CN111721321A - Intelligent collision avoidance method and system for navigation mark - Google Patents

Intelligent collision avoidance method and system for navigation mark Download PDF

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Publication number
CN111721321A
CN111721321A CN202010427248.1A CN202010427248A CN111721321A CN 111721321 A CN111721321 A CN 111721321A CN 202010427248 A CN202010427248 A CN 202010427248A CN 111721321 A CN111721321 A CN 111721321A
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ship
time
navigation mark
track
collision
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CN111721321B (en
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李锋
甄涛
江道伟
陈国伟
谢奎
邵哲平
洪长华
薛晗
孙洪波
洪炜煜
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Shanghai Aids To Navigation Department Of Donghai Navigation Safety Administration Mot
Jimei University
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Shanghai Aids To Navigation Department Of Donghai Navigation Safety Administration Mot
Jimei University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/22—Plotting boards

Abstract

The invention discloses an intelligent navigation mark collision avoidance method, which realizes intelligent prediction and early warning of ship-navigation mark collision risks by predicting ship tracks, constructing a 3D scene and estimating collision risks; predicting the ship track, namely predicting the ship position, the heading and the track of the ship characteristic points according to the ship characteristic point model and the real-time and historical ship position and heading of the ship, wherein the ship track is predicted according to a Kalman filtering method; constructing a 3D scene, and performing three-dimensional visual display on the cloth of the navigation mark, wherein the three-dimensional visual display comprises a schematic diagram of the cloth of the navigation mark; and (4) estimating collision risks, namely calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, estimating collision risks in real time, and prejudging whether the ship and the navigation mark collide. The invention also discloses a device based on the method. The invention has the advantages that: the intelligent prediction and early warning of the collision risk of the ship and the navigation mark can be effectively realized.

Description

Intelligent collision avoidance method and system for navigation mark
Technical Field
The invention relates to the technical field of ship track prediction, in particular to an intelligent navigation mark collision avoidance method and system based on oil gas.
Background
In the prior art, various intelligent prediction algorithms are adopted to predict the navigation track of a ship in the field of navigation prediction and control of the ship, so that the navigation safety of the ship is ensured. In the prior art, a method for predicting a ship navigation track adopts a Kalman filter for calculation. However, the conventional kalman filter uses the same sampling interval throughout the filtering process. However, as AIS location update reporting intervals vary with ship speeds, communication between coastal stations and buoys is blocked and network delays are incurred, and sometimes correct location data may not be received or the location reports are not updated in a timely manner. In the prior art, a Kalman filter is introduced to predict the ship track by adopting a self-adaptive mechanism, but the problems of untimely updating of position data or large estimation error still exist.
In addition, in the prior art, especially in the field of navigation prediction of navigation marks and ships, a method for visualizing the navigation marks and intelligently predicting and early warning the collision risk of the ships and the navigation marks is lacked.
Disclosure of Invention
The invention aims to solve the technical problem of how to realize intelligent prediction and early warning of the collision risk of a ship and a navigation mark, and provides an intelligent navigation mark collision avoidance method and system aiming at the technical problem to be solved.
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent navigation mark collision avoidance method realizes intelligent prediction and early warning of ship-navigation mark collision risks by predicting ship tracks, constructing a 3D scene and estimating collision risks;
predicting a ship track, namely predicting the ship position, heading and track of the ship characteristic points according to the ship characteristic point model and the real-time and historical ship position and heading of the ship, wherein the ship track is predicted according to a Kalman filtering method, and different time scales are introduced into the Kalman filtering method;
constructing a 3D scene, and performing three-dimensional visual display on the cloth of the navigation mark, wherein the three-dimensional visual display comprises a schematic diagram of the navigation mark cloth;
and (4) estimating collision risks, namely calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, estimating collision risks in real time, and prejudging whether the ship and the navigation mark collide.
Further, the mathematical model of the ship motion system adopted in the process of predicting the ship track is as follows:
Figure BDA0002499152060000021
Figure BDA0002499152060000022
Figure BDA0002499152060000023
Figure BDA0002499152060000024
wherein x is a state variable and u (k) is time kInput vector, wkSystem state noise at time k, k is the time sequence count, T (k) is the kth time interval of the AIS location update report, zkIs the output of the measurement for time k,kfor the noise of the measurement at time k,
Figure BDA0002499152060000025
denotes the error at time K +1, Kk+1Denotes the gain of the Kalman filtering at time k +1, x (k) denotes the state variable at time k,
Figure BDA0002499152060000026
the average value of the time interval is represented,
Figure BDA0002499152060000027
represents a measurement of the dynamics at the moment k, v (k +1) represents the speed of the vessel at the moment k +1,
Figure BDA0002499152060000028
covariance, P, representing the estimation error of k +1 timekDenotes the covariance of the error at time k, E denotes the mean, QkRepresenting the covariance of the system noise at time k.
Further, the covariance of the system noise at time k is calculated according to an adaptive mechanism for selecting the state noise covariance, and the calculation model is as follows:
Figure BDA0002499152060000029
wherein β is forgetting factor, β has a value range of 0.95<β<0.99，λkIs the gain factor at time k.
Further, the longitude and latitude of the ship at the instant of the k +1th time interval in the process of predicting the ship track can be calculated as follows:
Figure BDA00024991520600000210
λ(k+1)＝λ(k)+v(k)T(k)sin(θ(k))
the system state equation is:
x(k+1)＝x(k)+T(k)u(k)+w(k)
the system observations are:
z(k+1)＝x(k+1)+(k+1)
where x is a state variable, u (k) is an input vector at time k, wkSystem state noise at time k, k is the time sequence count, T (k) is the kth time interval of the AIS location update report, zkIs the output of the measurement for time k,kis the measurement noise for time k.
Further, the collision risk estimation process includes the steps of:
s01, calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, wherein the ship track is set as Q1Q2Observation line is P1P2；
s02, for quick repulsion test, set line segment P1P2The diagonal rectangle is R, and a line segment Q is set1Q2The diagonal rectangle is T, if R and T are not intersected, the two segments cannot be intersected, and the two tracks cannot be collided;
s03, performing a straddle test, wherein if two line segments intersect, the two line segments inevitably straddle each other, and if P is1P2Straddle Q1Q2Then vector P1And P2Two points are located at Q2And Q1The two sides of the straight line where the two tracks are located can not collide if the two tracks do not pass the straddle test, and the two tracks can not collide if the two tracks pass the straddle test.
The invention also aims to provide an intelligent collision avoidance system for a navigation mark, which comprises a ship track prediction device, a 3D scene display device and a collision risk estimation device, wherein the ship track prediction device predicts the ship track according to the method, the 3D scene display device performs three-dimensional visual display on the arrangement of the navigation mark according to the method, and the collision risk estimation device estimates the collision risk of the ship and the navigation mark according to the method.
Further, the 3D scene display device is a mobile terminal provided with a 3D scene display system based on a 3D max modeling tool and UNITY 3D release, and the mobile terminal includes a mobile terminal based on IOS, ANDROID, PC, WEB, PS3, and XBOX.
Compared with the prior art, the invention has the beneficial effects that:
the invention designs a self-adaptive variable parameter time standard Kalman filter by introducing different time scales in Kalman filtering, and estimates the buoy drift of different report intervals by adopting APVTS-KF. Therefore, the Kalman filtering can more accurately solve the drift estimation problem under the actual condition of the buoy dynamic interval position report. Before reaching steady state, a larger state noise covariance is selected to accelerate convergence speed. After reaching steady state, convergence accuracy can be improved by selecting a smaller state noise covariance. Under different working states, the noise covariance of the Kalman filter is adaptively updated and matched, rapid convergence and high estimation accuracy can be simultaneously obtained under various working environments, and therefore the trajectory prediction that the sampling release updating period of the AIS position changes with the dynamic uncertainty of the ship speed can be processed. In addition, the navigation mark is displayed in a three-dimensional view mode, so that navigation mark configuration can be comprehensively informed to navigation mark users, ship navigation is greatly facilitated, collision risk intelligent prediction and early warning are carried out through the terminal, collision can be effectively avoided, and ship danger avoidance can be informed in advance.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present embodiment discloses an intelligent collision avoidance method for a navigation mark, which realizes intelligent prediction and early warning of a collision risk between a ship and a navigation mark by predicting a ship track, constructing a 3D scene, and estimating the collision risk;
the method comprises the steps of predicting ship tracks, predicting positions, heading and tracks of ship feature points according to ship feature point models and ship real-time and historical positions and heading of ships, wherein the ship tracks are predicted according to a Kalman filtering method, and different time scales are introduced into the Kalman filtering method.
Feasible, the filtering method of the kalman filter adopted in the present embodiment is as follows, where the mathematical model of the ship motion system adopted in the process of predicting the ship track is:
Figure BDA0002499152060000041
Figure BDA0002499152060000042
Figure BDA0002499152060000043
Figure BDA0002499152060000044
where x is a state variable, u (k) is an input vector at time k, wkSystem state noise at time k, k is the time sequence count, T (k) is the kth time interval of the AIS location update report, zkIs the output of the measurement for time k,kfor the noise of the measurement at time k,
Figure BDA0002499152060000045
denotes the error at time K +1, Kk+1Denotes the gain of the Kalman filtering at time k +1, x (k) denotes the state variable at time k,
Figure BDA0002499152060000046
the average value of the time interval is represented,
Figure BDA0002499152060000047
a measurement representing the dynamics at time k, v (k +1) representing the speed of the vessel at time k +1,
Figure BDA0002499152060000051
covariance, P, representing the estimation error of k +1 timekDenotes the covariance of the error at time k, E denotes the mean, QkRepresenting the covariance of the system noise at time k.
The covariance of the system noise at time k is calculated according to the adaptive mechanism of the selected state noise covariance, and the calculation model is
Figure BDA0002499152060000052
Wherein β is forgetting factor, β has a value range of 0.95<β<0.99，λkIs the gain factor at time k. Before reaching steady state, lambdakThis results in a larger covariance of the state noise, which speeds up convergence. After reaching steady state, λkIs smaller, which results in a smaller state noise covariance to improve convergence accuracy. Adaptive strategy influences Q in mathematical model of ship motion system by adjustingkTo adjust the covariance of the estimation error
Figure BDA0002499152060000053
The setting and proving process of the mathematical model of the ship motion system adopted in the process of measuring the ship track is as follows:
in this embodiment, the motion model of the ship is expressed as follows:
t (k) is defined as the kth time interval of the AIS location update report.
Figure BDA0002499152060000054
Indicating the latitude of the vessel. λ (k) represents the longitude of the ship. v (k) represents the speed of the ship. θ (k) represents the heading of the vessel. Floating bodyThe latitude and longitude marked at the instant of time k +1th can be calculated as:
Figure BDA0002499152060000055
λ(k+1)＝λ(k)+v(k)T(k)sin(θ(k)) (2)
define x as a state variable:
Figure BDA0002499152060000056
define u (k) as the input vector:
Figure BDA0002499152060000057
consider whether noise is present in an actual system. Defined as the system noise. The system state equations of equations (1) and (2) can be written as:
x(k+1)＝x(k)+T(k)u(k)+w(k) (5)
define z as the measurement vector. Defined as measurement noise. The system observations can be written as:
z(k+1)＝x(k+1)+(k+1) (6)
the ship track prediction definition and the certification based on the adaptive variable parameter time scale Kalman filter are as follows:
definition of
Figure BDA0002499152060000061
State estimation of x (k), i.e.
Figure BDA0002499152060000062
Definition of
Figure BDA0002499152060000063
Is the average value of T (k), i.e.
Figure BDA0002499152060000064
The Kalman filtering is realized by two steps of time updating and measurement updating. The time update step predicts an estimate of the state, the time going one step forward.
Figure BDA0002499152060000065
Expressed as a posteriori estimate of the state at time k, calculated as follows
Figure BDA0002499152060000066
The measurement update step determines a posteriori estimates of time k
Figure BDA00024991520600000617
Measurement of state dynamics
Figure BDA0002499152060000067
Can be updated as follows
Figure BDA0002499152060000068
Where K is the kalman filter gain. Error of the k-th time
Figure BDA0002499152060000069
Given by:
Figure BDA00024991520600000610
it is possible to set the ratio of hypothesis 1: e [ w ]k]0. system state noise wkAnd the measurement output zkIs irrelevant. QkRepresenting the covariance of the system noise at time k, is calculated as follows:
Figure BDA00024991520600000611
assume 2: e2k]0. measurement noisekAnd xkAnd not related.
Figure BDA00024991520600000612
Representing the covariance of the measurement noise at time k + 1.
Figure BDA00024991520600000613
Preferably, in the present embodiment, for the system state equation (5) containing system state noise and the system observation equation (6) containing measurement noise, the adaptive variable parameter time scale kalman filtering method is given by equations (14) to (17).
Figure BDA00024991520600000614
Figure BDA00024991520600000615
Figure BDA00024991520600000616
Figure BDA0002499152060000071
In the formula
Figure BDA0002499152060000072
Covariance of the estimation error at time k. PkIs the covariance of the error at time k.
The proof procedure in this particular example is possible as follows:
substituting (5) into (11) can obtain:
Figure BDA0002499152060000073
substituting (10) into (18) yields:
Figure BDA0002499152060000074
substituting (6) into (19) yields:
Figure BDA0002499152060000075
substituting (5) into (20) yields:
Figure BDA0002499152060000076
substituting (9) into (21) yields:
Figure BDA0002499152060000077
the formula (14) is proved.
Figure BDA0002499152060000078
The calculation is as follows:
Figure BDA0002499152060000079
substituting (5) into (23) yields:
Figure BDA00024991520600000710
substituting (9) into (24) yields:
Figure BDA00024991520600000711
from hypothesis 1 we can derive:
Figure BDA0002499152060000081
the pattern of formula (15) is shown.
Figure BDA0002499152060000082
Substituting (10) into (27) yields:
Figure BDA0002499152060000083
substituting (6) into (27) yields:
Figure BDA0002499152060000084
from hypothesis 2 we can derive:
Figure BDA0002499152060000085
substituting (23) into (30) yields:
Figure BDA0002499152060000086
equation (16) is obtained. To (31) regarding Kk+1The derivation can be:
Figure BDA0002499152060000087
namely:
Figure BDA0002499152060000088
the following therefore holds:
Figure BDA0002499152060000089
the formula (17) is shown.
Substituting (34) into (31) yields:
Figure BDA0002499152060000091
when the state noise covariance is small, the convergence speed is low and the convergence accuracy is high. If the state motion changes too fast in a short time, the slow convergence speed easily leads to tracking failure. When the state noise covariance is large, the convergence speed is high and the precision is low. Thus, the noise covariance in the kalman filter can be adaptively updated and matched under different operating conditions. The adaptive Kalman filter can simultaneously obtain quick convergence and high estimation accuracy under a large-range working environment. In this embodiment, an adaptive mechanism for selecting the covariance of the state noise is adopted, which specifically includes the following steps:
Figure BDA0002499152060000092
Figure BDA0002499152060000093
wherein 0.95<β<0.99 is a forgetting factor, λkIs the gain factor at time k. Before reaching steady state, lambdakLarger, which results in larger covariance of the state noise, thereby speeding up convergence. After reaching steady state, λkIs smaller, which results in a smaller state noise covariance to improve convergence accuracy. The adaptive strategy affects Q in equation (15) by adjustingkTo adjust the covariance of the estimation error
Figure BDA0002499152060000094
In addition, the method also comprises the steps of carrying out three-dimensional visual display on the cloth of the navigation mark by constructing a 3D scene, wherein the three-dimensional visual display comprises a schematic diagram of the cloth of the navigation mark; preferably, the three-dimensional visual display can be performed through a 3D scene display device, the 3D scene display device is a mobile terminal provided with a 3D scene display system based on a 3D max modeling tool and a UNITY 3D release, and the mobile terminal includes a mobile terminal based on IOS, ANDROID, PC, WEB, PS3 and XBOX.
The system introduces the arrangement of the navigation mark in a three-dimensional view mode, and informs the navigation mark user of the concise and comprehensive arrangement intention of the navigation mark, thereby greatly facilitating the navigation of the ship. The arrangement of the navigation mark generally has three important positions: (1) harbor doors, such as a township corner light tower, are located in the south of the mansion door island at about 9 nautical miles; (2) a turning point, which needs an open water area for turning because the turning radius of the ship is large; (3) dangerous locations such as reef reefs, shoals, work areas, obstructions, bridges, docks, etc.
The system can complete a plurality of navigation aid functions of marking obstructive objects or dangerous objects, indicating the boundary of a navigation channel and the like, remind the ship to drive cautiously, help the ship to distinguish the boundary of the navigation channel, inform steering or obstructive objects, guide the ship to avoid shoals and reefs, distinguish the branches and junctions of the navigation section, correctly navigate, position and steer and safely pass through the navigation channel. The distance and the direction of the key navigation mark are reminded, and the distance and the direction between the ship position and the key navigation mark are displayed in real time in the ship driving process, so that a user can conveniently determine the navigation direction in time. When the ship approaches a certain navigation mark, the system will prompt the user to flash by light and inform the user of the information transmitted by the navigation mark in a form of characters and graphic representation.
The platform of the UNITY 3D game engine is applied using a 3D Max modeling tool. The Unity 3D supports rapid release of a plurality of platforms including IOS, ANDROID, PC, WEB, PS3, XBOX and the like, and provides a wide development space for popularization of a three-dimensional system to the plurality of platforms.
And (4) estimating collision risks, namely calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, estimating collision risks in real time, and prejudging whether the ship and the navigation mark collide.
Preferably, the collision risk estimation process includes the steps of:
s01, calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, wherein the ship track is set as Q1Q2Observation line is P1P2；
s02, for quick repulsion test, set line segment P1P2The diagonal rectangle is R, and a line segment Q is set1Q2If the R and the T do not intersect, the two line segments cannot intersect, and the two tracks are judged not to collide;
s03, performing a straddle test, wherein if two line segments intersect, the two line segments inevitably straddle each other, and if P is1P2Straddle Q1Q2Then vector P1And P2Two points are located at Q2And Q1The two sides of the straight line where the two tracks are located can not collide if the two tracks do not pass the straddle test, and the two tracks can not collide if the two tracks pass the straddle test.
Setting × represents cross multiplication and dot multiplication, and judging Q1Q2Straddle P1P2The basis of (A) is as follows:
(Q1P1×Q1Q2)*(Q1Q2×Q1P2)>0 (38)
(P1Q1×P1P2)*(P1P2×P1Q2)>0 (39)
according to the method, the ship motion track is predicted by adopting a Kalman filtering technology, the accuracy and the robustness of a Kalman filtering algorithm are improved, the Kalman filtering is popularized from an integer order system to a fractional order system, and a Kalman filtering algorithm based on a self-adaptive fractional order system is deduced. The adaptive mechanism of the state noise covariance selection deduces the mathematical process of the adaptive mechanism. Compared with an integer order filter, the fractional order Kalman filter has a wider parameter selection range and higher tracking precision. Kalman filtering based on adaptive fractional order has smaller central position error and better tracking precision than fractional Kalman filtering and Kalman filtering. The adaptive fractional order Kalman can better avoid the drift effect of the tracker, and has good robustness and effectiveness.
The method can be arranged in a navigation mark intelligent collision avoidance system, the system can be a shipborne mobile terminal and other equipment, has a communication function, communicates information with the AIS in real time, and can comprise a ship track prediction device, a 3D scene display device and a collision risk estimation device, wherein the ship track prediction device predicts a ship track, the 3D scene display device displays the arrangement of the navigation mark in a three-dimensional view, and the collision risk estimation device estimates the collision risk of the ship and the navigation mark. In addition, the results of system prediction, display and estimation can be displayed through a display device, and the results can be displayed on a display terminal on board, such as a terminal display screen, so that the results can be conveniently viewed by a user.
The invention designs a self-adaptive variable parameter time standard Kalman filter by introducing different time scales in Kalman filtering, and estimates the buoy drift of different report intervals by adopting APVTS-KF. Therefore, the Kalman filtering can more accurately solve the drift estimation problem under the actual condition of the buoy dynamic interval position report. Before reaching steady state, a larger state noise covariance is selected to accelerate convergence speed. After reaching steady state, convergence accuracy can be improved by selecting a smaller state noise covariance. Under different working states, the noise covariance of the Kalman filter is adaptively updated and matched, and rapid convergence and high estimation accuracy can be simultaneously obtained under various working environments. In addition, the navigation mark is displayed in a three-dimensional view mode, so that navigation mark configuration can be comprehensively informed to navigation mark users, ship navigation is greatly facilitated, collision risk intelligent prediction and early warning are carried out through the terminal, collision can be effectively avoided, and ship danger avoidance is informed in advance.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, and the scope of protection is still within the scope of the invention.

Claims (7)

1. An intelligent navigation mark collision avoidance method is characterized in that intelligent prediction and early warning of ship-navigation mark collision risks are realized by predicting ship tracks, constructing a 3D scene and estimating collision risks;
predicting a ship track, namely predicting the ship position, heading and track of the ship characteristic points according to the ship characteristic point model and the real-time and historical ship position and heading of the ship, wherein the ship track is predicted according to a Kalman filtering method, and different time scales are introduced into the Kalman filtering method;
constructing a 3D scene, and performing three-dimensional visual display on the cloth of the navigation mark, wherein the three-dimensional visual display comprises a schematic diagram of the cloth of the navigation mark;
and (4) estimating collision risks, namely calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, estimating collision risks in real time, and prejudging whether the ship and the navigation mark collide.
2. The intelligent navigation mark collision avoidance method according to claim 1, wherein the mathematical model of the ship motion system adopted in the process of predicting the ship track is as follows:
Figure FDA0002499152050000011
Figure FDA0002499152050000012
Figure FDA0002499152050000013
Figure FDA0002499152050000014
where x is a state variable, u (k) is an input vector at time k, wkSystem state noise at time k, k is the time sequence count, T (k) is the kth time interval of the AIS location update report, zkIs the output of the measurement for time k,kfor the noise of the measurement at time k,
Figure FDA0002499152050000015
denotes the error at time K +1, Kk+1Denotes the gain of the Kalman filtering at time k +1, x (k) denotes the state variable at time k,
Figure FDA0002499152050000016
the average value of the time interval is represented,
Figure FDA0002499152050000017
represents a measurement of the dynamics at the moment k, v (k +1) represents the speed of the vessel at the moment k +1,
Figure FDA0002499152050000018
covariance, P, representing the estimation error of k +1 timekDenotes the covariance of the error at time k, E denotes the mean, QkRepresenting the covariance of the system noise at time k.
3. The intelligent beacon collision avoidance method of claim 2, wherein the covariance of the system noise at time k is calculated according to an adaptive mechanism that selects the state noise covariance, and the calculation model is as follows:
Figure FDA0002499152050000019
wherein β is forgetting factor, β has a value range of 0.95<β<0.99，λkIs the gain factor at time k.
4. The intelligent navigation mark collision avoidance method according to claim 3, wherein the longitude and latitude of the ship at the instant of the k +1th time interval in the process of predicting the ship track can be calculated as:
Figure FDA0002499152050000021
λ(k+1)＝λ(k)+v(k)T(k)sin(θ(k))
the system state equation is:
x(k+1)＝x(k)+T(k)u(k)+w(k)
the system observations are:
z(k+1)＝x(k+1)+(k+1)
where x is a state variable, u (k) is an input vector at time k, wkSystem state noise at time k, k is the time sequence count, T (k) is the kth time interval of the AIS location update report, zkAt time kThe output of the measurement is taken and,kis the measurement noise at time k.
5. The intelligent navigation mark collision avoidance method according to claim 1, wherein the collision risk estimation process comprises the following steps:
s01, calculating the nearest meeting distance between the navigation mark and the ship characteristic point model and the driving distance required by the ship to reach the meeting point, wherein the ship track is set as Q1Q2Observation line is P1P2；
s02, for quick repulsion test, set line segment P1P2The diagonal rectangle is R, and a line segment Q is set1Q2The diagonal rectangle is T, if R and T are not intersected, the two segments cannot be intersected, and the two tracks cannot be collided;
s03, performing a straddle test, wherein if two line segments intersect, the two line segments inevitably straddle each other, and if P is1P2Straddle Q1Q2Then vector P1And P2Two points are located at Q2And Q1The two sides of the straight line where the two tracks are located can not collide if the two tracks do not pass the straddle test, and the two tracks can not collide if the two tracks pass the straddle test.
6. An intelligent navigation mark collision avoidance system, which is characterized by comprising a ship track prediction device, a 3D scene display device and a collision risk estimation device, wherein the ship track prediction device predicts a ship track according to the method of any one of claims 1 to 4, the 3D scene display device displays the arrangement of navigation marks in a three-dimensional view according to the method of claim 1, and the collision risk estimation device estimates the collision risk of a ship and the navigation marks according to the method of claim 1 or 5.
7. The intelligent navigation mark collision avoidance system of claim 6, wherein the 3D scene display device is a mobile terminal equipped with a 3D scene display system based on 3D Max modeling tool and UNITY 3D publishing, and the mobile terminal comprises IOS, ANDROID, PC, WEB, PS3 and XBOX based mobile terminals.
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