CN102183747B - Agile radar target detecting system and method - Google Patents

Abstract

The invention discloses an agile radar target detecting system. The system comprises a radar, a database and an upper computer; the radar, the database and the upper computer are connected sequentially; the radar irradiates a detected sea area and stores sea radar clutter data into the database; and the upper computer comprises a data preprocessing module, a sea clutter model modeling module, a target detecting module, a model updating module and a result displaying module. The invention also discloses an agile radar target detecting method. By the system and the method, the demand on samples is relatively small, the response is rapid, and the on-line detection can be achieved.

Description

A kind of quick Radar Targets'Detection system and method

Technical field

The present invention relates to the radar data process field, especially, relate to a kind of quick Radar Targets'Detection system and method.

Background technology

The sea clutter promptly comes from the radar backscattering echo on sea.In recent decades; Along with going deep into to extra large clutter understanding; Countries such as Germany, Norway attempt utilizing radar observation sea clutter to obtain radar wave image coming inverting wave information in succession, to obtain the real-time information about sea state, like wave height, direction and the cycle etc. of wave; Thereby further marine small objects is detected, this all has crucial meaning to marine activity.

The naval target detection technique has consequence, and it is one of vital task to extra large radar work that the accurate target judgement is provided.The radar automatic checkout system is made judgement according to decision rule under given detection threshold, and strong extra large clutter often becomes the main interference of weak target signal.How to handle extra large clutter and will directly have influence on the detectability of radar under marine environment: the 1) ice of navigation by recognition buoy, small pieces, swim in the greasy dirt on sea, these may bring potential crisis to navigation; 2) the monitoring illegal fishing is an important task of environmental monitoring.

When traditional target detection, extra large clutter is considered to disturb a kind of noise of navigation to be removed.Yet during to extra large observed object, faint moving target echo usually is buried in the extra large clutter at radar; Signal to noise ratio is lower; Radar is difficult for detecting target, and a large amount of spikes of extra large clutter also can cause serious false-alarm simultaneously, to the detection performance generation considerable influence of radar.As far as sea police's ring and early warning radar, the main target of research is to improve the detectability of target under the extra large clutter background for various.Therefore, not only have important significance for theories and practical significance, and be difficult point and focus that domestic and international naval target detects.

Summary of the invention

Often the desired data amount is big, response speed slow in order to overcome existing radar target detection method, can't realize the deficiency of online detection, and the present invention provides a kind of the less sample of need, can respond, realize the quick Radar Targets'Detection system and method for online detection fast.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of quick Radar Targets'Detection system; Comprise radar, database and host computer, radar, database and host computer link to each other successively, and said radar shines the detection marine site; And with Radar Sea clutter data storing to described database, described host computer comprises:

Data preprocessing module, in order to carry out the pre-service of Radar Sea clutter data, adopt following process to accomplish:

1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, wherein, i=1 ..., N;

2) training sample is carried out normalization and handles, obtain normalization amplitude

:

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ .\\ .\\ .\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

The forecasting model MBM, in order to set up forecasting model, adopt following process to accomplish:

With the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})(a_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier,

With

Be support vector, wherein, i=1 ..., M, j=1 ..., M,

And exp (‖ x-x _i‖/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

Module of target detection, in order to carry out target detection, adopt following process to accomplish:

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

2) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

3) the estimation function f (x) that treats that substitution forecasting model MBM obtains obtains the extra large clutter predicted value of sampling instant (t+1);

4) difference e of extra large clutter predicted value of calculating and radar return measured value, calculation control limit Q _α:

${\mathrm{\θ}}_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}$

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$

$h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

Wherein, α is a degree of confidence, θ ₁, θ ₂, θ ₃, h ₀Be intermediate variable, The i power of j eigenwert of expression covariance matrix, k is the sample dimension, C _αBe that the normal distribution degree of confidence is the statistics of α;

5) detect judgement: work as e ²Difference is greater than control limit Q _αThe time, there is target in this point, otherwise does not have target.

As preferred a kind of scheme: described host computer also comprises: the discrimination model update module; In order to sampling time interval image data by setting; Measured data that obtains and extra large clutter predicted value are compared; If relative error greater than 10%, then adds the training sample data with new data, upgrade forecasting model.

As preferred another kind of scheme: described host computer also comprises: display module as a result shows at host computer in order to the testing result with module of target detection.

The employed object detection method of a kind of quick Radar Targets'Detection system, described method may further comprise the steps:

(1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, wherein, i=1 ..., N;

(2) training sample is carried out normalization and handle, obtain normalization amplitude

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

(3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ .\\ .\\ .\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

(4) with the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})(a_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier, With

Be support vector, wherein, i=1 ..., M, j=1 ..., M,

And exp (‖ x-x _i‖/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

(5) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

(6) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

(7) the estimation function f (x) that treats that substitution step (4) obtains obtains the extra large clutter predicted value of sampling instant (t+1);

(8) difference e of extra large clutter predicted value of calculating and radar return measured value, calculation control limit Q _α:

${\mathrm{\θ}}_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}$

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$

$h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

Wherein, α is a degree of confidence, θ ₁, θ ₂, θ ₃, h ₀Be intermediate variable,

The i power of j eigenwert of expression covariance matrix, k is the sample dimension, C _αBe that the normal distribution degree of confidence is the statistics of α;

(9) detect judgement: work as e ²Difference is greater than control limit Q _αThe time, there is target in this point, otherwise does not have target.

As preferred a kind of scheme: described method also comprises:

(9), by the sampling time interval image data of setting, with the measured data that obtains and extra large clutter predicted value relatively, if relative error greater than 10%, then adds the training sample data with new data, the renewal forecasting model.

As preferred another kind of scheme: in described step (8), the testing result of module of target detection is shown at host computer.

Technical conceive of the present invention is: the chaotic characteristic that the present invention is directed to the Radar Sea clutter; Radar Sea clutter data are carried out reconstruct, and the data after the reconstruct are carried out nonlinear fitting, set up the forecasting model of Radar Sea clutter; Calculate predicted value and radar return measured value poor of Radar Sea clutter; Error when having target to exist can be significantly when not having target, introduce the small sample SVMs, thereby realize that the fast target under the extra large clutter background detects.

Beneficial effect of the present invention mainly shows: 1, set up Radar Sea clutter forecasting model, and can online detection naval target; 2, used detection method only needs that less sample gets final product, response speed is fast; 3, can quick and precisely detect small objects under the clutter background of going to sea.

Description of drawings

Fig. 1 is the hardware structure diagram of system proposed by the invention;

Fig. 2 is the functional block diagram of host computer proposed by the invention.

Embodiment

Below in conjunction with accompanying drawing the present invention is further described.The embodiment of the invention is used for the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change to the present invention makes all fall into protection scope of the present invention.

Embodiment 1

With reference to Fig. 1, Fig. 2; A kind of quick Radar Targets'Detection system; Comprise radar 1, database 2 and host computer 3, radar 1, database 2 and host computer 3 link to each other successively, and 1 pair of marine site of detecting of said radar is shone; And with Radar Sea clutter data storing to described database 2, described host computer 3 comprises:

Data preprocessing module 4, in order to carry out the pre-service of Radar Sea clutter data, adopt following process to accomplish:

1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, i=1 ..., N;

2) training sample is carried out normalization and handle, obtain normalization amplitude

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ .\\ .\\ .\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

Sea Clutter Model MBM 5, in order to set up forecasting model, adopt following process to accomplish:

With the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})(a_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier,

With

Be support vector, wherein, i=1 ..., M, j=1 ..., M,

And exp (‖ x-x _i‖/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

Module of target detection 6, in order to carry out target detection, adopt following process to accomplish:

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

2) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

3) the estimation function f (x) that treats that obtains of substitution sea Clutter Model MBM obtains the extra large clutter predicted value of sampling instant (t+1);

4) difference e of extra large clutter predicted value of calculating and radar return measured value, calculation control limit Q _α:

${\mathrm{\θ}}_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}$

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$

$h_0=1-\frac{{2\mathrm{\θ}}_1{\mathrm{\θ}}_3}{{3\mathrm{\θ}}_2^2}$

Wherein, α is a degree of confidence, θ ₁, θ ₂, θ ₃, h ₀Be intermediate variable,

The i power of j eigenwert of expression covariance matrix, k is the sample dimension, C _αBe that the normal distribution degree of confidence is the statistics of α;

5) detect judgement: work as e ²Difference is greater than control limit Q _αThe time, there is target in this point, otherwise does not have target.

Described host computer 3 also comprises: model modification module 8, by the sampling time interval of setting, image data; Measured data that obtains and extra large clutter predicted value are compared; If relative error greater than 10%, then adds the training sample data with new data, upgrade the estimation function of treating of MBM.

Said host computer 3 also comprises: display module 7 as a result, are used for the testing result that module of target detection obtains is shown at host computer.

The hardware components of said host computer 3 comprises: the I/O element is used for the collection of data and the transmission of information; Data-carrier store, data sample that storage running is required and operational factor etc.; Program storage, storage realizes the software program of functional module; Arithmetical unit, executive routine, the function of realization appointment; Display module shows the parameter and the testing result that are provided with.

Embodiment 2

With reference to Fig. 1, Fig. 2, a kind of quick radar target detection method, described method may further comprise the steps:

(1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, i=1 ..., N;

(2) training sample is carried out normalization and handle, obtain normalization amplitude

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

(3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ .\\ .\\ .\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

(4) with the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})(a_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier,

With

Be support vector, wherein, i=1 ..., M, j=1 ..., M,

And exp (‖ x-x _i‖/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

(5) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

(6) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

(7) the estimation function f (x) that treats that substitution step (4) obtains obtains the extra large clutter predicted value of sampling instant (t+1);

(8) calculate the difference of extra large clutter predicted value and radar return measured value, detect judgement: when difference during greater than the limits of error of appointment, there is target in this point, otherwise does not have target.

Described method also comprises: (9), by the sampling time interval of setting; Image data compares measured data that obtains and extra large clutter predicted value, if relative error is greater than 10%; Then new data is added the training sample data, upgrade the estimation function of treating of MBM.

Described method also comprises: the testing result that in described step (8), module of target detection is obtained shows at host computer.

Claims (6)

1. quick Radar Targets'Detection system; Comprise radar, database and host computer, radar, database and host computer link to each other successively, it is characterized in that: said radar shines the detection marine site; And with Radar Sea clutter data storing to described database, described host computer comprises:

Data preprocessing module, in order to carry out the pre-service of Radar Sea clutter data, adopt following process to accomplish:

1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, wherein, i=1 ..., N;

2) training sample is carried out normalization and handle, obtain normalization amplitude

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ .\\ .\\ .\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

The forecasting model MBM, in order to set up forecasting model, adopt following process to accomplish:

With the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})({\mathrm{\α}}_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier,

With

Be support vector, wherein, i=1 ..., M, j=1 ..., M,

And exp (|| x-x _i||/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

Module of target detection, in order to carry out target detection, adopt following process to accomplish:

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

2) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

3) the estimation function f (x) that treats that substitution forecasting model MBM obtains obtains the extra large clutter predicted value of sampling instant (t+1);

4) difference e of extra large clutter predicted value of calculating and radar return measured value, calculation control limit Q _α:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}$

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$

$h_0=1-\frac{2{\mathrm{\θ}}_1{\mathrm{\θ}}_3}{3{\mathrm{\θ}}_2^2}$

Wherein, α is a degree of confidence, θ ₁, θ ₂, θ ₃, h ₀Be intermediate variable, λ _j ⁱThe i power of j eigenwert of expression covariance matrix, k is the sample dimension, C _αBe that the normal distribution degree of confidence is the statistics of α;

5) detect judgement: work as e ²Difference is greater than control limit Q _αThe time, there is target in this point, otherwise does not have target.

2. quick Radar Targets'Detection as claimed in claim 1 system; It is characterized in that: described host computer also comprises: the discrimination model update module; In order to by the sampling time interval image data of setting, measured data that obtains and extra large clutter predicted value are compared, if relative error is greater than 10%; Then new data is added the training sample data, upgrade forecasting model.

3. according to claim 1 or claim 2 quick Radar Targets'Detection system, it is characterized in that: described host computer also comprises: display module as a result shows at host computer in order to the testing result with module of target detection.

4. employed object detection method of quick Radar Targets'Detection system as claimed in claim 1, it is characterized in that: described method may further comprise the steps:

(1) from database, gathers N Radar Sea clutter echoed signal amplitude x _iAs training sample, wherein, i=1 ..., N;

(2) training sample is carried out normalization and handle, obtain normalization amplitude

${\stackrel{\‾}{x}}_i=\frac{x_i-\min x}{\max x-\min x}$

Wherein, minx representes the minimum value in the training sample, and maxx representes the maximal value in the training sample;

(3), obtain input matrix X and corresponding output matrix Y respectively with the training sample reconstruct after the normalization:

$Y=\left[\begin{array}{c}{\stackrel{\‾}{x}}_{D+1}\\ {\stackrel{\‾}{x}}_{D+2}\\ \·\\ \·\\ \·\\ {\stackrel{\‾}{x}}_N\end{array}\right]$

Wherein, D representes the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70;

(4) with the X that obtains, the following quadratic programming problem of Y substitution:

$\underset{\mathrm{\α},{\mathrm{\α}}^{*}}{\max }\{-\frac{1}{2}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}\underset{j=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})({\mathrm{\α}}_j-{\mathrm{\α}}_j^{*})\exp (-||{\stackrel{\‾}{x}}_i-{\stackrel{\‾}{x}}_j||/{\mathrm{\θ}}^2)-\mathrm{\ϵ}\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i+{\mathrm{\α}}_i^{*})+\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{y}_i({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})\}$

$s.t.\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i-{\mathrm{\α}}_i^{*})=0$

0≤α _i≤γ

$0\≤{\mathrm{\α}}_i^{*}\≤\mathrm{\γ}$

Find the solution to such an extent that treat estimation function f (x):

$f\left(x\right)=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}({\mathrm{\α}}_i^{*}-{\mathrm{\α}}_i)\exp (-||x-x_i||/{\mathrm{\θ}}^2)$

Wherein, M is the number of support vector, α _iAnd α _jBe Lagrange multiplier,

With

Be support vector,

Wherein, i=1 ..., M, j=1 ..., M,

And exp (|| x-x _i||/θ ²) be the kernel function of SVMs, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and ε is insensitive coefficient, and x representes input variable, y _iBe i the component of Y, γ is a penalty coefficient;

(5) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1..., x _t], x _T-D+1The extra large clutter echoed signal amplitude of representing the t-D+1 sampling instant, x _tThe extra large clutter echoed signal amplitude of representing the t sampling instant;

(6) carrying out normalization handles;

$\stackrel{\‾}{\mathrm{TX}}=\frac{\mathrm{TX}-\min x}{\max x-\min x}$

(7) the estimation function f (x) that treats that substitution step (4) obtains obtains the extra large clutter predicted value of sampling instant (t+1);

(8) difference e of extra large clutter predicted value of calculating and radar return measured value, calculation control limit Q _α:

$Q_{\mathrm{\α}}={\mathrm{\θ}}_1{[\frac{C_{\mathrm{\α}}{h}_0\sqrt{{2\mathrm{\θ}}_2}}{{\mathrm{\θ}}_1}+1+\frac{{\mathrm{\θ}}_2{h}_0(h_0-1)}{{\mathrm{\θ}}_2}]}^{\frac{1}{h_0}}$

${\mathrm{\θ}}_i=\underset{j=k+1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\λ}}_j^i,i=\mathrm{1,2,3}$

$h_0=1-\frac{2{\mathrm{\θ}}_1{\mathrm{\θ}}_3}{3{\mathrm{\θ}}_2^2}$

Wherein, α is a degree of confidence, θ ₁, θ ₂, θ ₃, h ₀Be intermediate variable, λ _j ⁱThe i power of j eigenwert of expression covariance matrix, k is the sample dimension, C _αBe that the normal distribution degree of confidence is the statistics of α;

(9) detect judgement: work as e ²Difference is greater than control limit Q _αThe time, there is target in this point, otherwise does not have target.

5. object detection method as claimed in claim 4 is characterized in that: described method also comprises:

(10), by the sampling time interval image data of setting, with the measured data that obtains and extra large clutter predicted value relatively, if relative error greater than 10%, then adds the training sample data with new data, the renewal forecasting model.

6. like claim 4 or 5 described object detection methods, it is characterized in that: in described step (9), the testing result of module of target detection is shown at host computer.

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