CN104537891A - Ship trajectory real-time predicting method - Google Patents

Abstract

The invention relates to a ship trajectory real-time predicting method. The ship trajectory real-time predicting method comprises the following steps that firstly, real-time and historical position information of a ship is obtained through sea surface radar and is primarily processed; then, ship trajectory data are preprocessed at each sampling time, then are clustered at each sampling time and are subjected to parameter training through a hidden Markov model at each sampling time, and then a hidden state q corresponding to the observation value at the current time is obtained by the adoption of a Viterbi algorithm according to parameters of the hidden Markov model at each sampling time; finally, by setting the prediction time domain W at each sampling time, on the basis of the hidden state q of the ship at the current time, the position prediction value O of the ship in the future is obtained, and therefore the ship trajectory in the future can be obtained in a rolling speculation mode at each sampling time. The ship trajectory real-time predicting method predicts the ship trajectory in real time in a rolling mode, is good in accuracy and provides powerful guarantee for subsequent ship conflict resolution.

Description

A kind of boats and ships track real-time predicting method

Technical field

The present invention relates to a kind of marine site traffic control method, particularly relate to a kind of boats and ships track real-time predicting method based on Rolling Planning strategy.

Background technology

Along with the fast development of global shipping business, the traffic in the busy marine site of part is further crowded.In the intensive complicated marine site of vessel traffic flow, still adopt sail plan can not adapt to the fast development of shipping business in conjunction with the regulation model that artificial interval is allocated for the collision scenario between boats and ships.For ensureing the personal distance between boats and ships, implement the emphasis that effective conflict allotment just becomes marine site traffic control work.Boats and ships conflict Resolution is a gordian technique in navigational field, frees scheme safely and efficiently for increasing marine site boats and ships flow and guaranteeing that sea-freight safety is significant.

In order to improve the efficiency of navigation of boats and ships, marine radar automatic plotter has been widely applied in ship monitor and collision prevention at present, and this equipment provides reference frame by extracting boats and ships relevant informations for the judgement of collision scenario between boats and ships.Although this kind equipment greatly reduces manual supervisory load, it does not have the automatic conflict Resolution function of boats and ships.And boats and ships conflict Resolution is on the basis based on the prediction to boats and ships track, in boats and ships real navigation, by the impact of the various factors such as meteorological condition, navigator and driver's operation, its running status often not exclusively belongs to a certain specific motion state, in boats and ships trajectory predictions process, need the impact considering various enchancement factor, by the up-to-date characteristic that obtains all kinds of enchancement factor rolling forecast implemented to its Future Trajectory and strengthen the robustness of its trajectory predictions.

Summary of the invention

The technical problem to be solved in the present invention is to provide a kind of robustness good boats and ships track real-time predicting method, and the boats and ships trajectory predictions precision of the method is higher.

The technical scheme realizing the object of the invention is to provide a kind of boats and ships track real-time predicting method, comprises following several step:

1. obtain the real-time of boats and ships and historical position information by sea radar, the positional information of each boats and ships is discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'], by application wavelet transformation theory to original discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'] carry out rough handling, thus obtain the denoising discrete two-dimensional position sequence x=[x of boats and ships ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n];

2. in each sampling instant to the pre-service of boats and ships track data, according to the boats and ships original discrete two-dimensional position sequence x=[x that obtains ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n], adopt first order difference method to carry out processing new boats and ships discrete location sequence Δ x=[the Δ x of acquisition to it ₁, Δ x ₂..., Δ x _n-1] and Δ y=[Δ y ₁, Δ y ₂..., Δ y _n-1], wherein Δ x _i=x _i+1-x _i, Δ y _i=y _i+1-y _i(i=1,2 ..., n-1);

3. in each sampling instant to boats and ships track data cluster, to new boats and ships discrete two-dimensional position sequence Δ x and Δ y after process, by setting cluster number M ', adopt K-means clustering algorithm to carry out cluster to it respectively;

4. Hidden Markov Model (HMM) is utilized to carry out parameter training in each sampling instant to boats and ships track data, by the vessel motion track data Δ x after process and Δ y being considered as the aobvious observed reading of hidden Markov models, upgrade period τ ' by setting hidden state number N and parameter, according to nearest T ' individual position detection value and adopt B-W algorithm to roll to obtain up-to-date Hidden Markov Model (HMM) parameter lambda ';

5. in each sampling instant foundation Hidden Markov Model (HMM) parameter, the hidden state q corresponding to Viterbi algorithm acquisition current time observed reading is adopted;

6. in each sampling instant, by setting prediction time domain W, based on the hidden state q of boats and ships current time, obtain the position prediction value O of future time period boats and ships, thus infer the track to boats and ships in future time period in each sampling instant rolling.

Further, described step 1. in, by application wavelet transformation theory to original discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'] carry out rough handling, thus obtain the denoising discrete two-dimensional position sequence x=[x of boats and ships ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n]: for given original two dimensional sequence data x '=[x ₁', x ₂' ..., x _n'], utilize the linear representation of following form to be similar to it respectively: $\tilde{f}\left(x^{\′}\right)=\underset{\∀K}{\underset{\∀J}{\mathrm{\Σ}}}{c}_{J,K}{\mathrm{\ψ}}_{J,K}\left(x^{\′}\right),$

Wherein: $\begin{array}{c}{c}_{J,K}={\∫}_{-\∞}^{\∞}{f}^{\′}\left(x^{\′}\right){\mathrm{\ψ}}_{J,K}\left(x^{\′}\right){\mathrm{dx}}^{\′}\\ {\mathrm{\ψ}}_{J,K}\left(x^{\′}\right)=\mathrm{\δ}\·\mathrm{\ψ}(2^J{x}^{\′}-K)\end{array},$

F ' (x ') represents the function expression obtained after data smoothing processing, and ψ (x ') represents female ripple, and δ, J and K are wavelet transformation constant, ψ _{j, K}(x ') represents the transition form of female ripple, c _{j, K}represent the function coefficients obtained by wavelet transform procedure, it embodies wavelet ψ _{j, K}(x '), to the weight size of whole approximation to function, if this coefficient is very little, so it means wavelet ψ _{j, K}the weight of (x ') is also less, thus can under the prerequisite of not influence function key property, by wavelet ψ from approximation to function process _{j, K}(x ') removes; In real data processing procedure, implemented " threshold transition " by setting threshold value χ, work as c _{j, K}during < χ, setting c _{j, K}=0; Choosing of threshold function table adopts the following two kinds mode:

${\mathrm{\&rho;}}_1(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.$ With ${\mathrm{\&rho;}}_2(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d-\left(\frac{d}{\left|d\right|}\mathrm{\&chi;}\right)& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.;$

For y '=[y ₁', y ₂' ..., y _n'], also adopt said method to carry out denoising.

Further, described step 4. in determine that flight path Hidden Markov Model (HMM) parameter lambda '=process of (π, A, B) is as follows:

4.1) variable initialize: application is uniformly distributed to variable π _i, a _ijand b _j(o _k) initialize with and make it meet constraint condition: ${\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&pi;}}_i^0=1,{\mathrm{\&Sigma;}}_{j=1}^N{a}_{\mathrm{ij}}^0=1(1\&le;i\&le;N)$ With ${\mathrm{\&Sigma;}}_{k=1}^{M^{\&prime;}}{b}_j^0\left(o_k\right)=1(1\&le;j\&le;N),$ Obtain λ thus ₀=(π ₀, A ₀, B ₀), wherein o _krepresent a certain aobvious observed reading, π ₀, A ₀and B ₀by element respectively with the matrix formed, makes parameter l=0, o=(o _{t-T '+1}..., o _t-1, o _t) be the individual historical position observed reading of the T ' before current time t;

4.2) E-M algorithm is performed:

4.2.1) E-step: by λ ₁calculate ξ _e(i, j) and γ _e(s _i);

Variable ${\mathrm{\&xi;}}_e(i,j)=\frac{P(q_e=s_i,q_{e+1}=s_j,o|{\mathrm{\&lambda;}}_l)}{P\left(o\right|{\mathrm{\&lambda;}}_l)},$ So ${\mathrm{\&gamma;}}_e\left(s_i\right)={\mathrm{\&Sigma;}}_{j=1}^N{\mathrm{\&xi;}}_e(i,j),$

Wherein s represents a certain hidden state;

4.2.2) M-step: use ${\stackrel{\&OverBar;}{\mathrm{\&pi;}}}_i={\mathrm{\&gamma;}}_1\left(s_i\right),{\stackrel{\&OverBar;}{a}}_{\mathrm{ij}}=\frac{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&xi;}}_e(i,j)}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&gamma;}}_e\left(s_i\right)},{\stackrel{\&OverBar;}{b}}_j\left(o_k\right)=\frac{\underset{o_e=o_k}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}}{\mathrm{\&gamma;}}_e\left(s_j\right)}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}{\mathrm{\&gamma;}}_e\left(s_j\right)}$ Estimate π respectively _i, a _ijand b _j(o _k) and obtain λ thus _l+1;

4.2.3) circulate: l=l+1, repeats E-step and M-step, until π _i, a _ijand b _j(o _k) convergence, namely

wherein parameter ε=0.00001, returns step 4.2.4);

4.2.4): make λ '=λ _l+1, algorithm terminates.

Further, described step 5. in determine that the iterative process of the best hidden status switch of ship track is as follows:

5.1) variable initialize: make g=2, β _{t '}(s _i)=1 (s _i∈ S), δ ₁(s _i)=π _ib _i(o ₁), ψ ₁(s _i)=0, wherein,

${\mathrm{\&delta;}}_g\left(s_i\right)=\underset{q_1,q_2,...,q_{g-1}}{\max }P(q_1,q_2,...,q_{g-1},q_g=s_i,o_1,o_2,...,o_g|{\mathrm{\&lambda;}}^{\&prime;})$

, wherein variable ψ _g(s _j) represent make variable δ _g-1(s _i) a _ijget the hidden state s of ship track of maximal value _i, parameter S represents the set of hidden state;

5.2) recursive process: ${\mathrm{\&delta;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right]b_j\left(o_g\right),{\mathrm{\&psi;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right];$

5.3) moment upgrade: make g=g+1, if g≤T ', return step 5.2), otherwise iteration ends and forward step 5.4 to);

5.4) $p^{*}=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{T\&prime;}\left(s_i\right)\right],q_{T\&prime;}^{*}=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{T\&prime;}\left(s_i\right)\right],$ Forward step 5.5 to);

5.5) optimum hidden status switch obtains:

5.5.1) variable initialize: make g=T '-1;

5.5.2) backward recursion:

5.5.3) moment upgrades: make g=g-1, if g >=1, return step 5.5.2), otherwise stop.

Further, described step 3. in, the value of cluster number M ' is 4.

Further, described step 4. in, the value of state number N is 3, parameter upgrade period τ ' be 30 seconds, T ' is 10.

Further, described step 6. in, prediction time domain W be 300 seconds.

The present invention has positive effect: (1) the present invention is in the process of boats and ships track real-time estimate, incorporate the impact of enchancement factor, the rolling track prediction scheme adopted can extract the changing condition of extraneous enchancement factor in time, improves the accuracy of boats and ships trajectory predictions.

(2) the present invention is based on different performance index, its boats and ships track real-time estimate result can provide for the multiple boats and ships that there is conflict frees trajectory planning scheme, improves the economy of vessel motion and the utilization factor of sea area resources.

Accompanying drawing explanation

Fig. 1 is the vessel motion short-term Track Pick-up schematic flow sheet in the present invention.

Embodiment

(embodiment 1)

See Fig. 1, a kind of boats and ships track real-time predicting method of the present embodiment comprises following several step:

1. obtain the real-time of boats and ships and historical position information by sea radar, the positional information of each boats and ships is discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'], by application wavelet transformation theory to original discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'] carry out rough handling, thus obtain the denoising discrete two-dimensional position sequence x=[x of boats and ships ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n]: y=[y ₁, y ₂..., y _n]: for given original two dimensional sequence data x '=[x ₁', x ₂' ..., x _n'], utilize the linear representation of following form to be similar to it respectively: $\tilde{f}\left(x^{\&prime;}\right)=\underset{\&ForAll;K}{\underset{\&ForAll;J}{\mathrm{\&Sigma;}}}{c}_{J,K}{\mathrm{\&psi;}}_{J,K}\left(x^{\&prime;}\right),$

Wherein: $\begin{array}{c}{c}_{J,K}={\&Integral;}_{-\&infin;}^{\&infin;}{f}^{\&prime;}\left(x^{\&prime;}\right){\mathrm{\&psi;}}_{J,K}\left(x^{\&prime;}\right){\mathrm{dx}}^{\&prime;}\\ {\mathrm{\&psi;}}_{J,K}\left(x^{\&prime;}\right)=\mathrm{\&delta;}\&CenterDot;\mathrm{\&psi;}(2^J{x}^{\&prime;}-K)\end{array},$

F ' (x ') represents the function expression obtained after data smoothing processing, and ψ (x ') represents female ripple, and δ, J and K are wavelet transformation constant, ψ _{j, K}(x ') represents the transition form of female ripple, c _{j, K}represent the function coefficients obtained by wavelet transform procedure, it embodies wavelet ψ _{j, K}(x '), to the weight size of whole approximation to function, if this coefficient is very little, so it means wavelet ψ _{j, K}the weight of (x ') is also less, thus can under the prerequisite of not influence function key property, by wavelet ψ from approximation to function process _{j, K}(x ') removes; In real data processing procedure, implemented " threshold transition " by setting threshold value χ, work as c _{j, K}during < χ, setting c _{j, K}=0; Choosing of threshold function table adopts the following two kinds mode:

${\mathrm{\&rho;}}_1(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.$ With ${\mathrm{\&rho;}}_2(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d-\left(\frac{d}{\left|d\right|}\mathrm{\&chi;}\right)& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.;$

For y '=[y ₁', y ₂' ..., y _n'], also adopt said method to carry out denoising;

2. in each sampling instant to the pre-service of boats and ships track data, according to the boats and ships original discrete two-dimensional position sequence x=[x that obtains ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n], adopt first order difference method to carry out processing new boats and ships discrete location sequence Δ x=[the Δ x of acquisition to it ₁, Δ x ₂..., Δ x _n-1] and Δ y=[Δ y ₁, Δ y ₂..., Δ y _n-1], wherein Δ x _i=x _i+1-x _i, Δ y _i=y _i+1-y _i(i=1,2 ..., n-1);

3. in each sampling instant to boats and ships track data cluster, to new boats and ships discrete two-dimensional position sequence Δ x and Δ y after process, by setting cluster number M ', adopt K-means clustering algorithm to carry out cluster to it respectively;

4. Hidden Markov Model (HMM) is utilized to carry out parameter training in each sampling instant to boats and ships track data, by the vessel motion track data Δ x after process and Δ y being considered as the aobvious observed reading of hidden Markov models, upgrade period τ ' by setting hidden state number N and parameter, according to nearest T ' individual position detection value and adopt B-W algorithm to roll to obtain up-to-date Hidden Markov Model (HMM) parameter lambda '; Determine that flight path Hidden Markov Model (HMM) parameter lambda '=process of (π, A, B) is as follows:

4.1) variable initialize: application is uniformly distributed to variable π _i, a _ijand b _j(o _k) initialize with and make it meet constraint condition: ${\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&pi;}}_i^0=1,{\mathrm{\&Sigma;}}_{j=1}^N{a}_{\mathrm{ij}}^0=1(1\&le;i\&le;N)$ With ${\mathrm{\&Sigma;}}_{k=1}^{M^{\&prime;}}{b}_j^0\left(o_k\right)=1(1\&le;j\&le;N),$ Obtain λ thus ₀=(π ₀, A ₀, B ₀), wherein o _krepresent a certain aobvious observed reading, π ₀, A ₀and B ₀by element respectively with the matrix formed, makes parameter l=0, o=(o _{t-T '+1}..., o _t-1, o _t) be the individual historical position observed reading of the T ' before current time t;

4.2) E-M algorithm is performed:

4.2.1) E-step: by λ ₁calculate ξ _e(i, j) and γ _e(s _i);

Variable ${\mathrm{\&xi;}}_e(i,j)=\frac{P(q_e=s_i,q_{e+1}=s_j,o|{\mathrm{\&lambda;}}_l)}{P\left(o\right|{\mathrm{\&lambda;}}_l)},$ So ${\mathrm{\&gamma;}}_e\left(s_i\right)={\mathrm{\&Sigma;}}_{j=1}^N{\mathrm{\&xi;}}_e(i,j),$

Wherein s represents a certain hidden state;

4.2.2) M-step: use ${\stackrel{\&OverBar;}{\mathrm{\&pi;}}}_i={\mathrm{\&gamma;}}_1\left(s_i\right),{\stackrel{\&OverBar;}{a}}_{\mathrm{ij}}=\frac{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&xi;}}_e(i,j)}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&gamma;}}_e\left(s_i\right)},{\stackrel{\&OverBar;}{b}}_j\left(o_k\right)=\frac{\underset{o_e=o_k}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}}{\mathrm{\&gamma;}}_e\left(s_j\right)}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}{\mathrm{\&gamma;}}_e\left(s_j\right)}$ Estimate π respectively _i, a _ijand b _j(o _k) and obtain λ thus _l+1;

4.2.3) circulate: l=l+1, repeats E-step and M-step, until π _i, a _ijand b _j(o _k) convergence, namely

wherein parameter ε=0.00001, returns step 4.2.4);

4.2.4): make λ '=λ _l+1, algorithm terminates.

5. in each sampling instant foundation Hidden Markov Model (HMM) parameter, the hidden state q corresponding to Viterbi algorithm acquisition current time observed reading is adopted:

5.1) variable initialize: make g=2, β _{t '}(s _i)=1 (s _i∈ S), δ ₁(s _i)=π _ib _i(o ₁), ψ ₁(s _i)=0, wherein,

${\mathrm{\&delta;}}_g\left(s_i\right)=\underset{q_1,q_2,...,q_{g-1}}{\max }P(q_1,q_2,...,q_{g-1},q_g=s_i,o_1,o_2,...,o_g|{\mathrm{\&lambda;}}^{\&prime;})$

, wherein variable ψ _g(s _j) represent make variable δ _g-1(s _i) a _ijget the hidden state s of ship track of maximal value _i, parameter S represents the set of hidden state;

5.2) recursive process: ${\mathrm{\&delta;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right]b_j\left(o_g\right),{\mathrm{\&psi;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right];$

5.3) moment upgrade: make g=g+1, if g≤T ', return step 5.2), otherwise iteration ends and forward step 5.4 to);

5.4) $p^{*}=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{T\&prime;}\left(s_i\right)\right],q_{T\&prime;}^{*}=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{T\&prime;}\left(s_i\right)\right],$ Forward step 5.5 to);

5.5) optimum hidden status switch obtains:

5.5.1) variable initialize: make g=T '-1;

5.5.2) backward recursion:

5.5.3) moment upgrades: make g=g-1, if g >=1, return step 5.5.2), otherwise stop.。

6. in each sampling instant, by setting prediction time domain W, based on the hidden state q of boats and ships current time, the position prediction value O of future time period boats and ships is obtained

The value of above-mentioned cluster number M ' is 4, the value of state number N is 3, and it is 30 seconds that parameter upgrades period τ ', and T ' is 10, and prediction time domain W is 300 seconds.

(application examples, navigation traffic control method)

The navigation traffic control method of the present embodiment comprises following several step:

Steps A, the boats and ships track real-time predicting method obtained according to embodiment 1 obtain the track of boats and ships boats and ships in the future time period that each sampling instant is inferred;

Step B, in each sampling instant, the running status current based on boats and ships and historical position observation sequence, obtain the numerical value of marine site wind field variable, its detailed process is as follows:

B.1) stop position setting boats and ships is track reference coordinate initial point;

B.2) when boats and ships are in straight running condition and at the uniform velocity turning running status, marine site wind field linear filtering model is built;

B.3) numerical value of wind field variable is obtained according to constructed Filtering Model.

Step C, in each sampling instant, the safety rule collection that need meet when running in marine site based on the running status of each boats and ships and the boats and ships of setting, when likely occurring violating the situation of safety rule when between boats and ships, provide warning information timely to its dynamic behaviour implementing monitoring and for maritime traffic control center;

Step D, when warning information occurs, under the prerequisite meeting boats and ships physical property and marine site traffic rules, by setting optimizing index function and incorporating wind field variable value, Model Predictive Control Theory method is adopted to carry out Rolling Planning to boats and ships collision avoidance track, and program results is transferred to the execution of each boats and ships, its detailed process is as follows:

D.1) termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions time domain γ of boats and ships collision avoidance trajectory planning is set;

D.2) under being set in the prerequisite of given optimizing index function, based on cooperative collision avoidance trajectory planning thought, give different weights by giving each boats and ships and incorporate real-time wind field variable filtering numerical value, obtain the collision avoidance track of each boats and ships and collision avoidance control strategy and program results is transferred to each boats and ships performing, and its first Optimal Control Strategy only implemented by each boats and ships in Rolling Planning interval;

D.3) in next sampling instant, step 5.2 is repeated) until each boats and ships all arrive it free terminal.

Above-mentioned termination reference point locations P is set as the next navigation channel point of vessel position conflict point, and collision avoidance policy control time domain Θ is 300 seconds; Trajectory predictions time domain γ is 300 seconds.

Obviously, above-described embodiment is only for example of the present invention is clearly described, and is not the restriction to embodiments of the present invention.For those of ordinary skill in the field, can also make other changes in different forms on the basis of the above description.Here exhaustive without the need to also giving all embodiments.And these belong to spirit institute's apparent change of extending out of the present invention or change and are still among protection scope of the present invention.

Claims (7)

1. a boats and ships track real-time predicting method, is characterized in that comprising following several step:

1. obtain the real-time of boats and ships and historical position information by sea radar, the positional information of each boats and ships is discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'], by application wavelet transformation theory to original discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'] carry out rough handling, thus obtain the denoising discrete two-dimensional position sequence x=[x of boats and ships ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n];

2. in each sampling instant to the pre-service of boats and ships track data, according to the boats and ships original discrete two-dimensional position sequence x=[x that obtains ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n], adopt first order difference method to carry out processing new boats and ships discrete location sequence Δ x=[the Δ x of acquisition to it ₁, Δ x ₂..., Δ x _n-1] and Δ y=[Δ y ₁, Δ y ₂..., Δ y _n-1], wherein Δ x _i=x _i+1-x _i, Δ y _i=y _i+1-y _i(i=1,2 ..., n-1);

3. in each sampling instant to boats and ships track data cluster, to new boats and ships discrete two-dimensional position sequence Δ x and Δ y after process, by setting cluster number M ', adopt K-means clustering algorithm to carry out cluster to it respectively;

4. Hidden Markov Model (HMM) is utilized to carry out parameter training in each sampling instant to boats and ships track data, by the vessel motion track data Δ x after process and Δ y being considered as the aobvious observed reading of hidden Markov models, upgrade period τ ' by setting hidden state number N and parameter, according to nearest T ' individual position detection value and adopt B-W algorithm to roll to obtain up-to-date Hidden Markov Model (HMM) parameter lambda ';

5. in each sampling instant foundation Hidden Markov Model (HMM) parameter, the hidden state q corresponding to Viterbi algorithm acquisition current time observed reading is adopted;

6. in each sampling instant, by setting prediction time domain W, based on the hidden state q of boats and ships current time, obtain the position prediction value O of future time period boats and ships, thus infer the track to boats and ships in future time period in each sampling instant rolling.

2. a kind of boats and ships track real-time predicting method according to claim 1, is characterized in that: described step 1. in, by application wavelet transformation theory to original discrete two-dimensional position sequence x '=[x ₁', x ₂' ..., x _n'] and y '=[y ₁', y ₂' ..., y _n'] carry out rough handling, thus obtain the denoising discrete two-dimensional position sequence x=[x of boats and ships ₁, x ₂..., x _n] and y=[y ₁, y ₂..., y _n]: for given original two dimensional sequence data x '=[x ₁', x ₂' ..., x _n'], utilize the linear representation of following form to be similar to it respectively:

Wherein: $c_{J,K}={\&Integral;}_{-\&infin;}^{\&infin;}{f}^{\&prime;}\left(x^{\&prime;}\right){\mathrm{\&psi;}}_{J,K}\left(x^{\&prime;}\right){\mathrm{dx}}^{\&prime;},$

ψ _J，K(x′)＝δ·ψ(2 ^Jx′-K)

F ' (x ') represents the function expression obtained after data smoothing processing, and ψ (x ') represents female ripple, and δ, J and K are wavelet transformation constant, ψ _{j, K}(x ') represents the transition form of female ripple, c _{j, K}represent the function coefficients obtained by wavelet transform procedure, it embodies wavelet ψ _{j, K}(x '), to the weight size of whole approximation to function, if this coefficient is very little, so it means wavelet ψ _{j, K}the weight of (x ') is also less, thus can under the prerequisite of not influence function key property, by wavelet ψ from approximation to function process _{j, K}(x ') removes; In real data processing procedure, implemented " threshold transition " by setting threshold value χ, work as c _{j, K}during < χ, setting c _{j, K}=0; Choosing of threshold function table adopts the following two kinds mode:

${\mathrm{\&rho;}}_1(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.$ With ${\mathrm{\&rho;}}_2(d,\mathrm{\&chi;})=\left\{\begin{array}{cc}d-\left(\frac{d}{\left|d\right|}\mathrm{\&chi;}\right)& \mathrm{if}\left|d\right|>\mathrm{\&chi;}\\ 0& \mathrm{if}\left|d\right|\&le;\mathrm{\&chi;}\end{array}\right.;$

For y '=[y ₁', y ₂' ..., y _n'], also adopt said method to carry out denoising.

3. a kind of boats and ships track real-time predicting method according to claim 1 and 2, is characterized in that: described step 4. in determine that flight path Hidden Markov Model (HMM) parameter lambda '=process of (π, A, B) is as follows:

4.1) variable initialize: application is uniformly distributed to variable π _i, a _ijand b _j(o _k) initialize with and make it meet constraint condition: ${\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&pi;}}_i^0=1,$ ${\mathrm{\&Sigma;}}_{j=1}^N{a}_{\mathrm{ij}}^0=1(1\&le;i\&le;N)$ With ${\mathrm{\&Sigma;}}_{k=1}^{M^{\&prime;}}{b}_j^0\left(o_k\right)=1(1\&le;j\&le;N),$ Obtain λ thus ₀=(π ₀, A ₀, B ₀), wherein o _krepresent a certain aobvious observed reading, π ₀, A ₀and B ₀by element respectively with the matrix formed, makes parameter l=0, o=(o _{t-T '+1}..., o _t-1, o _t) be the individual historical position observed reading of the T ' before current time t;

4.2) E-M algorithm is performed:

4.2.1) E-step: by λ _lcalculate ξ _e(i, j) and γ _e(s _i);

Variable ${\mathrm{\&xi;}}_e(i,j)=\frac{P(q_e=s_i,q_{e+1}=s_j,o|{\mathrm{\&lambda;}}_l)}{P\left(o\right|{\mathrm{\&lambda;}}_l)},$ So ${\mathrm{\&gamma;}}_e\left(s_i\right)={\mathrm{\&Sigma;}}_{j=1}^N{\mathrm{\&xi;}}_e(i,j),$

Wherein s represents a certain hidden state;

4.2.2) M-step: use ${\stackrel{\&OverBar;}{\mathrm{\&pi;}}}_i={\mathrm{\&gamma;}}_1\left(s_i\right),$ ${\stackrel{\&OverBar;}{a}}_{\mathrm{ij}}=\frac{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&xi;}}_e(i,j)}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}-1}{\mathrm{\&gamma;}}_e\left(s_i\right)},$ ${\stackrel{\&OverBar;}{b}}_j\left(o_k\right)=\frac{\underset{o_e=o_k}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}{\mathrm{\&gamma;}}_e\left(s_j\right)}}{{\mathrm{\&Sigma;}}_{e=1}^{T^{\&prime;}}{\mathrm{\&gamma;}}_e\left(s_j\right)}$ Estimate π respectively _i, a _ijand b _j(o _k) and obtain λ thus _l+1;

4.2.3) circulate: l=l+1, repeats E-step and M-step, until π _i, a _ijand b _j(o _k) convergence, namely | P (o| λ _l+1)-P (o| λ _l) | < ε, wherein parameter ε=0.00001, return step 4.2.4);

4.2.4): make λ '=λ _l+1, algorithm terminates.

4., according to a kind of boats and ships track real-time predicting method one of claims 1 to 3 Suo Shu, it is characterized in that: described step 5. in determine that the iterative process of the best hidden status switch of ship track is as follows:

5.1) variable initialize: make g=2, β _{t '}(s _i)=1 (s _i∈ S), δ ₁(s _i)=π _ib _i(o ₁), ψ ₁(s _i)=0, wherein,

${\mathrm{\&delta;}}_g\left(s_i\right)=\underset{q_1,q_2,...,q_{g-1}}{\max }P(q_1,q_2,...,q_{g-1},q_g=s_i,o_1,o_2,...,o_g|{\mathrm{\&lambda;}}^{\&prime;}),$

Wherein variable ψ _g(s _j) represent make variable δ _g-1(s _i) a _ijget the hidden state s of ship track of maximal value _i, parameter S represents the set of hidden state;

5.2) recursive process: ${\mathrm{\&delta;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right]b_j\left(o_g\right),$ ${\mathrm{\&psi;}}_g\left(s_j\right)=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{g-1}\left(s_i\right)a_{\mathrm{ij}}\right];$

5.3) moment upgrade: make g=g+1, if g≤T ', return step 5.2), otherwise iteration ends and forward step 5.4 to);

5.4) $p^{*}=\underset{s_i\&Element;S}{\max }\left[{\mathrm{\&delta;}}_{T^{\&prime;}}\left(s_i\right)\right],$ $q_{T^{\&prime;}}^{*}=\underset{s_i\&Element;S}{\arg \max }\left[{\mathrm{\&delta;}}_{T^{\&prime;}}\left(s_i\right)\right],$ Forward step 5.5 to);

5.5) optimum hidden status switch obtains:

5.5.1) variable initialize: make g=T '-1;

5.5.2) backward recursion:

5.5.3) moment upgrades: make g=g-1, if g >=1, return step 5.5.2), otherwise stop.

5., according to a kind of boats and ships track real-time predicting method one of Claims 1-4 Suo Shu, it is characterized in that: described step 3. in, the value of cluster number M ' is 4.

6., according to a kind of boats and ships track real-time predicting method one of claim 1 to 5 Suo Shu, it is characterized in that: described step 4. in, the value of state number N is 3, parameter upgrade period τ ' be 30 seconds, T ' is 10.

7., according to a kind of boats and ships track real-time predicting method one of claim 1 to 6 Suo Shu, it is characterized in that: described step 6. in, prediction time domain W be 300 seconds.