression method based on priori landform cover data.The present invention can carry out clutter inhibition to the echo data of radar in non-homogeneous clutter environment.

For realizing above-mentioned technical purpose, the present invention adopts following technical scheme to be achieved.

Clutter suppression method based on priori landform cover data comprises the following steps:

S1: utilize airborne radar to obtain echo data, choose the echo data of L range gate in radar return data, L is greater than 1 natural number; Using the echo data of L range gate as a corresponding L original training sample; Draw the object matching coefficient of each original training sample, in L original training sample, by the L of object matching coefficient maximum _out1individual original training sample is rejected, and obtains once rejecting rear training sample; L _out1for the once rejecting number of setting;

S2: in training sample, draw the covariance matrix of each training sample after once rejecting; Draw the covariance matrix R of the echo data of pending range gate ₀, draw covariance matrix and the R of each training sample ₀similarity, the covariance matrix of g training sample and R ₀similarity be D _g, g gets 1 to L1, L1=L-L _out1; At D ₁to D _l1in choose maximum L _out2individual numerical value, after once rejecting in training sample, by described L _out2the L that individual numerical value is corresponding _out2individual training sample is rejected, and obtains secondary and rejects rear training sample; L _out2for the secondary of setting is rejected number;

S3: the expectation that draws the covariance matrix of the rear training sample of secondary rejecting

$\hat{R}=\frac{1}{L2}\underset{h=1}{\overset{L2}{\mathrm{\Σ}}}{x}_h{x}_h^H$

Wherein, L2=L-L _out1-L _out2, x _hrepresent the echo data of h training sample in the rear training sample of secondary rejecting, the conjugate transpose of H representing matrix;

Then basis calculate adaptive weight wherein, μ is normalization coefficient, s _tsteering vector during for target empty; Then according to adaptive weight w _optthe echo data of pending range gate is carried out to filtering, and the result of exporting after filtering is: the clutter of the echo data of pending range gate suppresses result.

Feature of the present invention and further improvement are:

In step S1, the range gate that each original training sample is corresponding and pending range gate differ and are no more than A range gate, and A is the natural number of setting.

In step S1, in L original training sample, the object matching coefficient p of l original training sample _lfor:

$p_l=\left|\frac{s_t^H{x}_l}{\sqrt{x_l^H{x}_l{s}_t^H{s}_t}}\right|$

Wherein, l gets 1 to L, x _lit is the echo data of l original training sample; The conjugate transpose of H representing matrix.

In step S2, after once rejecting in training sample, the covariance matrix R of g training sample _gfor

$R_g=\underset{g\left(i\right)=1}{\overset{N_g}{\mathrm{\Σ}}}{\mathrm{\ρ}}_{g\left(i\right)}{\left({\mathrm{\ρ}}_{g\left(i\right)}\right)}^H(s_{a,g\left(i\right)}\⊗s_{b,g\left(i\right)}){(s_{a,g\left(i\right)}\⊗s_{b,g\left(i\right)})}^H$

Wherein, ρ _g(i) the echo data amplitude of i scattering point of g training sample in rear training sample, s are once rejected in expression _{a, g (i)}represent the spatial domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time, s _{b, g (i)}represent the time domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time; N _gfor once rejecting in rear training sample the number of the scattering point that g training sample is corresponding; the direct product of representing matrix;

Draw the covariance matrix R of the echo data of pending range gate ₀:

$R_0=\underset{i=1}{\overset{N_0}{\mathrm{\Σ}}}{\mathrm{\ρ}}_i{\left({\mathrm{\ρ}}_i\right)}^H(s_{a,i}\⊗s_{b,i}){(s_{a,i}\⊗s_{b,i})}^H$

Wherein, ρ _irepresent the echo data amplitude of pending range gate, s _a,irepresent in the echo data of pending range gate the spatial domain steering vector of the target that i scattering point is corresponding, s _b,irepresent in the echo data of pending range gate the time domain steering vector of the target that i scattering point is corresponding;

Then determine covariance matrix and the R of g training sample according to following formula ₀similarity D _g:

$D_g={\left|\right|R_0^{-1}{R}_g-I\left|\right|}_F$

Wherein, I be with with the unit matrix of dimension, ‖ ‖ _fthe quadratic sum of all elements in representing matrix.

In step S3, drawing adaptive weight w _optafterwards, show that according to following formula the clutter of the echo data of pending range gate suppresses result wherein, x _l0represent the echo data of pending range gate.

Beneficial effect of the present invention is: the present invention in the time selecting training sample first according to the matching degree of training sample and goal orientation vector, reject likely by the training sample of target stains, the problem that so just can avoid space-time adaptive to process time, target disappears mutually, keep target output constant, then reject the training sample the poorest with pending range gate similarity according to the covariance matrix similarity of training sample and pending range gate, thereby pick out the not the most similar sample of pending range gate of following containing target and make training sample, estimate that by the method obtaining adaptive weight can send out and adaptive power residual basis clutter reduction better than broad sense inner product, improve detection probability.In the time that the statistical property of pending range gate is different from most training sample, the present invention can pick out and the immediate training sample of pending range gate statistical property from training sample, thereby can suppress well the clutter of this pending range gate, reduce false-alarm.

Accompanying drawing explanation

Fig. 1 is the geometric relationship schematic diagram of even linear array radar and target;

Fig. 2 is the process flow diagram of the clutter suppression method based on priori landform cover data of the present invention;

Fig. 3 is the distance-Doppler image of the single passage of airborne radar in emulation experiment one;

Fig. 4 is the range Doppler image of the echo data of the single spatial domain passage that obtains according to landform cover data and clutter model simulation in emulation experiment one;

Fig. 5 is that the space-time adaptive of choosing sample based on broad sense Law of Inner Product in emulation experiment one is processed filtered distance-Doppler image;

Fig. 6 is that in emulation experiment one, the present invention processes filtered distance-Doppler image through space-time adaptive;

Fig. 7 is the mean value contrast schematic diagram of the filtering output power that in emulation experiment one, two kinds of methods obtain;

Fig. 8 is that the space-time adaptive of choosing sample based on broad sense Law of Inner Product in emulation experiment two is processed filtered distance-Doppler image;

Fig. 9 is that in emulation experiment two, the present invention processes filtered distance-Doppler image through space-time adaptive;

Figure 10 is the mean value contrast schematic diagram of the filtering output power that in emulation experiment two, several method obtains.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described:

The echo data model of paper airborne radar.The receiving array of airborne radar is even linear array, and the geometric relationship of even linear array radar and target as shown in Figure 1.Carrier aircraft is parallel to ground flying with speed v along X-axis positive dirction, and the angle of radar front and carrier aircraft speed is α, target with respect to position angle, the angle of pitch, cone angle and the air line distance of radar be respectively θ, φ and R _l.In Fig. 1, describe as an example of any range gate example, in this range gate, target echo data s can be expressed as:

s=ρ ₀s _t

Wherein, ρ ₀represent the amplitude of target data, steering vector while representing target empty, represent operation of direct product symbol (element of the same position of two matrixes is multiplied each other), s _a0and s _b0the spatial domain steering vector and the time domain steering vector that represent respectively target, can be expressed as:

$s_{a0}=\frac{1}{\sqrt{N}}{[1,e^{{j2\mathrm{\πu}}_t},...,e^{{j2\mathrm{\π}(N-1)u}_t}]}^T$

$s_{b0}=\frac{1}{\sqrt{K}}{[1,e^{{j2\mathrm{\πv}}_t},...,e^{{j2\mathrm{\π}(K-1)v}_t}]}^T$

Wherein, the array number of the receiving array that N is airborne radar, K is relevant umber of pulse of processing in interval of airborne radar, u _tfor normalization spatial frequency, the array element distance of the receiving array that d is airborne radar, λ is the airborne radar wavelength that transmits, ν _tfor normalization Doppler frequency, f _rthe pulse repetition rate transmitting for airborne radar.

The clutter echoed signal of this range gate can be expressed as:

$c=\underset{q=1}{\overset{N_c}{\mathrm{\Σ}}}{\mathrm{\ρ}}_q{s}_{\mathrm{aq}}\⊗s_{\mathrm{bq}}$

In formula, N _cfor the number of current distance door clutter scattering point, s _aqfor the spatial domain steering vector of q clutter scattering point of the current distance family status, s _bqfor the time domain steering vector of q clutter scattering point of the current distance family status, ρ _qfor the echo data amplitude of q clutter scattering point of the current distance family status.ρ _qrelevant with factors such as scattering coefficient, clutter scattering point area, radar emission power, radar emission directional diagram and the receiving patterns of clutter scattering point.

According to whether containing echo signal, echo has two kinds of hypothesis forms, and under a kind of hypothesis, echo data comprises clutter information and noise therein, and under another kind hypothesis, echo data comprises clutter, noise and echo signal.

With reference to Fig. 2, it is the process flow diagram of the clutter suppression method based on priori landform cover data of the present invention.Should comprise the following steps by the clutter suppression method based on priori landform cover data:

Near S1: utilize airborne radar to obtain echo data, select L range gate pending range gate, L is greater than 1 natural number.In this L range gate, the range gate that each original training sample is corresponding and pending range gate differ and are no more than A range gate, and A is the natural number of setting.Using the echo data of an above-mentioned L range gate as a corresponding L original training sample.

An above-mentioned L original training sample is likely by target stains, at this moment, can cause target to disappear mutually with required adaptive weight in the time that space-time adaptive is processed, and makes the output power of target be less than real power, reduces the detection probability of radar.So first we will reject by the original training sample of target stains.In the embodiment of the present invention, during according to original training sample and target empty, the matching degree of steering vector is rejected by the training sample of target stains.

Draw the object matching coefficient of each original training sample, in L original training sample, the object matching coefficient p of l original training sample _lfor:

$p_l=\left|\frac{s_t^H{x}_l}{\sqrt{x_l^H{x}_l{s}_t^H{s}_t}}\right|$

Wherein, l gets 1 to L, x _lit is the echo data of l original training sample; The conjugate transpose of H representing matrix.

In L original training sample, by the L of object matching coefficient maximum _out1individual original training sample is rejected, and (now, the number of training sample is L1=L-L to obtain once rejecting rear training sample _out1); L _out1for the once rejecting number of setting; L _out1<L.

S2: in training sample, draw the covariance matrix of each training sample after once rejecting; Draw the covariance matrix R of the echo data of pending range gate ₀, draw covariance matrix and the R of each training sample ₀similarity, the covariance matrix of g training sample and R ₀similarity be D _g, g gets 1 to L1; At D ₁to D _l1in choose maximum L _out2individual numerical value, after once rejecting in training sample, by described L _out2the L that individual numerical value is corresponding _out2individual training sample is rejected, and obtains secondary and rejects rear training sample; L _out2for the secondary of setting is rejected number; L _out2<L1.Be described as follows:

After once rejecting in training sample, the covariance matrix R of g training sample _gfor

$R_g=\underset{g\left(i\right)=1}{\overset{N_g}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}_{g\left(i\right)}{\left({\mathrm{\&rho;}}_{g\left(i\right)}\right)}^H(s_{a,g\left(i\right)}\&CircleTimes;s_{b,g\left(i\right)}){(s_{a,g\left(i\right)}\&CircleTimes;s_{b,g\left(i\right)})}^H$

Wherein, ρ _g(i) the echo data amplitude of i scattering point of g training sample in rear training sample, ρ are once rejected in expression _g(i) drawn by airborne radar backscattering coefficient model, airborne radar clutter model and landform cover data.S _{a, g (i)}represent the spatial domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time, s _{b, g (i)}represent the time domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time; N _gfor once rejecting in rear training sample the number of the scattering point that g training sample is corresponding; the direct product of representing matrix.

Draw the covariance matrix R of the echo data of pending range gate ₀:

$R_0=\underset{i=1}{\overset{N_0}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}_i{\left({\mathrm{\&rho;}}_i\right)}^H(s_{a,i}\&CircleTimes;s_{b,i}){(s_{a,i}\&CircleTimes;s_{b,i})}^H$

Wherein, ρ _irepresent the echo data amplitude of pending range gate, s _a,irepresent in the echo data of pending range gate the spatial domain steering vector of the target that i scattering point is corresponding, s _b,irepresent in the echo data of pending range gate the time domain steering vector of the target that i scattering point is corresponding;

Then determine covariance matrix and the R of g training sample according to following formula ₀similarity D _g:

$D_g={\left|\right|R_0^{-1}{R}_g-I\left|\right|}_F$

Wherein, I be with with the unit matrix of dimension, ‖ ‖ _fthe quadratic sum of all elements in representing matrix.

S3: the expectation that draws the covariance matrix of the rear training sample of secondary rejecting

$\hat{R}=\frac{1}{L2}\underset{h=1}{\overset{L2}{\mathrm{\&Sigma;}}}{x}_h{x}_h^H$

Wherein, L2=L-L _out1-L _out2, x _hrepresent the echo data of h training sample in the rear training sample of secondary rejecting, the conjugate transpose of H representing matrix;

Then basis calculate adaptive weight wherein, μ is normalization coefficient, and for example μ equals 1, s _tsteering vector during for target empty; Then according to adaptive weight w _optthe echo data of pending range gate is carried out to filtering, and the result of exporting after filtering is: the clutter of the echo data of pending range gate suppresses result.Particularly, drawing adaptive weight w _optafterwards, show that according to following formula the clutter of the echo data of pending range gate suppresses result y _l0, wherein, x _l0represent the echo data of pending range gate.

Advantage of the present invention can further be verified by following emulation experiment:

Emulation experiment one:

1) experiment parameter and experiment condition:

This experiment is verified according to certain measured data, the receiving array of airborne radar is positive side battle array, and front antenna adopts 2 × 11 linear array, is separated with 128 pulses between each coherent processing, useful range gate number is 500, peak transmitted power is 1.5kW, and fire pulse width (before pulse pressure) is 50.4 μ s, instant bandwidth 800kHz, the pulse repetition rate that airborne radar transmits is 1984Hz, array element interval d is 0.109m, and radar carrier frequency is 1.24GHz, and range resolution is 120m.

2) experiment content and interpretation of result

With reference to Fig. 3, it is the distance-Doppler image of the single passage of airborne radar in emulation experiment one.With reference to Fig. 4, it is the range Doppler image of the echo data of the single spatial domain passage that obtains according to landform cover data and clutter model simulation in emulation experiment one.In Fig. 3 and Fig. 4, the energy of gray-scale value representative data, gray-scale value is larger, and energy is higher.For the performance of checking this method, select the space-time adaptive processing test as a comparison of training sample herein by classical broad sense inner product.In emulation experiment one, target is added in No. 250 apart from No. 100 Doppler's passages of the family status.With reference to Fig. 5, for the space-time adaptive of choosing sample based on broad sense Law of Inner Product in emulation experiment one is processed filtered distance-Doppler image.Be that in emulation experiment one, the present invention processes filtered distance-Doppler image through space-time adaptive with reference to Fig. 6.In Fig. 5 and Fig. 6, the energy of gray-scale value representative data, gray-scale value is larger, and energy is higher.Can significantly find out that by comparison diagram 5 and Fig. 6 conventional art broad sense inner product approach clutter reduction residue is more, and relatively little many of clutter reduction of the present invention residue, the latter's effect is more a lot of than the former, can see there is a target at No. 250 range gate place of the 100th Doppler's passage.In order further to contrast the superior of the inventive method and prior art, Fig. 7 provides explanation relatively.With reference to Fig. 7, the mean value contrast schematic diagram of the filtering output power obtaining for two kinds of methods in emulation experiment one.The mean value of filtering output power of the present invention (7.007dB) is than the low 4.002dB of mean value (11.09dB) that selects the filtering output power of training sample method based on broad sense inner product as seen from Figure 7, effective inhibition of background clutter plus noise is conducive to the detection of target, the detection probability that improves target, illustrates that the present invention can obtain better clutter rejection than traditional broad sense inner product approach.We it can also be seen that, for discrete strong clutter point, the inventive method can be good at suppressing, and this can reduce false-alarm probability.

Emulation experiment two:

1) experiment parameter and experiment condition:

In emulation experiment two, radar system parameter is identical with emulation experiment one, just changes target component.Target is added in to No. 100 Doppler unit of No. 150 range gate.With reference to Fig. 8, for the space-time adaptive of choosing sample based on broad sense Law of Inner Product in emulation experiment two is processed filtered distance-Doppler image.Be that in emulation experiment two, the present invention processes filtered distance-Doppler image through space-time adaptive with reference to Fig. 9.In Fig. 8 and Fig. 9, the energy of gray-scale value representative data, gray-scale value is larger, and energy is higher.From Fig. 8 and and Fig. 9 can draw the conclusion identical with emulation experiment one.With reference to Figure 10, the mean value contrast schematic diagram of the filtering output power obtaining for several method in emulation experiment two.The mean value (6.24dB) of the filtering output power of the radar training sample selection based on priori landform cover data and covariance matrix method is than the low 3.769dB of filtering output power mean value (9.009dB) that selects training sample method based on broad sense inner product as seen from Figure 10.

By above-mentioned experiment and analysis, we may safely draw the conclusion: in the time that clutter plus noise is non-homogeneous, choose the self-adaptation power clutter reduction effect that training sample obtains better than classic method by the inventive method.

Obviously, those skilled in the art can carry out various changes and modification and not depart from the spirit and scope of the present invention the present invention.Like this, if within of the present invention these are revised and modification belongs to the scope of the claims in the present invention and equivalent technologies thereof, the present invention is also intended to comprise these changes and modification interior.

Claims (5)

1. the clutter suppression method based on priori landform cover data, is characterized in that, comprises the following steps:

S1: utilize airborne radar to obtain echo data, choose the echo data of L range gate in radar return data, L is greater than 1 natural number; Using the echo data of L range gate as a corresponding L original training sample; Draw the object matching coefficient of each original training sample, in L original training sample, by the L of object matching coefficient maximum _out1individual original training sample is rejected, and obtains once rejecting rear training sample; L _out1for the once rejecting number of setting;

S2: in training sample, draw the covariance matrix of each training sample after once rejecting; Draw the covariance matrix R of the echo data of pending range gate ₀, draw covariance matrix and the R of each training sample ₀similarity, the covariance matrix of g training sample and R ₀similarity be D _g, g gets 1 to L1, L1=L-L _out1; At D ₁to D _l1in choose maximum L _out2individual numerical value, after once rejecting in training sample, by described L _out2the L that individual numerical value is corresponding _out2individual training sample is rejected, and obtains secondary and rejects rear training sample; L _out2for the secondary of setting is rejected number;

S3: the expectation that draws the covariance matrix of the rear training sample of secondary rejecting

$\hat{R}=\frac{1}{L2}\underset{h=1}{\overset{L2}{\mathrm{\&Sigma;}}}{x}_h{x}_h^H$

Wherein, L2=L-L _out1-L _out2, x _hrepresent the echo data of h training sample in the rear training sample of secondary rejecting, the conjugate transpose of H representing matrix;

Then basis calculate adaptive weight wherein, μ is normalization coefficient, s _tsteering vector during for target empty; Then according to adaptive weight w _optthe echo data of pending range gate is carried out to filtering, and the result of exporting after filtering is: the clutter of the echo data of pending range gate suppresses result.

2. the clutter suppression method based on priori landform cover data as claimed in claim 1, is characterized in that, in step S1, the range gate that each original training sample is corresponding and pending range gate differ and be no more than A range gate, and A is the natural number of setting.

3. the clutter suppression method based on priori landform cover data as claimed in claim 1, is characterized in that, in step S1, and in L original training sample, the object matching coefficient p of l original training sample _lfor:

$p_l=\left|\frac{s_t^H{x}_l}{\sqrt{x_l^H{x}_l{s}_t^H{s}_t}}\right|$

Wherein, l gets 1 to L, x _lit is the echo data of l original training sample; The conjugate transpose of H representing matrix.

4. the clutter suppression method based on priori landform cover data as claimed in claim 1, is characterized in that, in step S2, and after once rejecting in training sample, the covariance matrix R of g training sample _gfor

$R_g=\underset{g\left(i\right)=1}{\overset{N_g}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}_{g\left(i\right)}{\left({\mathrm{\&rho;}}_{g\left(i\right)}\right)}^H(s_{a,g\left(i\right)}\&CircleTimes;s_{b,g\left(i\right)}){(s_{a,g\left(i\right)}\&CircleTimes;s_{b,g\left(i\right)})}^H$

Wherein, ρ _g(i) the echo data amplitude of i scattering point of g training sample in rear training sample, s are once rejected in expression _{a, g (i)}represent the spatial domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time, s _{b, g (i)}represent the time domain steering vector of the target corresponding with i scattering point of g training sample in training sample after rejecting for the first time; N _gfor once rejecting in rear training sample the number of the scattering point that g training sample is corresponding; the direct product of representing matrix;

Draw the covariance matrix R of the echo data of pending range gate ₀:

$R_0=\underset{i=1}{\overset{N_0}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}_i{\left({\mathrm{\&rho;}}_i\right)}^H(s_{a,i}\&CircleTimes;s_{b,i}){(s_{a,i}\&CircleTimes;s_{b,i})}^H$

Wherein, ρ _irepresent the echo data amplitude of pending range gate, s _a,irepresent in the echo data of pending range gate the spatial domain steering vector of the target that i scattering point is corresponding, s _b,irepresent in the echo data of pending range gate the time domain steering vector of the target that i scattering point is corresponding;

Then determine covariance matrix and the R of g training sample according to following formula ₀similarity D _g:

$D_g={\left|\right|R_0^{-1}{R}_g-I\left|\right|}_F$

Wherein, I be with with the unit matrix of dimension, ‖ ‖ _fthe quadratic sum of all elements in representing matrix.

5. the clutter suppression method based on priori landform cover data as claimed in claim 1, is characterized in that, in step S3, is drawing adaptive weight w _optafterwards, show that according to following formula the clutter of the echo data of pending range gate suppresses result wherein, x _l0represent the echo data of pending range gate.