CN102183754A - System and method for detecting sea target by using robust intelligent radar - Google Patents

Abstract

A robust intelligent radar sea target detection system, including a radar, a database, and a host computer; the radar, database, and host computer are connected in sequence, and the radar irradiates the detected sea area, and stores radar sea clutter data in the database, the host computer includes a data preprocessing module, a robust forecasting model modeling module, an improved intelligent optimization module, a target detection module, a model update module and a result display module. And a robust intelligent radar maritime target detection method is proposed. The invention provides an intelligent radar sea target detection system and method with robustness, avoiding the influence of human factors, and high intelligence.

Description

A kind of robust intelligence radar naval target detection system and method

Technical field

The present invention relates to the radar data process field, especially, relate to a kind of robust intelligence radar naval target 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 real-time information about sea state, as wave height, direction and the cycle etc. of wave, thereby further marine small objects is detected, this 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.For 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

In order to overcome the shortcoming that existing radar method for detecting targets at sea robustness is not high, be subject to human factor influence, intelligent deficiency, the invention provides and a kind ofly have robustness, avoid human factor influence, intelligent high intelligent radar naval target detection system and method.

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

A kind of robust intelligence radar naval target detection system, comprise radar, database and host computer, radar, database and host computer link to each other successively, and described 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 finish:

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

2) training sample is carried out normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

Wherein, D represents the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70; Robust forecasting model MBM, in order to set up forecasting model, adopt following process to finish: with X, the following linear equation of Y substitution that obtains:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein

Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T,

K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix, Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias,

And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

Improve intelligent optimizing module, the nuclear parameter θ and the penalty coefficient γ of forecasting model be optimized, adopt following process to finish in order to adopt evolution genetic algorithm:

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

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

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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) carry out normalized;

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

3) the estimation function f (x) that treats that substitution forecasting model MBM obtains calculates 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:

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

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

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

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.

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, the measured data and the model prediction value that obtain are compared, if relative error is greater than 10%, then new data is added the training sample 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 radar method for detecting targets at sea of a kind of robust intelligence radar naval target detection system, 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 normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

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

(4) with the X, the following linear equation of Y substitution that obtain:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein

Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T, K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix,

Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias,

And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

(5) be optimized with the nuclear parameter θ and the penalty coefficient γ of evolution genetic algorithm, adopt following process to finish step (4):

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

(6) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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;

(7) carry out normalized;

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

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

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

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

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

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

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;

(10) 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:

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

As preferred another kind of scheme: in described step (10), 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 reconstructed, and the data after the reconstruct are carried out nonlinear fitting, set up the forecasting model of Radar Sea clutter, calculate the poor of the predicted value of Radar Sea clutter and measured value, introduce the robust intelligent optimization method, the error when having target to exist can be significantly when not having target, introduce the robust intelligent optimization method, thereby realize the robust intelligence target detection under the extra large clutter background.

Beneficial effect of the present invention mainly shows: 1, can online detection naval target; 2, used detection method only needs less sample; 3, strong robustness, intelligent, height have been avoided artificial factor.

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 robust intelligence radar naval target detection system, comprise radar 1, database 2 and host computer 3, radar 1, database 2 and host computer 3 link to each other successively, 1 pair of marine site of detecting of described 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 finish:

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

2) training sample is carried out normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

Wherein, D represents the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70; Robust forecasting model MBM 5, in order to set up forecasting model, adopt following process to finish: with X, the following linear equation of Y substitution that obtains:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein

Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T,

K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix,

Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias, And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

Improve intelligent optimizing module 6, the nuclear parameter θ and the penalty coefficient γ of forecasting model be optimized, adopt following process to finish in order to adopt evolution genetic algorithm:

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

Module of target detection 7, in order to carry out target detection, adopt following process to finish:

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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) carry out normalized;

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

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:

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

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

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

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.

Described host computer 3 also comprises: model modification module 8, by the time interval image data of setting, measured data and the model prediction value that obtains compared, and if relative error greater than 10%, then adds new data the training sample data, upgrade forecasting model.

Described host computer 3 also comprises: display module 9 as a result, and the testing result of module of target detection is shown at host computer.

The hardware components of described 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 robust intelligence radar method for detecting targets at sea, 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 normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

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

(4) with the X, the following linear equation of Y substitution that obtain:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein

Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T,

K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix,

Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias,

And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

(5) be optimized with the nuclear parameter θ and the penalty coefficient γ of evolution genetic algorithm, adopt following process to finish step (4):

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

(6) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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;

(7) carry out normalized;

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

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

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

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

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

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

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;

(10) 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 method also comprises: (11), by the time interval image data of setting, with the measured data that obtains and model prediction value relatively, if relative error greater than 10%, then adds new data the training sample data, the renewal forecasting model.

Described method also comprises: in described step (10), the testing result of module of target detection is shown at host computer.

Claims (6)

1. robust intelligence radar naval target detection system, comprise radar, database and host computer, radar, database and host computer link to each other successively, it is characterized in that: described 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 finish:

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

2) training sample is carried out normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

Wherein, D represents the reconstruct dimension, and D is a natural number, and D＜N, and the span of D is 50-70; Robust forecasting model MBM, in order to set up forecasting model, adopt following process to finish: with X, the following linear equation of Y substitution that obtains:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T,

K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix,

Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias, And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

Improve intelligent optimizing module, the nuclear parameter θ and the penalty coefficient γ of forecasting model be optimized, adopt following process to finish in order to adopt evolution genetic algorithm:

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

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

1) gathers D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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) carry out normalized;

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

3) the estimation function f (x) that treats that substitution forecasting model MBM obtains calculates 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:

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

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

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

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. robust intelligence radar naval target detection system as claimed in claim 1, it is characterized in that: described host computer also comprises: the discrimination model update module, in order to sampling time interval image data by setting, the measured data and the model prediction value that obtain are compared, if relative error is greater than 10%, then new data is added the training sample data, upgrade forecasting model.

3. robust as claimed in claim 1 or 2 intelligence radar naval target 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 radar method for detecting targets at sea of robust as claimed in claim 1 intelligence radar naval target detection system, 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, i=1 ..., N;

(2) training sample is carried out normalized, obtain the normalization amplitude

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

Wherein, minx represents the minimum value in the training sample, and maxx represents 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:

$X=\left[\begin{array}{cccc}{\stackrel{\‾}{x}}_1& {\stackrel{\‾}{x}}_2& L& {\stackrel{\‾}{x}}_D\\ {\stackrel{\‾}{x}}_2& {\stackrel{\‾}{x}}_3& L& {\stackrel{\‾}{x}}_{D+1}\\ M& M& O& M\\ {\stackrel{\‾}{x}}_{N-D}& {\stackrel{\‾}{x}}_{N-D+1}& L& {\stackrel{\‾}{x}}_{N-1}\end{array}\right]$

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

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

(4) with the X, the following linear equation of Y substitution that obtain:

$\left[\begin{array}{cc}0& 1_v^T\\ 1_v& K+V_{\mathrm{\γ}}\end{array}\right]\left[\begin{array}{c}{b}^{*}\\ {\mathrm{\α}}^{*}\end{array}\right]=\left[\begin{array}{c}0\\ Y\end{array}\right]$

Wherein $V_{\mathrm{\γ}}=\mathrm{diag}\{\frac{1}{{\mathrm{\γv}}_1},K,\frac{1}{{\mathrm{\γv}}_M}\}$

Weight factor ν _iCalculate by following formula:

Wherein

Be error variance ξ _iThe estimation of standard deviation, c ₁, c ₂Be constant;

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

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

Wherein, M is the number of support vector, 1 _ν=[1 ..., 1] ^T,

K=exp (|| x _i-x _j||/θ ²), the transposition of subscript T representing matrix,

Be Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b ^*Be amount of bias,

And exp (|| x-x _i||/θ ²) be the kernel function of support vector machine, x _jBe j Radar Sea clutter echoed signal amplitude, θ is a nuclear parameter, and x represents input variable, and γ is a penalty coefficient;

(5) be optimized with the nuclear parameter θ and the penalty coefficient γ of evolution genetic algorithm, adopt following process to finish step (4):

5.1) adopt real number coding method that θ and γ are encoded;

5.2) produce initial population at random;

5.3) calculate each individual fitness, and judge whether to meet the algorithm end condition, if meet, the optimum solution of output optimized individual and representative thereof, and finish to calculate, otherwise continue iteration;

5.4) adopt normal distribution probability to select individuality;

5.5) produce new individuality by the single-point linear crossing;

5.6) produce new individuality by even variation mode;

5.7) the new simple property of a body and function method is carried out the determinacy optimizing;

5.8) population of new generation that produces, return 5.3) carry out iteration;

Wherein, the initial population size is 50-200, maximum algebraically 50-300, it is 0.05-0.1 that optimized individual is selected probability, crossover probability is 0.5-0.9, the variation probability is 0.001-0.01, the extensive root-mean-square error of ideal adaptation degree preference pattern, and end condition is for reaching maximum algebraically or the five generations successively fitness is constant;

(6) gather D extra large clutter echoed signal amplitude at sampling instant t and obtain TX=[x _T-D+1, K, 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;

(7) carry out normalized;

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

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

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

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

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

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

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;

(10) 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. radar method for detecting targets at sea as claimed in claim 4 is characterized in that: described method also comprises:

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

6. as claim 4 or 5 described radar method for detecting targets at sea, it is characterized in that: in described step (10), the testing result of module of target detection is shown at host computer.

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