CN111157982A - Intelligent ship and shore cooperative target tracking system and method based on shore-based radar - Google Patents
Intelligent ship and shore cooperative target tracking system and method based on shore-based radar Download PDFInfo
- Publication number
- CN111157982A CN111157982A CN201911140774.3A CN201911140774A CN111157982A CN 111157982 A CN111157982 A CN 111157982A CN 201911140774 A CN201911140774 A CN 201911140774A CN 111157982 A CN111157982 A CN 111157982A
- Authority
- CN
- China
- Prior art keywords
- ship
- shore
- track
- target
- intelligent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 claims abstract description 73
- 238000005457 optimization Methods 0.000 claims abstract description 19
- 230000004927 fusion Effects 0.000 claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims description 22
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000007405 data analysis Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 238000009499 grossing Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
-
- 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
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
-
- 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
- G01C21/203—Specially adapted for sailing ships
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention relates to a shore-based radar-based intelligent ship and shore cooperative target tracking system and method, which are applied to the advanced technical field of intelligent ships and realize accurate tracking of target ships in a specified sea area. The system comprises: the system comprises a shore-based radar arranged on a shore line, an intelligent ship monitoring system, a data fusion system, a track prediction system and a track optimization system, wherein the intelligent ship monitoring system, the data fusion system, the track prediction system and the track optimization system are positioned on an intelligent ship; analyzing and processing the fused monitoring data by a track prediction system to obtain a predicted sailing track of the target ship; the flight path optimization system performs optimization processing on the predicted navigation path to obtain an optimized predicted path of the target ship, so that the intelligent ship performs target tracking on the target ship according to the optimized predicted path; the target tracking system and the target tracking method acquire the accurate track of the target ship by means of a shore-based radar and an intelligent ship monitoring system.
Description
Technical Field
The invention belongs to the technical field of intelligent ship track tracking, and particularly relates to a system and a method for tracking a ship-shore cooperative target of an intelligent ship based on a shore-based radar.
Background
With the rapid development of modern science and technology, the supervision of the water shipping safety is gradually advanced to the informationization, networking and intellectualization directions. The development of shipping industry increases the types and the quantity of ships, and the sea traffic density and the loading capacity of dangerous goods are also increased continuously, so that sea damage accidents are easily caused, and the navigation safety and the marine ecological environment of the ships are seriously threatened. Therefore, the timely tracking and prediction of the ship navigation track in the monitored sea area are effectively realized, and the method is an important technical support for the early warning of the marine traffic accident and has important significance on the ship navigation safety and the marine ecological environment.
At present, a common ship radar is combined with a computer technology, so that the ship radar can be used for tracking and detecting a ship target on the sea, and the course, distance, speed, direction, nearest meeting distance and nearest meeting time of the target ship can be measured; sea targets may also be monitored autonomously. However, the marine radar also has technical disadvantages: because of the limit of the detection distance, a blind area exists in the detection process; because the radar is used for detecting electromagnetic waves, when a non-target obstacle is encountered, the reflected information is influenced; in addition, the measurement precision of the ship radar is not high, and when multiple targets appear, the targets are easy to lose or tracking errors occur; and finally, the ship radar is greatly influenced by weather.
The ship AIS system can provide various information of ships, including static information such as ship call numbers, ship types, ship names, ship lengths and ship widths. Dynamic information such as ship position, ship speed, course and the like. Compared with radar information, the AIS can better track ship information in a dense ship place according to different MMSI of a target ship; AIS works in VHF frequency band, and wave diffraction ability is strong, does not receive distance, position influence, compares with radar detection, also has almost no weather restriction. However, the AIS detection method is passive, only receiving is performed without transmitting, and the received position belongs to the point information position of the AIS equipment, and the shape and size of the ship cannot be reflected.
At present, the prediction of the navigation track of a ship is mainly focused on the prediction of the navigation track of the ship by adopting a ship radar and a ship AIS system, and the prediction method can ensure that the ship navigates according to the predicted air route and ensure the navigation safety of the ship.
The shore-based radar has the main functions of identifying and tracking the navigation state of the ship in the monitored sea area and acquiring ship data information in real time. In addition, compared with ship equipment, the shore-based radar is not easily influenced by environmental factors and has strong anti-interference capability; the detection cost is low, and the target can be detected in a large range in all weather; and a shore-based radar is arranged along a coastline, so that a net-shaped monitoring system in a navigation channel and a sea area can be formed. At present, shore end monitoring equipment is mainly used for monitoring, identifying and positioning a target ship in real time, tracking the navigation track of the target ship and further early warning dangerous behaviors of the ship.
However, both of the above methods cannot realize the function of predicting the sailing trajectory of the target ship, and have limitations.
Disclosure of Invention
Technical problem to be solved
In the sailing state of the ship, the collision danger is the most common type of marine traffic accidents, and huge economic loss and environmental pollution are easily caused. Therefore, prediction and tracking of the sailing track of the target ship are important research contents for ensuring the sailing safety of the ship. The current position information, the ship navigation speed information and the ship navigation course information of the target ship are monitored, the navigation track of the target ship in a period of time in the future is calculated, and the navigation track is tracked in real time.
The prediction of the ship sailing track mainly comprises time prediction and position prediction, wherein the most important prediction is the prediction of the ship sailing position. The prediction of the ship navigation position refers to the prediction of the navigation track of the target ship in a period of time in the future by monitoring the current position, the course, the ship speed and other information of the target ship.
The data of the shore-based radar and the intelligent ship monitoring system are fused, and information received by various sensors needs to be fused under the same condition. However, in the prior art, the shore-based radar and the ship AIS system have different information acquisition modes and have differences in coordinates and time, so that data of the two systems need to be processed, and the data are guaranteed to be uniform in time and space.
In addition, in the calculation process of the target tracking algorithm, the sensing data comprise complex noise and clutter from the ocean surface, the measured data comprise a large amount of sea clutter, certain deviation also exists between the predicted track and the real sailing track of the target ship, in addition, the effect of the original data is weakened while new data is added, and therefore, the accuracy rate of the time-varying system is improved. To solve this problem, further sophisticated optimization of the target tracking method is required.
Aiming at the problems, the system and the method for tracking the ship-shore cooperative target of the intelligent ship based on the shore-based radar are provided.
(II) technical scheme
The present invention is directed to solving, at least to some extent, one of the technical problems in the art described above. Therefore, an object of the present invention is to provide an intelligent ship-shore cooperative target tracking system based on a shore-based radar, which is characterized by comprising: the system comprises a shore-based radar arranged on a shore line, an intelligent ship monitoring system, a data fusion system, a track prediction system and a track optimization system, wherein the intelligent ship monitoring system, the data fusion system, the track prediction system and the track optimization system are positioned on an intelligent ship;
the shore-based radar acquires monitoring data of the shore-based radar;
the intelligent ship monitoring system acquires ship monitoring data;
the data fusion system fuses the shore-based radar monitoring data and the intelligent ship monitoring data to obtain fused monitoring data about a target ship;
the track prediction system analyzes and processes the fusion monitoring data to obtain a predicted navigation track of the target ship;
and the track optimization system further optimizes the predicted navigation track to obtain a ship navigation track tracked by the ship-shore cooperative target, so that the intelligent ship can track the target ship according to the optimized predicted track.
As further preferable in the present technical solution: the number of the shore-based radars is multiple, and a mesh monitoring system in a channel and a sea area is formed; the intelligent ship monitoring system comprises an intelligent ship radar and an intelligent ship AIS system.
As further preferable in the present technical solution: the shore-based radar monitoring data and the smart ship monitoring data comprise ship positions and ship speeds.
As further preferable in the present technical solution: the data fusion system carries out interpolation calculation on the ship monitoring data and calculates a data approximate value of a vacant time period, so that the ship monitoring data and the shore-based radar monitoring data are matched in time; and according to the mercator projection principle, converting the longitude and latitude coordinates of the ship into coordinates on a planar mercator chart, so that ship monitoring data and shore-based radar monitoring data are converted into a same planar coordinate system in space.
As further preferable in the present technical solution: the track prediction system tracks and predicts the navigation track of the target ship by adopting a mode of linear estimation of a state sequence of the dynamic system to obtain the predicted navigation track of the target ship.
As further preferable in the present technical solution: the track prediction system describes a dynamic system through a state equation and an observation equation, wherein the state equation and the observation equation are as follows:
the state equation is as follows:
s(k)＝As(k-1)+u(k)
the observation equation:
z(k)＝Hs(k)+v(k)，
wherein the state value matrix is s (k) ═ x, vx,y,vy]The horizontal axis coordinate, the velocity component in the horizontal axis direction, the vertical axis coordinate and the velocity component in the vertical axis direction of the track are respectively represented, and k represents the (0,1,2 …) time;
observed value is [ x, y]TRepresenting the coordinates of the horizontal and vertical axes of the observed track, namely longitude and latitude, and z (k) is an observed value at the moment k; a is a system state transition matrix, and H is an observation matrix;
u (k), v (k) represent the state and observed noise, respectively, assumed to be gaussian noise, where v (k) N (0, Q), u (k) N (0, R), Q and R are relative, indicating whether the observation or prediction is more believable.
As further preferable in the present technical solution: the track prediction system further adopts the following equation in a target tracking prediction calculation method adopting dynamic system data analysis:
and (3) predicting the system state:
s(k|k-1)＝As(k-1|k-1)
error covariance prediction:
P(k|k-1)＝AP(k-1|k-1)A′+Q
gain coefficient equation:
K＝P(k|k-1)H′×(HP(k|k-1)H′+R)
and (3) updating the system state:
s(k|k)＝s(k|k-1)+K(Z(k)-Hs(k|k-1))
updating the prior covariance:
P(k|k)＝(1-kH)P(k|k-1)
in the formula: p is the covariance of the state at the moment, H is the measurement system parameter, A is the system parameter, and the values of the matrixes A and H are as follows:
as further preferable in the present technical solution: the track prediction system further adopts the following mode in a target tracking prediction calculation method adopting dynamic system data analysis:
first, the initial state is confirmed, and the state matrix is:
wherein, the state component adopts a matrix of 1 × 4 dimension, which respectively represents the x-axis position, the x-axis direction velocity component, the y-axis position and the y-axis direction velocity component; x is the number of1，y1Represents the position of the abscissa, x, at the first moment2，y2Representing the position of a horizontal coordinate and a vertical coordinate at a second moment, and t represents the time interval between the first moment and the second moment;
next, the initialization of the mean square error is confirmed:
wherein S is a state value at a certain time,
in order to be the state prediction value,
the variance of the observed noise on each component is taken as T, and T is the time length of one period of the scanning data;
assigning values to the parameters, wherein the state vector S is given by an initial flight path; the period T in the initial covariance P is 60s, and the covariance σP、σθAre all set to 100; and setting Q and R into diagonal matrixes with small diagonal element values to ensure that the operation time is full.
As further preferable in the present technical solution: the track optimization system optimizes the position and the speed of the ship and estimates the signals according to the following equation:
s(k)＝s(k|k-1)+α[x(k)-s(k|k-1)]
v(k)＝v(k|k-1)+β[x(k)-s(k|k-1)]/T
where s (k) is the coordinate of the kth time, s (k | k-1) is the predicted coordinate of the kth time calculated during the (k-1) th time, x (k) is the measured coordinate of the kth time, v (k) is the velocity estimate at the kth time, v (k | k-1) is the predicted velocity at the kth time calculated during the (k-1) th time, T is the time period of the scan, α and β are smoothing coefficients of position and velocity, and the following calculation formula is used for the determination of the parameters α:
α＝2(2k-1)/k(k-1)
β＝6/k(k-1)。
the second purpose of the invention is to provide an intelligent ship bank cooperative target tracking method based on a bank-based radar, which comprises the following steps:
step 1: collecting shore-based radar monitoring data and ship monitoring data;
step 2: fusing the shore-based radar monitoring data with the ship monitoring data to obtain fused monitoring data about a target ship;
and step 3: analyzing and processing the fusion monitoring data, and obtaining a predicted navigation track of the target ship by adopting a target tracking prediction calculation method of dynamic system data analysis;
and 4, step 4: and further optimizing the predicted sailing track to obtain a ship sailing track tracked by the ship-shore cooperative target, so that the intelligent ship can track the target of the target ship according to the optimized predicted track.
As further preferable in the present technical solution: step 2 further comprises: carrying out interpolation calculation on the ship monitoring data, and calculating a data approximate value of a vacant time period, so that the ship monitoring data and the shore-based radar monitoring data are matched in time; and according to the mercator projection principle, converting the longitude and latitude coordinates of the ship into coordinates on a planar mercator chart, so that ship monitoring data and shore-based radar monitoring data are converted into a same planar coordinate system in space.
(III) advantageous effects
The invention has the beneficial effects that: data in the shore-based radar-based intelligent ship and shore cooperative target tracking method are collected from monitoring information of the shore-based radar and monitoring perception information of the intelligent ship, and the accuracy of the monitoring data can be guaranteed due to multiple data sources; compared with a traditional ship radar system, the shore-based radar monitoring system has the advantages that monitored information is not interfered by environmental factors and various noise waves, collected data information is more reliable, and the shore-based radar is arranged along a coastline, so that a net-shaped monitoring system is formed in a monitoring sea area and a channel, and ships in the monitoring sea area are monitored in an all-round mode; the target tracking calculation method combines the self-perception data of the intelligent ship and the shore-based radar monitoring data to predict and calculate the navigation track, and meanwhile, the navigation track is optimized on the basis, the finally predicted navigation track of the target ship is almost the same as the navigation track of the real ship, and the calculation method for verifying target tracking prediction has usability.
Drawings
FIG. 1 is a schematic flow chart of the navigation trajectory tracking prediction principle of the present invention;
FIG. 2 is a comparison graph of the predicted track of the ship and the actual sailing track of the ship according to the present invention;
fig. 3 is a comparison graph of the predicted trajectory after optimization and the actual trajectory of the ship.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings. It is to be understood that the embodiments described herein are merely illustrative and explanatory of the invention and are not restrictive thereof.
Embodiments of the present invention will be described below with reference to fig. 1 to 3.
According to the shore-based radar-based intelligent ship and shore cooperative target tracking method, the intelligent ship needs to adopt intelligent ship sensing equipment to collect target ship navigation information, then data information of the target ship monitored by the shore-based radar is combined, the target ship navigation information is collected through two channels, and a reliable basis is provided for predicting the subsequent navigation track of the target ship. The intelligent ship mainly derives navigation data collected by the intelligent ship radar and the intelligent ship AIS system for collecting perception data.
Firstly, the shore-based radar is arranged along a coastline, the number of radars in the same interval is reasonably configured, and resource waste caused by excessive overlapping of the radars is prevented. The unit interval of adjacent radar is adjusted, guarantees at suitable distance, should not too far away or too near, and each radar can promote each other and cooperate, improves work efficiency, realizes monitoring the comprehensive coverage in the sea area. For radar arrangement in busy sea areas such as ports along the line, the radars need to be reasonably configured according to specific requirements, and the radar monitoring function in the busy sea areas is improved.
Information fusion of a shore-based radar and an intelligent ship AIS system requires that information received by various sensors is fused under the same condition. The information received by the two systems is a state value of a target ship at a certain moment, but the shore-based radar and the AIS system have different information acquisition modes and have difference in coordinates and time, so that the data of the two systems need to be processed, and the unification of the data in time and space is ensured.
When the shore-based radar scans the sea level, the shore-based radar operates in a natural period, so that the information collected by the radar is continuous in a time interval; the AIS system collects information in a non-fixed period, the collection frequency of the system changes according to the state change of a ship, the AIS collection time interval is generally between 2s and 6min, and therefore two kinds of equipment need to be unified in time.
Because the sensing data period of the intelligent ship sensing system is not fixed, and the shore-based radar data are collected uniformly and continuously in a period of time, the processed data of the intelligent ship sensing system is fused with the shore-based radar data. Carrying out interpolation calculation on the collected ship monitoring data, and knowing a point A (x)1,y1)、B(x2,y2) Coordinate value, calculate point C (x)3,y3) Coordinate values, wherein the three points A, B and C are on the same straight line, and the coordinate of the point C is calculated by utilizing the characteristic of the same slope,
and (3) assuming that the motion of the ship in a short time is linear operation, carrying out interpolation calculation according to the collected ship information, and calculating a data approximate value of the vacant time.
After the problem of time unification is solved, the problem of space unification also needs to be solved. The position of the target ship obtained by the intelligent ship sensing equipment is derived from a GPS, and the coordinates of the position are longitude and latitude data of satellite positioning, which are equivalent to spherical coordinates. The shore-based radar measurement data is calculated according to the time difference between the emission and the reception of electromagnetic waves and is the distance between the radar and a target. The processing of both types of information requires the aid of the mercator projection principle, which is the main method for processing maps of nautical information.
The calculation method of the mercator projection comprises the following steps: assuming that the longitude and latitude coordinates of a certain ship under a spherical coordinate system to be obtained are (Q, W), converting coordinates (X, Y) on a mercator chart on a plane;
firstly, according to the values of the length and the half axis of an ellipsoid of the earth: a is 6378137 m; short half shaft of the earth: and b is 6356752.3142 m. The first eccentricity e and the second eccentricity e can be obtained1Comprises the following steps:
let the original point longitude and latitude of the sea chart of the ink card support be (X)0,Y0) Calculating the equatorial radius of curvature to be NB0：
According to the known first eccentricity e and the radius of curvature N of the equatorB0Calculating ship coordinates on the mercator map:
YW＝K(L-L0)
wherein Q is0Is the standard latitude, XQ、YWIs a coordinate output parameter of converting longitude and latitude coordinates into coordinates on a chart, K is the latitude circle radius of the reference latitude, L is the longitude of the taken point, L is the latitude of the taken point0As a result of the longitude of the origin,
is the radius of curvature at point Q.
The prediction calculation of the target ship navigation track comprises the following steps: the method comprises the following steps of performing tracking prediction estimation on a navigation track of a ship by adopting a mode of linear estimation on a state sequence of a dynamic system, wherein a state equation and an observation equation of the tracking prediction estimation are as follows:
the state equation is as follows:
s(k)＝As(k-1)+u(k)
the observation equation:
z(k)＝Hs(k)+v(k)，
wherein the state value matrix is s (k) ═ x, vx,y,vy]The horizontal axis coordinate, the velocity component in the horizontal axis direction, the vertical axis coordinate and the velocity component in the vertical axis direction of the track are respectively represented, and k represents the (0,1,2 …) time;
observed value is [ x, y]TRepresenting the coordinates of the horizontal and vertical axes of the observed track, namely longitude and latitude, and z (k) is an observed value at the moment k; a is a system state transition matrix, and H is an observation matrix;
u (k), v (k) represent the state and observed noise, respectively, assumed to be gaussian noise, where v (k) N (0, Q), u (k) N (0, R), Q and R are relative, representing more confident observations or predictions;
and (3) predicting the system state:
s(k|k-1)＝As(k-1|k-1)
error covariance prediction:
P(k|k-1)＝AP(k-1|k-1)A′+Q
gain coefficient equation:
K＝P(k|k-1)H′×(HP(k|k-1)H′+R)
and (3) updating the system state:
s(k|k)＝s(k|k-1)+K(Z(k)-Hs(k|k-1))
updating the prior covariance:
P(k|k)＝(1-kH)P(k|k-1)
in the formula: p is the covariance of the state at the moment, H is the measurement system parameter, A is the system parameter, and the values of the matrixes A and H are as follows:
FIG. 1 is a schematic flow chart of target vessel voyage trajectory tracking prediction. It can be seen from the flow chart that at the start of the operation, the quantities that need to be input include the target starting state vector, the starting state covariance, and the observation and process noise. The state of the next moment is predicted through the prior information and the state covariance of each moment, and the state change of each moment can be effectively, accurately and simply calculated through the circulation mode.
Firstly, confirming the initial state, wherein the state component adopts a matrix of 1 × 4 dimensions, which respectively represents the x-axis position, the x-axis direction speed component, the y-axis position and the y-axis direction speed component. x is the number of1，y1Represents the position of the abscissa, x, at the first moment2，y2Represents the horizontal and vertical coordinate position of the second moment, t represents the separation time between the first moment and the second moment, and the state matrix is as follows:
next, the initialization of the mean square error is confirmed:
wherein S is a state value at a certain time,
in order to be the state prediction value,
t is the variance of the observed noise on each component, and T is the length of time of one cycle of the scan data.
And after the observation value at each time is determined, predicting the next time according to the states at every two previous times, finding the observation value at the next time according to the predicted value, and calculating the state value at the time through prediction and observation, and so on.
Assigning values to the parameters, wherein the state vector S is given by an initial flight path; the period T in the initial covariance P is 60s, and the covariance σ is large due to large detection range error in the oceanP、σθAre all set to 100; because the measured data is lower in noise, Q and R can be set into diagonal matrixes with smaller elements so as to ensure that the operation time rank is full.
Shown in fig. 2 are the ship's true track 1, the ship's predicted track 2 (i.e., the predicted track based on shore-based radar), and the predicted track 3 of the target ship using only the smart ship. As can be seen from the figure, the prediction of the target ship navigation track by the shore-based radar-based intelligent ship-shore cooperation method is closer to the real ship navigation track, and the prediction result is deviated due to the fact that the prediction of the target ship navigation track by the intelligent ship is interfered by various factors. As can be seen from the figure: and performing target tracking algorithm calculation on the collected data information based on the shore-based radar in a ship-shore cooperative mode to obtain a predicted target navigation track close to a real track, which shows that the adopted target tracking method has reliability.
However, in the calculation process of the target tracking algorithm, the sensing data contains complex noise and clutter from the ocean surface, and the measured data contains a large amount of sea clutter, so that a certain deviation also exists between the predicted track and the real sailing track of the target ship. To solve this problem, further sophisticated optimization of the target tracking method is required.
The optimization method is based on the premise of uniformly changing input signals, and after measurement values x (k) at the time k are obtained, the signals are estimated according to the following equation:
s(k)＝s(k|k-1)+α[x(k)-s(k|k-1)]
v(k)＝v(k|k-1)+β[x(k)-s(k|k-1)]/T
where s (k) is the coordinate at the kth time, s (k | k-1) is the predicted coordinate at the kth time calculated during the (k-1) th time, x (k) is the measured coordinate at the kth time, v (k) is the velocity estimate at the kth time, v (k | k-1) is the predicted velocity at the kth time calculated during the (k-1) th time, T is the time period of the scan, α and β are smoothing coefficients of position and velocity, and the following calculation formula is used for the determination of parameter α:
α＝2(2k-1)/k(k-1)
β＝6/k(k-1)。
the parameters are determined according to experience and a determination method summarized by the prediction of the current ship sailing track. And finally, optimizing the whole navigation track by optimizing a ship navigation track state equation and an observation equation. The display effect after optimization is shown in fig. 3.
Fig. 3 includes four types, namely a target ship true sailing trajectory 1, a ship prediction trajectory 2 (kalman prediction trajectory), an intelligent ship prediction trajectory 4, and an optimized prediction trajectory 3. Comparing with fig. 3, it can be clearly seen that the ship navigation track obtained after the target tracking calculation and the optimization is performed is closer to the real ship navigation track, and in the later stage of the prediction calculation, the predicted navigation track is almost coincident with the real navigation track, so that the prediction result obtained after the two algorithms are combined is further verified to be closer to the real navigation track.
The shore-based radar-based intelligent ship and shore cooperative target tracking method is calculated and analyzed, and the shore-based radar-based intelligent ship and shore cooperative target tracking method is more accurate in prediction of the navigation track of the target ship compared with that of the intelligent ship, so that the ship data in the monitored sea area can be more comprehensively and reliably collected by arranging the radar along the coastline, and the data acquisition anti-jamming capability is higher.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (10)
1. The utility model provides an intelligent boats and ships bank is target tracking system in coordination based on bank based radar which characterized in that includes: the system comprises a shore-based radar arranged on a shore line, an intelligent ship monitoring system, a data fusion system, a track prediction system and a track optimization system, wherein the intelligent ship monitoring system, the data fusion system, the track prediction system and the track optimization system are positioned on an intelligent ship;
the shore-based radar acquires monitoring data of the shore-based radar;
the intelligent ship monitoring system acquires ship monitoring data;
the data fusion system fuses the shore-based radar monitoring data and the intelligent ship monitoring data to obtain fused monitoring data about a target ship;
the track prediction system analyzes and processes the fusion monitoring data to obtain a predicted sailing track of the target ship;
and the track optimization system performs optimization processing on the predicted sailing track to obtain an optimized predicted track of the target ship, so that the intelligent ship performs target tracking on the target ship according to the optimized predicted track.
2. The shore-based radar-based smart ship shore cooperative target tracking system according to claim 1, wherein the number of the shore-based radars is plural, forming a network monitoring system in a navigation channel and a sea area; the intelligent ship monitoring system comprises a ship radar and a ship AIS system.
3. The shore-based radar-based smart ship-based ship-shore cooperative target tracking system according to claim 1 or 2, wherein the shore-based radar monitoring data and the ship monitoring data comprise ship position and ship speed.
4. The shore-based radar-based intelligent ship-based cooperative target tracking system as claimed in claim 1 or 2, wherein the data fusion system performs interpolation calculation on the ship monitoring data, calculates a data approximate value of the vacant time period, and matches the ship monitoring data with the shore-based radar monitoring data in time; and according to the mercator projection principle, converting the longitude and latitude coordinates of the ship into coordinates on a planar mercator chart, so that ship monitoring data and shore-based radar monitoring data are converted into a same planar coordinate system in space.
5. The shore-based radar-based intelligent ship-based cooperative target tracking system for the ship and the shore is characterized in that the track prediction system performs tracking prediction on the navigation track of the target ship by means of dynamic system data analysis to obtain the predicted navigation track of the target ship.
6. The shore-based radar-based smart ship shore cooperative target tracking system of claim 5, wherein said track prediction system describes said dynamic system by means of state equations and observation equations as follows:
the state equation is as follows:
s(k)＝As(k-1)+u(k)
the observation equation:
z(k)＝Hs(k)+v(k)，
wherein the state value matrix is s (k) ═ x, vx,y,vy]The horizontal axis coordinate, the velocity component in the horizontal axis direction, the vertical axis coordinate and the velocity component in the vertical axis direction of the track are respectively represented, and k represents the (0,1,2 …) time;
observed value is [ x, y]TRepresenting the coordinates of the horizontal and vertical axes of the observed track, namely longitude and latitude, and z (k) is an observed value at the moment k; a is a system state transition matrix, and H is an observation matrix;
u (k), v (k) represent the state and observed noise, respectively, assumed to be gaussian noise, where v (k) N (0, Q), u (k) N (0, R), Q and R are relative, indicating whether the observation or prediction is more believable.
7. The shore-based radar-based smart ship shore cooperative target tracking system according to claim 6, wherein the track prediction system further uses the following equation in a target tracking prediction calculation method using dynamic system data analysis:
and (3) predicting the system state:
s(k|k-1)＝As(k-1|k-1)
error covariance prediction:
P(k|k-1)＝AP(k-1|k-1)A′+Q
gain coefficient equation:
K＝P(k|k-1)H′×(HP(k|k-1)H′+R)
and (3) updating the system state:
s(k|k)＝s(k|k-1)+K(Z(k)-Hs(k|k-1))
updating the prior covariance:
P(k|k)＝(1-kH)P(k|k-1)
in the formula: p is the covariance of the state at the moment, H is the measurement system parameter, A is the system parameter, and the values of the matrixes A and H are as follows:
8. the shore-based radar-based smart ship shore cooperative target tracking system according to claim 7, wherein the track prediction system further adopts the following method in a target tracking prediction calculation method using dynamic system data analysis:
first, the initial state is confirmed, and the state matrix is:
wherein, the state component adopts a matrix of 1 × 4 dimension, which respectively represents the x-axis position, the x-axis direction velocity component, the y-axis position and the y-axis direction velocity component; x is the number of1，y1Represents the position of the abscissa, x, at the first moment2，y2Representing the position of a horizontal coordinate and a vertical coordinate at a second moment, and t represents the time interval between the first moment and the second moment;
next, the initialization of the mean square error is confirmed:
wherein S is a state value at a certain time,
in order to be the state prediction value,
t is the variance of the observed noise on each component, and T is the length of time of one cycle of the scan data.
9. The shore-based radar-based smart ship and shore cooperative target tracking system according to claim 1 or 2, wherein the track optimization system is optimized for ship position and ship speed, and estimates the signals according to the following equation:
s(k)＝s(k|k-1)+α[x(k)-s(k|k-1)]
v(k)＝v(k|k-1)+β[x(k)-s(k|k-1)]/T
where s (k) is the coordinate of the kth time, s (k | k-1) is the predicted coordinate of the kth time calculated during the (k-1) th time, x (k) is the measured coordinate of the kth time, v (k) is the velocity estimate at the kth time, v (k | k-1) is the predicted velocity at the kth time calculated during the (k-1) th time, T is the time period of the scan, α and β are smoothing coefficients of position and velocity, and the following calculation formula is used for the determination of the parameters α:
α＝2(2k-1)/k(k-1)
β＝6/k(k-1)。
10. a shore-based radar-based intelligent ship-shore cooperative target tracking method, wherein the shore-based radar-based intelligent ship-shore cooperative target tracking system according to any one of claims 1 to 9 is adopted, the method comprising the following steps:
step 1: collecting shore-based radar monitoring data and intelligent ship monitoring data;
step 2: fusing the shore-based radar monitoring data with the intelligent ship monitoring data to obtain fused monitoring data about a target ship;
and step 3: analyzing and processing the fusion monitoring data to obtain a predicted sailing track of the target ship;
and 4, step 4: and optimizing the predicted sailing track to obtain an optimized predicted track of the target ship so that the intelligent ship can track the target ship according to the optimized predicted track.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911140774.3A CN111157982A (en) | 2019-11-20 | 2019-11-20 | Intelligent ship and shore cooperative target tracking system and method based on shore-based radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911140774.3A CN111157982A (en) | 2019-11-20 | 2019-11-20 | Intelligent ship and shore cooperative target tracking system and method based on shore-based radar |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111157982A true CN111157982A (en) | 2020-05-15 |
Family
ID=70556037
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911140774.3A Pending CN111157982A (en) | 2019-11-20 | 2019-11-20 | Intelligent ship and shore cooperative target tracking system and method based on shore-based radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111157982A (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507429A (en) * | 2020-05-29 | 2020-08-07 | 智慧航海（青岛）科技有限公司 | Intelligent ship multi-source perception data ship-side fusion method and device and decision system |
| CN111812610A (en) * | 2020-06-29 | 2020-10-23 | 珠海云洲智能科技有限公司 | Overwater target supervision system and method, terminal device and storage medium |
| CN111880549A (en) * | 2020-09-14 | 2020-11-03 | 大连海事大学 | Unmanned ship path planning-oriented deep reinforcement learning reward function optimization method |
| CN111882619A (en) * | 2020-07-08 | 2020-11-03 | 智慧航海(青岛)智能系统工程有限公司 | Sea surface target identification method for simulating and testing visual equipment on intelligent ship |
| CN111915928A (en) * | 2020-06-28 | 2020-11-10 | 大连海事大学 | Ship collision accident rate prediction method |
| CN112373642A (en) * | 2020-10-30 | 2021-02-19 | 东南大学 | Inland ship overtaking behavior detection and tracking method based on ship field |
| CN112859133A (en) * | 2021-01-20 | 2021-05-28 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Ship depth fusion positioning method based on radar and Beidou data |
| CN113296509A (en) * | 2021-05-21 | 2021-08-24 | 上海海事大学 | Autonomous trajectory tracking fusion control method for unmanned surface vessel |
| CN113312438A (en) * | 2021-03-09 | 2021-08-27 | 中南大学 | Sea target position prediction method integrating airline extraction and trend judgment |
| CN113447922A (en) * | 2021-06-28 | 2021-09-28 | 武汉理工港航科技研究院有限公司 | Shore-based berthing system, method and equipment based on radar capture and laser tracking |
| CN113850848A (en) * | 2021-09-26 | 2021-12-28 | 大连海事大学 | Unmanned boat-mounted marine radar and visual image cooperative marine multi-target long-term detection and tracking method |
| CN113985406A (en) * | 2021-12-24 | 2022-01-28 | 中船（浙江）海洋科技有限公司 | Target track splicing method for marine radar |
| CN114022775A (en) * | 2021-09-14 | 2022-02-08 | 南京智慧水运科技有限公司 | Radar scanning variable-based underwater multi-target video image coordinate estimation method |
| CN114898593A (en) * | 2022-04-11 | 2022-08-12 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN115494498A (en) * | 2022-10-10 | 2022-12-20 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Photoelectric high-precision intelligent ship monitoring and tracking method based on multifunctional radar |
| CN115951325A (en) * | 2023-03-15 | 2023-04-11 | 中国电子科技集团公司第十五研究所 | BiGRU-based multi-ship target tracking method, storage medium and product |
| CN116626671A (en) * | 2023-07-20 | 2023-08-22 | 长威信息科技发展股份有限公司 | Ship identification method based on Fast-DTW |
| CN117556376A (en) * | 2024-01-11 | 2024-02-13 | 宁波朗达工程科技有限公司 | Ship dynamic track prediction and tracking method based on multi-source data fusion |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104091470A (en) * | 2014-07-15 | 2014-10-08 | 南京大学 | Channel traffic information prediction method and application based on multidata fusion |
| CN106021675A (en) * | 2016-05-13 | 2016-10-12 | 国家海洋局第三海洋研究所 | Multisource automobile identifying system data-based data fusion method |
| CN106526586A (en) * | 2016-11-03 | 2017-03-22 | 成都能通科技有限公司 | Data processing method based on shore-based information radar |
| CN108645415A (en) * | 2018-08-03 | 2018-10-12 | 上海海事大学 | A kind of ship track prediction technique |
-
2019
- 2019-11-20 CN CN201911140774.3A patent/CN111157982A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104091470A (en) * | 2014-07-15 | 2014-10-08 | 南京大学 | Channel traffic information prediction method and application based on multidata fusion |
| CN106021675A (en) * | 2016-05-13 | 2016-10-12 | 国家海洋局第三海洋研究所 | Multisource automobile identifying system data-based data fusion method |
| CN106526586A (en) * | 2016-11-03 | 2017-03-22 | 成都能通科技有限公司 | Data processing method based on shore-based information radar |
| CN108645415A (en) * | 2018-08-03 | 2018-10-12 | 上海海事大学 | A kind of ship track prediction technique |
Non-Patent Citations (2)
| Title |
|---|
| 温家宝: "高频低波雷达与AIS航迹跟踪算法研究" * |
| 穆晔: "船用导航雷达ARPA跟踪算法研究" * |
Cited By (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507429A (en) * | 2020-05-29 | 2020-08-07 | 智慧航海（青岛）科技有限公司 | Intelligent ship multi-source perception data ship-side fusion method and device and decision system |
| CN111507429B (en) * | 2020-05-29 | 2023-08-01 | 智慧航海（青岛）科技有限公司 | Intelligent ship multisource perception data ship end fusion method, device and decision system |
| CN111915928A (en) * | 2020-06-28 | 2020-11-10 | 大连海事大学 | Ship collision accident rate prediction method |
| CN111915928B (en) * | 2020-06-28 | 2022-04-26 | 大连海事大学 | Ship collision accident rate prediction method |
| CN111812610A (en) * | 2020-06-29 | 2020-10-23 | 珠海云洲智能科技有限公司 | Overwater target supervision system and method, terminal device and storage medium |
| CN111812610B (en) * | 2020-06-29 | 2023-09-29 | 珠海云洲智能科技股份有限公司 | Water target supervision system, method, terminal equipment and storage medium |
| CN111882619A (en) * | 2020-07-08 | 2020-11-03 | 智慧航海(青岛)智能系统工程有限公司 | Sea surface target identification method for simulating and testing visual equipment on intelligent ship |
| CN111882619B (en) * | 2020-07-08 | 2024-06-11 | 智慧航海（青岛）科技有限公司 | Sea surface target identification method for simulating and testing visual equipment on intelligent ship |
| CN111880549B (en) * | 2020-09-14 | 2024-06-04 | 大连海事大学 | Deep reinforcement learning rewarding function optimization method for unmanned ship path planning |
| CN111880549A (en) * | 2020-09-14 | 2020-11-03 | 大连海事大学 | Unmanned ship path planning-oriented deep reinforcement learning reward function optimization method |
| CN112373642A (en) * | 2020-10-30 | 2021-02-19 | 东南大学 | Inland ship overtaking behavior detection and tracking method based on ship field |
| CN112859133A (en) * | 2021-01-20 | 2021-05-28 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Ship depth fusion positioning method based on radar and Beidou data |
| CN112859133B (en) * | 2021-01-20 | 2022-06-14 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Ship depth fusion positioning method based on radar and Beidou data |
| CN113312438A (en) * | 2021-03-09 | 2021-08-27 | 中南大学 | Sea target position prediction method integrating airline extraction and trend judgment |
| CN113312438B (en) * | 2021-03-09 | 2023-09-15 | 中南大学 | Marine target position prediction method integrating route extraction and trend judgment |
| CN113296509A (en) * | 2021-05-21 | 2021-08-24 | 上海海事大学 | Autonomous trajectory tracking fusion control method for unmanned surface vessel |
| CN113296509B (en) * | 2021-05-21 | 2022-11-08 | 上海海事大学 | Autonomous trajectory tracking fusion control method for unmanned surface vessel |
| CN113447922A (en) * | 2021-06-28 | 2021-09-28 | 武汉理工港航科技研究院有限公司 | Shore-based berthing system, method and equipment based on radar capture and laser tracking |
| CN114022775A (en) * | 2021-09-14 | 2022-02-08 | 南京智慧水运科技有限公司 | Radar scanning variable-based underwater multi-target video image coordinate estimation method |
| CN114022775B (en) * | 2021-09-14 | 2024-04-30 | 南京智慧水运科技有限公司 | Water multi-target video image coordinate estimation method based on radar scanning variable |
| CN113850848B (en) * | 2021-09-26 | 2024-04-02 | 大连海事大学 | Marine multi-target long-term detection and tracking method based on cooperation of unmanned ship carrying navigation radar and visual image |
| CN113850848A (en) * | 2021-09-26 | 2021-12-28 | 大连海事大学 | Unmanned boat-mounted marine radar and visual image cooperative marine multi-target long-term detection and tracking method |
| CN113985406B (en) * | 2021-12-24 | 2022-05-10 | 中船（浙江）海洋科技有限公司 | Target track splicing method for marine radar |
| CN113985406A (en) * | 2021-12-24 | 2022-01-28 | 中船（浙江）海洋科技有限公司 | Target track splicing method for marine radar |
| CN114898593B (en) * | 2022-04-11 | 2024-02-02 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN114898593A (en) * | 2022-04-11 | 2022-08-12 | 珠海云洲智能科技股份有限公司 | Track acquisition method, track acquisition system and server |
| CN115494498B (en) * | 2022-10-10 | 2023-10-27 | 中船鹏力(南京)大气海洋信息系统有限公司 | Photoelectric high-precision intelligent ship monitoring and tracking method based on multifunctional radar |
| CN115494498A (en) * | 2022-10-10 | 2022-12-20 | 中船重工鹏力（南京）大气海洋信息系统有限公司 | Photoelectric high-precision intelligent ship monitoring and tracking method based on multifunctional radar |
| CN115951325B (en) * | 2023-03-15 | 2023-06-02 | 中国电子科技集团公司第十五研究所 | BiGRU-based multi-ship target tracking method, storage medium and product |
| CN115951325A (en) * | 2023-03-15 | 2023-04-11 | 中国电子科技集团公司第十五研究所 | BiGRU-based multi-ship target tracking method, storage medium and product |
| CN116626671B (en) * | 2023-07-20 | 2023-10-31 | 长威信息科技发展股份有限公司 | Ship identification method based on Fast-DTW |
| CN116626671A (en) * | 2023-07-20 | 2023-08-22 | 长威信息科技发展股份有限公司 | Ship identification method based on Fast-DTW |
| CN117556376A (en) * | 2024-01-11 | 2024-02-13 | 宁波朗达工程科技有限公司 | Ship dynamic track prediction and tracking method based on multi-source data fusion |
| CN117556376B (en) * | 2024-01-11 | 2024-04-30 | 宁波朗达工程科技有限公司 | Ship dynamic track prediction and tracking method based on multi-source data fusion |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111157982A (en) | Intelligent ship and shore cooperative target tracking system and method based on shore-based radar | |
| CN111028546B (en) | Multi-ship cooperative collision prevention system and method for intelligent ship based on shore-based radar | |
| CN111899568B (en) | Bridge anti-collision early warning system, method and device and storage medium | |
| Perera et al. | Ocean vessel trajectory estimation and prediction based on extended Kalman filter | |
| KR100818531B1 (en) | Offering Method of Navigation Risk of Ship and System Thereof | |
| Schuster et al. | Collision avoidance for vessels using a low-cost radar sensor | |
| Wilthil et al. | A target tracking system for ASV collision avoidance based on the PDAF | |
| CN109100698B (en) | A kind of radar target spherical projection method for maritime formation | |
| JP6567665B2 (en) | A method for estimating each drift (floating) vector at all points in a ship's route | |
| CN112880678A (en) | Unmanned ship navigation planning method in complex water area environment | |
| Anderson et al. | Track association for over-the-horizon radar with a statistical ionospheric model | |
| CN113687349A (en) | Unmanned ship sea surface target tracking method and device based on multi-sensor fusion | |
| Mullane et al. | X-band radar based SLAM in Singapore's off-shore environment | |
| CN114061565B (en) | Unmanned ship SLAM and application method thereof | |
| Stateczny et al. | FMCW radar implementation in River Information Services in Poland | |
| CN116466317B (en) | Target association method based on radar echo multi-feature information | |
| Qin et al. | Research on information fusion structure of radar and AIS | |
| CN115718905A (en) | VTS system-oriented multi-sensor information fusion method | |
| CN112731400B (en) | Method and system for estimating target vector speed of marine vessel | |
| Assaf et al. | The use of Kalman filter techniques for ship track estimation | |
| Wu et al. | A method with improved accuracy and robustness for object detection in wharf scenarios | |
| Mashoshin et al. | Application of passive underwater landmarks for autonomous unmanned underwater vehicles navigation | |
| RU2206104C2 (en) | Method for identification of distant aerial objects | |
| Wilthil | Maritime Target Tracking with Varying Sensor Performance | |
| CN115372956B (en) | Hybrid system radar track starting method based on forward and reverse logic Hough transformation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200515 |