CN103900541A - Marine condition estimator - Google Patents

Abstract

The invention belongs to the field of marine environment monitoring and in particular relates to a marine condition estimator, which comprises a ship rolling signal acquisition device, an AR (auto regressive) model parameter identifier, a white noise simulator, a rolling angular spectrum density estimator, a sea wave spectrum density reverse calculator and a marine condition computing unit. Compared with a measuring method based on a radar wave and a radio wave reflection principle of the radar wave, the marine condition estimator is simple in structure, needs no extra hardware cost, is accurate in estimation and can meet the requirement of supporting devices of a sea ship on marine condition information.

Description

A kind of sea situation estimator

Technical field

The invention belongs to ship environment monitoring field, be specifically related to a kind of sea situation estimator.

Background technology

The afloat motion attitude control of boats and ships and sea situation relation are very close, control effect and the sea situation of Ship Motion Attitude control system have very close relationship, how in control system, sea situation to be estimated in real time, adjust in good time and control parameter, to greatly improve Ship Motion Attitude control effect, improve the ability that boats and ships adapt to severe sea condition.For solving the real-time analysis of sea situation, occurred utilizing the radar emission signal that assembles on boats and ships and radar signal thereof to calculate the wave height of wave by the mistiming between the reflection wave on corrugated and speed of the ship in metres per second, then determine sea situation according to the wave height of calculating, the method possible in theory, but also can be subject to the impact of the speed of a ship or plane, wave celerity etc., and its cost is very high, need to be equipped with radar signal and reflected signal receiving trap thereof.

Summary of the invention

The object of the present invention is to provide one directly to utilize navigational system or angular transducer etc. to obtain ship rolling signal, by the statistics of ship rolling signal being realized to the sea situation estimator of sea situation.

The object of the present invention is achieved like this:

Sea situation estimator, comprise ship rolling signal picker, AR identification of Model Parameters device, white noise simulator, roll angle spectral density estimation device, ocean wave spectrum density backstepping device, sea situation computing unit, ship rolling signal picker, adopt digital A/D acquisition interface to realize, this interface is responsible for the collection of ship rolling angle signal, comprises the filtering processing of collection signal; White noise simulator adopts pseudo-random sequence and digital form, and the white noise sequence that simulation needs in AR identification of Model Parameters, as the random input signal of AR identification of Model Parameters device; AR identification of Model Parameters device, for by white noise and the input of ship rolling angle random signal, carries out identification to AR model parameter, and acquisition can utilize white noise to carry out the forecasting model of ship rolling angle signal accurate forecast; Roll angle spectral density function estimation device, utilizes permanent white noise spectrum density and the AR model of foundation, and estimation obtains roll angle spectral density function; Ocean wave spectrum density backstepping device utilizes the transport function relation between ship rolling angle and wave, utilizes the counter spectral density function of releasing wave of roll angle spectral density function obtaining; The anti-ocean wave spectrum density function that pushes away acquisition of sea situation computing unit utilization, by the mean square deviation of ocean wave spectrum Density functional calculations wave, is then had the relation between adopted wave height and wave root mean square and then is calculated the adopted wave height of having of wave by wave, completes the estimation of sea situation.

AR identifier uses N the ship rolling angular data φ measuring _t(t=1,2 ..., N), set up P (P < N) rank AR (P) model:

${\mathrm{\&phi;}}_t=-\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{\mathrm{\&phi;}}_{t-k}+w_t$

Wherein w _tthat zero-mean, variance are

stationary white noise sequence; a _k(k=1,2 ... P) be autoregressive coefficient, can pass through any two the roll angle time series φ based in AR (P) model _k, φ _jbetween covariance r _φ(k, j) is to meet Yule-Walker equation

$\left[\begin{array}{cccc}{r}_{\mathrm{\&phi;}}\left(0\right)& r_{\mathrm{\&phi;}}(-1)& ...& r_{\mathrm{\&phi;}}(-P)\\ r_{\mathrm{\&phi;}}\left(1\right)& r_{\mathrm{\&phi;}}\left(0\right)& ...& r_{\mathrm{\&phi;}}(-P+1)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ r_{\mathrm{\&phi;}}\left(P\right)& r_{\mathrm{\&phi;}}(P-1)& ...& r_{\mathrm{\&phi;}}\left(0\right)\end{array}\right]\left[\begin{array}{c}1\\ a_1\\ .\\ .\\ .\\ a_P\end{array}\right]=\left[\begin{array}{c}{\mathrm{\&sigma;}}_w^2\\ 0\\ .\\ .\\ .\\ 0\end{array}\right]$ For condition, select the one in correlation method, covariance method, modified covariance method, to r _φ(k, j) estimate, wherein

Correlation method

${\hat{r}}_{\mathrm{\&phi;}}\left(k\right)=\frac{1}{N}\underset{t=0}{\overset{N-1-k}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+k}{\mathrm{\&phi;}}_t,k=\mathrm{0,1},...,P$

Covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l.k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{N-P}\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k},l,k=\mathrm{0,1},...,P$

Modified covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l,k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{2(N-P)}[\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k}+\underset{t=0}{\overset{N-1-P}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+l}{\mathrm{\&phi;}}_{t+k}],k=\mathrm{0,1},...,P$

Determine covariance r by estimation mode _φafter (k, j), calculate the autoregressive coefficient a in AR (P) model _k(k=1,2 ... P).

Between roll angle spectral density estimation device ship rolling angle and white noise, there is following relation

$\underset{K=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k\mathrm{\&phi;}\left(z\right)z^{-k}=W\left(z\right),$

$G\left(z\right)=\frac{X\left(z\right)}{W\left(z\right)}=\frac{1}{A\left(z\right)},$

$A\left(z\right)=\underset{k=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k}=1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k},$ A ₀be defined as 1

Between roll angle spectral density function and white noise density spectra, close and be:

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=S_w\left(\mathrm{\&omega;}\right)\frac{1}{A\left(z\right)A\left(z^{-1}\right)},$

Get the spectral density S of white noise _w(ω)=b, b is permanent number, ship rolling angular spectrum is

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=b{\left|\frac{1}{A\left(e^{\mathrm{j\&omega;}}\right)}\right|}^2=\frac{b}{{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-\mathrm{j\&omega;k}}|}^2}.$

Ocean wave spectrum density backstepping device

According to random theory, between ocean wave spectrum, ship rolling angular spectrum and the rolling motion of boats and ships, be related to deal with data

S _φ(ω)＝G _φh(-jω)G _φh(jω)S _h(ω)＝|G _φh(jω)| ²S _h(ω)

Wherein S _φ(ω) be ship rolling angular spectrum; G _{φ h}(s) be ship rolling motion model, in the time of the rolling of boats and ships low-angle, G _{φ h}(s) be second-order linearity function, in the time of the rolling of boats and ships wide-angle, G _{φ h}(s) be nonlinear function; S _h(ω) be ocean wave spectrum,

The downward wave of different waves meets with spectral density function and is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}$

Under encounter frequency, ocean wave spectrum density is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}=\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}.$

Sea situation computing unit, by the ocean wave spectrum density of estimating to obtain, carries out variance statistical computation

${\mathrm{\&sigma;}}_h^2={\&Integral;}_0^{\&infin;}{S}_h\left({\mathrm{\&omega;}}_e\right)d{\mathrm{\&omega;}}_e={\&Integral;}_0^{\&infin;}\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}d{\mathrm{\&omega;}}_e$

Then according to the relation between the adopted wave height of having of wave and wave root mean square, calculate the adopted wave height that has under current sea situation, and contrast sea situation classification, forecast the residing sea situation grade of current boats and ships

$h_{1/3}=2\sqrt{{\mathrm{\&sigma;}}_h^2}=2{\mathrm{\&sigma;}}_h.$

Beneficial effect of the present invention is:

With compared with radar wave and radio wave attenuation principle measuring method thereof, estimator is simple in structure, does not need extra hardware cost.Estimate accurately, can meet the demand of marine ships corollary apparatus to sea situation information.

Accompanying drawing explanation

The sea situation estimator block diagram of Fig. 1 based on AR model;

Fig. 2 sea situation is estimated flow process.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described further.

The present invention is based on the sea situation estimator structured flowchart of AR model as shown in Figure 1.Estimator is mainly made up of parts such as ship rolling signal picker, AR identification of Model Parameters device, white noise simulator, roll angle spectral density estimation device, ocean wave spectrum density backstepping device, sea situation computing units.

Wherein ship rolling signal picker, adopts digital A/D acquisition interface to realize, and this interface is responsible for the collection of ship rolling angle signal, comprises the filtering processing of collection signal.White noise simulator adopts pseudo-random sequence and digital form, and the white noise sequence that simulation needs in AR identification of Model Parameters, as the random input signal of AR identification of Model Parameters device.AR identification of Model Parameters device is mainly used in, by white noise and the input of ship rolling angle random signal, AR model parameter being carried out to identification, and acquisition can utilize white noise to carry out the forecasting model of ship rolling angle signal accurate forecast.The estimation of roll angle spectral density function, is to utilize permanent white noise spectrum density and the AR model of foundation, and estimation obtains roll angle spectral density function.Ocean wave spectrum inversion of Density is the transport function relation of utilizing between ship rolling angle and wave, utilizes the counter spectral density function of releasing wave of roll angle spectral density function obtaining.It is to utilize the anti-ocean wave spectrum density function that pushes away acquisition that sea situation is calculated, by the mean square deviation of ocean wave spectrum Density functional calculations wave, then there is the relation between adopted wave height and wave root mean square and then calculate the adopted wave height of having of wave by wave, and then completing the estimation of sea situation.

A.AR identifier

In whole sea situation estimator, according to roll angle time series φ _k, introduce white noise, the AR identification of Model Parameters device principle of estimation of carrying out AR (P) model parameter is as follows:

If ship rolling angle is the stationary stochastic process that a class average is zero, use N the ship rolling angular data φ measuring _t(t=1,2 ..., N), can set up the P shown in formula (1) (P < N) rank AR (P) model:

${\mathrm{\&phi;}}_t=-\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{\mathrm{\&phi;}}_{t-k}+w_t---\left(1\right)$

Wherein w _tthat zero-mean, variance are

stationary white noise sequence; Order P can rule of thumb determine, or adopts the methods such as Akaike's Information Criterion to pre-determine an index relevant with model order P, then determines order P by optimization index; a _k(k=1,2 ... P) be autoregressive coefficient, can be by following any two the roll angle time series φ based in AR (P) model _k, φ _jbetween covariance r _φ(k, j) meets Yule-Walker equation (2) for condition, then can be according to actual conditions, and any of selecting type (3), (4), (5) is to r _φ(k, j) estimates.Determining covariance r by estimation mode _φafter (k, j), utilize Yule-Walker equation (2) to calculate the autoregressive coefficient a in AR (P) model _k(k=1,2 ... P).

$\left[\begin{array}{cccc}{r}_{\mathrm{\&phi;}}\left(0\right)& r_{\mathrm{\&phi;}}(-1)& ...& r_{\mathrm{\&phi;}}(-P)\\ r_{\mathrm{\&phi;}}\left(1\right)& r_{\mathrm{\&phi;}}\left(0\right)& ...& r_{\mathrm{\&phi;}}(-P+1)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ r_{\mathrm{\&phi;}}\left(P\right)& r_{\mathrm{\&phi;}}(P-1)& ...& r_{\mathrm{\&phi;}}\left(0\right)\end{array}\right]\left[\begin{array}{c}1\\ a_1\\ .\\ .\\ .\\ a_P\end{array}\right]=\left[\begin{array}{c}{\mathrm{\&sigma;}}_w^2\\ 0\\ .\\ .\\ .\\ 0\end{array}\right]---\left(2\right)$

1) correlation method

${\hat{r}}_{\mathrm{\&phi;}}\left(k\right)=\frac{1}{N}\underset{t=0}{\overset{N-1-k}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+k}{\mathrm{\&phi;}}_t,k=\mathrm{0,1},...,P---\left(3\right)$

2) covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l.k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{N-P}\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k},l,k=\mathrm{0,1},...,P---\left(4\right)$

3) modified covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l,k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{2(N-P)}[\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k}+\underset{t=0}{\overset{N-1-P}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+l}{\mathrm{\&phi;}}_{t+k}],k=\mathrm{0,1},...,P---\left(5\right)$

B. roll angle spectral density function estimation principle

Contrast formula (1), has following relation between ship rolling angle and white noise

$\underset{K=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k\mathrm{\&phi;}\left(z\right)z^{-k}=W\left(z\right)---\left(6\right)$

Formula (6) represents the transport function shown in an accepted way of doing sth (7):

$G\left(z\right)=\frac{X\left(z\right)}{W\left(z\right)}=\frac{1}{A\left(z\right)}---\left(7\right)$

In formula (7) $A\left(z\right)=\underset{k=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k}=1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k}---\left(8\right)$

A ₀be defined as 1.

By Principle of Random Process, between the white noise density spectra in roll angle spectral density function and formula (1), there is relation shown in formula (9)

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=S_w\left(\mathrm{\&omega;}\right)\frac{1}{A\left(z\right)A\left(z^{-1}\right)}---\left(9\right)$

Get the spectral density S of white noise _w(ω)=b, b is permanent number, ship rolling angular spectrum is

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=b{\left|\frac{1}{A\left(e^{\mathrm{j\&omega;}}\right)}\right|}^2=\frac{b}{{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-\mathrm{j\&omega;k}}|}^2}---\left(10\right)$

C. ocean wave spectrum inversion of Density principle

According to random theory, between ocean wave spectrum, ship rolling angular spectrum and the rolling motion of boats and ships, there is the relation shown in formula (11):

S _φ(ω)＝G _φh(-jω)G _φh(jω)S _h(ω)＝|G _φh(jω)| ²S _h(ω) (11)

Wherein S _φ(ω) be ship rolling angular spectrum; G _{φ h}(s) be ship rolling motion model, in the time of the rolling of boats and ships low-angle, G _{φ h}(s) be second-order linearity function, in the time of the rolling of boats and ships wide-angle, G _{φ h}(s) be nonlinear function; S _h(ω) be ocean wave spectrum.

According to formula (11), the downward wave of different waves meets with spectral density function and is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}---\left(12\right)$

Convolution (10), under encounter frequency, ocean wave spectrum density is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}=\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}---\left(13\right)$

D. sea situation is calculated

By the ocean wave spectrum density of estimating to obtain, available formula (14) is carried out variance statistical computation

${\mathrm{\&sigma;}}_h^2={\&Integral;}_0^{\&infin;}{S}_h\left({\mathrm{\&omega;}}_e\right)d{\mathrm{\&omega;}}_e={\&Integral;}_0^{\&infin;}\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}d{\mathrm{\&omega;}}_e---\left(14\right)$

Then according between the adopted wave height of having of wave and wave root mean square suc as formula relation shown in (15), calculate the adopted wave height that has under current sea situation, and contrast sea situation classification, can go out the residing sea situation grade of current boats and ships by accurate forecast.

$h_{1/3}=2\sqrt{{\mathrm{\&sigma;}}_h^2}=2{\mathrm{\&sigma;}}_h---\left(15\right)$

The implementation step of whole estimator is as follows:

As shown in Figure 2, concrete implementation step is as follows for the flow process that whole sea situation is estimated:

1). determining time, Real-time Collection ship rolling angle, speed of a ship or plane signal;

2). the data that gather are carried out to filtering processing, pick out and measure wild point, filter and measure noise;

3). select AR identification of Model Parameters algorithm;

4). use pseudo-random function, produce approximate white noise sequence;

5). according to the white noise sequence of image data and computing machine generation, use the identification algorithm of selecting to carry out the identification of AR model parameter;

6). use FFT, carry out the close analysis of spectrum of white noise sequence, obtain white noise spectrum density function, then pass through AR model as formed filter, determine roll angle spectral density function;

7). input ship rolling parameter, the transport function between wave and ship rolling angle, and utilize 6) definite anti-Wave Spectrum Density Function that pushes away of roll angle spectral density function;

8). carry out the calculating of wave variance, obtain wave variance and have adopted wave height;

9). determine sea situation grade according to wave height

10). determine that end is no, proceed to step 1 if do not finish), restart sea situation and estimate to calculate.

Patent of the present invention relates to utilizes Wave Information to carry out the estimation problem of sea situation.The method that adopts this patent to propose can calculate according to the rolling motion information of boats and ships the rolling statistical variance of boats and ships, and utilize AR model to release the rolling angular spectrum of boats and ships, have after rolling angular spectrum, according to the relation between STOCHASTIC CONTROL principle and ship rolling angular spectrum and ocean wave spectrum, the anti-ocean wave spectrum of releasing, thereby obtain the statistical parameter of sea situation, realize the estimation to sea situation.

Claims (5)

1. a sea situation estimator, comprise ship rolling signal picker, AR identification of Model Parameters device, white noise simulator, roll angle spectral density estimation device, ocean wave spectrum density backstepping device, sea situation computing unit, it is characterized in that: ship rolling signal picker, adopt digital A/D acquisition interface to realize, this interface is responsible for the collection of ship rolling angle signal, comprises the filtering processing of collection signal; White noise simulator adopts pseudo-random sequence and digital form, and the white noise sequence that simulation needs in AR identification of Model Parameters, as the random input signal of AR identification of Model Parameters device; AR identification of Model Parameters device, for by white noise and the input of ship rolling angle random signal, carries out identification to AR model parameter, and acquisition can utilize white noise to carry out the forecasting model of ship rolling angle signal accurate forecast; Roll angle spectral density function estimation device, utilizes permanent white noise spectrum density and the AR model of foundation, and estimation obtains roll angle spectral density function; Ocean wave spectrum density backstepping device utilizes the transport function relation between ship rolling angle and wave, utilizes the counter spectral density function of releasing wave of roll angle spectral density function obtaining; The anti-ocean wave spectrum density function that pushes away acquisition of sea situation computing unit utilization, by the mean square deviation of ocean wave spectrum Density functional calculations wave, is then had the relation between adopted wave height and wave root mean square and then is calculated the adopted wave height of having of wave by wave, completes the estimation of sea situation.

2. a kind of sea situation estimator according to claim 1, is characterized in that: described AR identifier uses N the ship rolling angular data φ measuring _t(t=1,2 ..., N), set up P (P < N) rank AR (P) model:

${\mathrm{\&phi;}}_t=-\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{\mathrm{\&phi;}}_{t-k}+w_t$

Wherein w _tthat zero-mean, variance are

stationary white noise sequence; a _k(k=1,2 ... P) be autoregressive coefficient, can pass through any two the roll angle time series φ based in AR (P) model _k, φ _jbetween covariance r _φ(k, j) is to meet Yule-Walker equation

$\left[\begin{array}{cccc}{r}_{\mathrm{\&phi;}}\left(0\right)& r_{\mathrm{\&phi;}}(-1)& ...& r_{\mathrm{\&phi;}}(-P)\\ r_{\mathrm{\&phi;}}\left(1\right)& r_{\mathrm{\&phi;}}\left(0\right)& ...& r_{\mathrm{\&phi;}}(-P+1)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ r_{\mathrm{\&phi;}}\left(P\right)& r_{\mathrm{\&phi;}}(P-1)& ...& r_{\mathrm{\&phi;}}\left(0\right)\end{array}\right]\left[\begin{array}{c}1\\ a_1\\ .\\ .\\ .\\ a_P\end{array}\right]=\left[\begin{array}{c}{\mathrm{\&sigma;}}_w^2\\ 0\\ .\\ .\\ .\\ 0\end{array}\right]$ For condition, select the one in correlation method, covariance method, modified covariance method, to r _φ(k, j) estimate, wherein

Correlation method

${\hat{r}}_{\mathrm{\&phi;}}\left(k\right)=\frac{1}{N}\underset{t=0}{\overset{N-1-k}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+k}{\mathrm{\&phi;}}_t,k=\mathrm{0,1},...,P$

Covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l.k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{N-P}\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k},l,k=\mathrm{0,1},...,P$

Modified covariance method

${\hat{r}}_{\mathrm{\&phi;}}(l,k)={\hat{r}}_{\mathrm{\&phi;}}(k,l)=\frac{1}{2(N-P)}[\underset{t=P}{\overset{N-1}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t-l}{\mathrm{\&phi;}}_{t-k}+\underset{t=0}{\overset{N-1-P}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_{t+l}{\mathrm{\&phi;}}_{t+k}],k=\mathrm{0,1},...,P$

Determine covariance r by estimation mode _φafter (k, j), calculate the autoregressive coefficient a in AR (P) model _k(k=1,2 ... P).

3. a kind of sea situation estimator according to claim 1, is characterized in that: between described roll angle spectral density estimation device ship rolling angle and white noise, have following relation

$\underset{K=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k\mathrm{\&phi;}\left(z\right)z^{-k}=W\left(z\right),$

$G\left(z\right)=\frac{X\left(z\right)}{W\left(z\right)}=\frac{1}{A\left(z\right)},$

$A\left(z\right)=\underset{k=0}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k}=1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{z}^{-k},$ A ₀be defined as 1

Between roll angle spectral density function and white noise density spectra, close and be:

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=S_w\left(\mathrm{\&omega;}\right)\frac{1}{A\left(z\right)A\left(z^{-1}\right)},$

Get the spectral density S of white noise _w(ω)=b, b is permanent number, ship rolling angular spectrum is

$S_{\mathrm{\&phi;}}\left(\mathrm{\&omega;}\right)=b{\left|\frac{1}{A\left(e^{\mathrm{j\&omega;}}\right)}\right|}^2=\frac{b}{{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-\mathrm{j\&omega;k}}|}^2}.$

4. a kind of sea situation estimator according to claim 1, is characterized in that: described ocean wave spectrum density backstepping device is according to random theory, between ocean wave spectrum, ship rolling angular spectrum and the rolling motion of boats and ships, is related to deal with data

S _φ(ω)＝G _φh(-jω)G _φh(jω)S _h(ω)＝|G _φh(jω)| ²S _h(ω)

Wherein S _φ(ω) be ship rolling angular spectrum; G _{φ h}(s) be ship rolling motion model, in the time of the rolling of boats and ships low-angle, G _{φ h}(s) be second-order linearity function, in the time of the rolling of boats and ships wide-angle, G _{φ h}(s) be nonlinear function; S _h(ω) be ocean wave spectrum,

The downward wave of different waves meets with spectral density function and is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}$

Under encounter frequency, ocean wave spectrum density is

$S_h\left({\mathrm{\&omega;}}_e\right)=\frac{S_{\mathrm{\&phi;}}\left({\mathrm{\&omega;}}_e\right)}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2}=\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}.$

5. a kind of sea situation estimator according to claim 1, is characterized in that: described sea situation computing unit, by the ocean wave spectrum density of estimating to obtain, carries out variance statistical computation

${\mathrm{\&sigma;}}_h^2={\&Integral;}_0^{\&infin;}{S}_h\left({\mathrm{\&omega;}}_e\right)d{\mathrm{\&omega;}}_e={\&Integral;}_0^{\&infin;}\frac{b}{{\left|G_{\mathrm{\&phi;h}}\left(j{\mathrm{\&omega;}}_e\right)\right|}^2{|1+\underset{k=1}{\overset{P}{\mathrm{\&Sigma;}}}{a}_k{e}^{-j{\mathrm{\&omega;}}_{e}k}|}^2}d{\mathrm{\&omega;}}_e$

Then according to the relation between the adopted wave height of having of wave and wave root mean square, calculate the adopted wave height that has under current sea situation, and contrast sea situation classification, forecast the residing sea situation grade of current boats and ships

$h_{1/3}=2\sqrt{{\mathrm{\&sigma;}}_h^2}=2{\mathrm{\&sigma;}}_h.$

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