CN109856625A - A kind of vessel position recognition methods based on multisource data fusion - Google Patents
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Abstract
The vessel position recognition methods based on multisource data fusion that the present invention relates to a kind of, it include: data calibration, receive the vessel position data that radar is sent and the vessel position data that AIS is sent, the progress time moves and coordinate transform, so that the vessel position data that vessel position data and AIS that radar is sent are sent form unified time and spatial reference point；Data fusion, using normal state Subordinate Function to the radar measured data and AIS measurement data progress data fusion after data calibration；Coordinate prediction, is predicted using track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship.The present invention merges radar and the AIS data detected using multisource data fusion technology; it is predicted by track of the Kalman filtering algorithm to ship; estimate the coordinate position of ship; once ship enters banned region and has the trend of casting anchor, can automatic phasing answer ship to give a warning or expel to have the function that protect sea area.
Description
Technical field
The present invention relates to radar target acquisition, ship automatic identification system (AIS) and computer graphics disposal technology field,
Especially a kind of vessel position recognition methods based on multisource data fusion.
Background technique
Radar is the electronic equipment using electromagnetic wave detection target.Radar tracking technology is by radar emission electromagnetic wave pair
Target is irradiated and receives its echo, thus to obtain information such as the distances, speed, azel of target to radar site.
Therefore, the information of the target and static target of the available movement of radar.AIS can receive the ship equipped with AIS system
Information.The information of AIS contain the static informations such as the cargo of the name of vessel of ship, catchword, MMSI, length, GPS location, loading with
And the navigation informations such as harbour, weather, ocean current.
Multisource data fusion, which refers to, is all integrated into one for all data investigated, analysis is got using correlation means
It rises, and to the process that obtained a variety of data are recognized, integrated, judged.The data for wherein participating in fusion often have multi-source
Property, isomerism and incompleteness.The level of fusion is divided into pixel-based fusion, Stage fusion and decision level fusion.Data level melts
Conjunction is the fusion of lowest level, is directly handled original data；Stage fusion proposes original data
It takes and handles, reduce useless data；Decision level fusion is most intelligentized fusion, is built upon on the basis of Model Fusion
Comprehensive decision is carried out for final processing result.
Kalman filtering is a kind of using linear system state equation, data is observed by system input and output, to system
State carries out the algorithm of optimal estimation, with good performance in terms of maneuvering target tracking and trajectory predictions.
In existing technology, the ships data that radar tracking detects and the ships data that AIS is detected are not melted
It closes to predict vessel position, ships data that the ships data or AIS only detected according to radar tracking detects carries out
Prediction, may cause prediction to a certain extent, there are relatively large deviations.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose a kind of position detection result it is more accurate based on
The vessel position recognition methods of multisource data fusion.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of vessel position recognition methods based on multisource data fusion, comprising:
Data calibration, receives the vessel position data that radar is sent and the vessel position data that AIS is sent, progress time remove
Shifting and coordinate transform, so that the vessel position data that vessel position data and AIS that radar is sent are sent form the unified time
And spatial reference point；
Data fusion, using normal state Subordinate Function to after data calibration radar measured data and AIS measurement data into
Row data fusion；
Coordinate prediction, is predicted using track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship.
Preferably, using normal state Subordinate Function to after data calibration radar measured data and AIS measurement data carry out
Data fusion specifically includes:
1) fuzzy factor set is established, the fuzzy factor set v (ζ 1, ζ 2, ζ 3) including three set of factors is established；Wherein, ζ 1 is indicated
Accommodation, ζ 2 indicate ship's speed, and ζ 3 indicates course；
2) weight for determining fuzzy factor set, the method for determining weight include DELPHI method, expert survey and level point
Analysis method；
3) Euclidean distance of fuzzy factors is calculated, as follows:
31) Euclidean distance at accommodation is calculated, as follows:
ζ1(t)2=(xi (t)-xj (t))2+(yi(t)-yj(t))2
Wherein, xi (t) and yi (t) respectively indicates the longitude and latitude value by the AIS t moment obtained；Xj (t) and yj
(t) longitude and latitude value of the t moment obtained by radar are respectively indicated；
32) Euclidean distance of ship's speed is calculated, as follows:
ζ 2 (t)=| Vi (t)-Vj (t) |
Wherein, Vi (t) and Vj (t) respectively indicates the ship's speed of the t moment by AIS and radar acquisition；
33) Euclidean distance in course is calculated, as follows:
ζ 3 (t)=| Si (t)-Sj (t) |
Wherein, Si (t) and Sj (t) respectively indicates the course of the t moment by AIS and radar acquisition；
4) the subordinating degree function value of each fuzzy factors, the Euclidean distance at the accommodation that step 3) is calculated, ship's speed are calculated
Euclidean distance and course Euclidean distance as fuzzy factor substitute into following formula to obtain the subordinating degree function value of each fuzzy factors:
ξ (ζ)=exp [- α (ζ/σ)]
Wherein, α indicates the weight in accommodation, ship's speed or course；ζ indicate the Euclidean distance at accommodation, ship's speed Euclidean distance or
The Euclidean distance in course；σ indicates the latitude of emulsion that fuzzy factors are concentrated；
5) weighted average calculation comprehensive similarity is pressed, as follows:
λ ij (t)=∑ α k ξ [ζ k (t)], i ∈ m, j ∈ n
Wherein, k indicates the weight of fuzzy factor set, and k (t) indicates the Euclidean distance of t time；
6) the track i from AIS node is established with the track j from radar node and is obscured m*n rank phase based on λ ij (t)
It closes matrix λ (t), i.e.,
7) track related check, according to threshold values rule carry out track related check the step of it is as follows:
71) value for determining threshold values ε, takes ε >=1/2；
72) greatest member λ ij (t) is found out in matrix λ (t), if λ ij (t) >=ε, is determined as that track i and j are examined
Correlation, the row and column element where scratching λ ij (t) in matrix λ (t) obtain new depression of order fuzzy matrix λ 1 (t), original matrix
Line number and row number remain unchanged；
73) process that step 72) is repeated to matrix λ 1 (t), obtains λ 2 (t) ... λ m (t), until all members in λ m (t)
Element is respectively less than ε, then track representated by the corresponding line number of remaining element and row number is uncorrelated in t moment；
74) judge whether the track obtained from AIS dynamic data and the track obtained from radar are related at any time,
If so, showing that then their information is from same target ship；
75) track association emulates, radar detection to targetpath and AIS provided by after targetpath is associated
Realize information fusion.
Preferably, it is predicted with track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship, wrapped
It includes:
The dynamical equation for constructing the modeling of the track AIS, is located in geographic coordinate system, and the position latitude and longitude coordinates of ship are usedIt indicates, according to loxodrome boat method, the equation in coordinates at positioning node k moment is as follows:
Wherein,Indicate that the coordinate position of k -1 moment ship, ν (k-1) indicate the speed of k -1 moment ship, θ
(k-1) course at ship k -1 moment is indicated；T indicates time interval；Wλ(k) respectively indicate that mean value is zero, variance is
σW 2White Gaussian noise W (k) two quadrature components, any timeWλ(k) independently of each other, It indicatesWithAverage value；
By the ship track recursive process based on Kalman filtering, the vessel position geographical coordinate at ship k moment is obtained
Estimate Z (k):
The invention has the following beneficial effects:
(1) a kind of vessel position recognition methods based on multisource data fusion of the present invention, uses multisource data fusion technology
Radar and the AIS data detected are merged, accurate data information can be obtained；
(2) a kind of vessel position recognition methods based on multisource data fusion of the present invention, passes through Kalman filtering algorithm pair
The track of ship is predicted, the coordinate position of ship is estimated, and predicts the movement tendency of ship, once ship enter by
Forbidden zone domain and have the trend of casting anchor, can automatic phasing answer ship to give a warning or expel to have the function that protect sea area.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those skilled in the art without any creative labor, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of flow diagram of the vessel position recognition methods based on multisource data fusion of the present invention；
Fig. 2 is the ship track recursive process of the invention based on Kalman filtering.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It is shown in Figure 1, a kind of vessel position recognition methods based on multisource data fusion, comprising:
Data calibration, receives the vessel position data that radar is sent and the vessel position data that AIS is sent, progress time remove
Shifting and coordinate transform, so that the vessel position data that vessel position data and AIS that radar is sent are sent form the unified time
And spatial reference point；
Data fusion, using normal state Subordinate Function to after data calibration radar measured data and AIS measurement data into
Row data fusion；
Coordinate prediction, is predicted using track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship.
Specifically, radar and AIS track relevant treatment object are the target position datas that two sensors provide.By data
The theory of fusion is it is found that timeliness and spatiality must be taken into consideration in data fusion.And radar and AIS are obtaining target position data
When, sampling instant is not necessarily identical；And the two is also different to the description of target position, radar is expressed as distance and inclination angle, AIS
It is expressed as longitude and latitude.Therefore, before the targetpath for carrying out the two is related, the calibration of Yao Jinhang data.Data calibration
Effect be time and spatial reference point for unified radar and AIS, carrying out the time moves and coordinate transform, to form unification
Time and space reference point.
The fusion of radar and AIS target data is mainly by the relevant data of track according to algorithm appropriate, to from difference
The information that sensor states same target is merged, and more accurate track data is obtained.
Radar and AIS targetpath correlation process method will mainly find out determining AIS from numerous radar targets
Targetpath is one corresponding.It is same with the time to judge that the information of two sensors is coordinately transformed by track correlation needs
Step, then do track correlated judgment.
Track correlation refers to the similarity degree for two tracks that sensing system obtains.The present invention is using in fuzzy mathematics
Normal state Subordinate Function carries out fusion calculation, and when two tracks are at a distance of remoter, Euclidean distance is bigger, and membership function value is smaller.
Specifically, using normal state Subordinate Function to after data calibration radar measured data and AIS measurement data carry out
Data fusion, comprising:
1) fuzzy factor set is established, the fuzzy factor set v (ζ 1, ζ 2, ζ 3) including three set of factors is established；Wherein, ζ 1 is indicated
Accommodation, ζ 2 indicate ship's speed, and ζ 3 indicates course；
2) weight for determining fuzzy factor set, the method for determining weight include DELPHI method, expert survey and level point
Analysis
It is accommodation, ship's speed and course due to influencing the relevant factor of track, wherein accommodation plays a leading role, and the speed of a ship or plane takes second place,
Course influences minimum, and therefore, the weight at 3 fuzzy factors accommodations, ship's speed and course can be taken as α 1=0.80, α 2=respectively
0.15, α 3=0.05.
3) Euclidean distance of fuzzy factors is calculated, as follows:
31) Euclidean distance at accommodation is calculated, as follows:
ζ1(t)2=(xi (t)-xj (t))2+(yi(t)-yj(t))2
Wherein, xi (t) and yi (t) respectively indicates the longitude and latitude value by the AIS t moment obtained；Xj (t) and yj
(t) longitude and latitude value of the t moment obtained by radar are respectively indicated；
32) Euclidean distance of ship's speed is calculated, as follows:
ζ 2 (t)=| Vi (t)-Vj (t) |
Wherein, Vi (t) and Vj (t) respectively indicates the ship's speed of the t moment by AIS and radar acquisition；
33) Euclidean distance in course is calculated, as follows:
ζ 3 (t)=| Si (t)-Sj (t) |
Wherein, Si (t) and Sj (t) respectively indicates the course of the t moment by AIS and radar acquisition；
4) the subordinating degree function value of each fuzzy factors, the Euclidean distance at the accommodation that step 3) is calculated, ship's speed are calculated
Euclidean distance and course Euclidean distance as fuzzy factor substitute into following formula to obtain the subordinating degree function value of each fuzzy factors:
ξ (ζ)=exp [- α (ζ/σ)]
Wherein, α indicates the weight in accommodation, ship's speed or course；ζ indicate the Euclidean distance at accommodation, ship's speed Euclidean distance or
The Euclidean distance in course；σ indicates the latitude of emulsion that fuzzy factors are concentrated；
5) weighted average calculation comprehensive similarity is pressed, as follows:
λ ij (t)=∑ α k ξ [ζ k (t)], i ∈ m, j ∈ n
Wherein, k indicates the weight of fuzzy factor set, and k (t) indicates the Euclidean distance of t time；
6) the track i from AIS node is established with the track j from radar node and is obscured m*n rank phase based on λ ij (t)
It closes matrix λ (t)；
7) track related check, according to threshold values rule carry out track related check the step of it is as follows:
71) value for determining threshold values ε, takes ε >=1/2；
72) greatest member λ ij (t) is found out in matrix λ (t), if λ ij (t) >=ε, is determined as that track i and j are examined
Correlation, the row and column element where scratching λ ij (t) in matrix λ (t) obtain new depression of order fuzzy matrix λ 1 (t), original matrix
Line number and row number remain unchanged；
73) process that step 72) is repeated to matrix λ 1 (t), obtains λ 2 (t) ... λ m (t), until all members in λ m (t)
Element is respectively less than ε, then track representated by the corresponding line number of remaining element and row number is uncorrelated in t moment；
74) judge whether the track obtained from AIS dynamic data and the track obtained from radar are related at any time,
If so, showing that then their information is from same target ship；
75) track association emulates, radar detection to targetpath and AIS provided by after targetpath is associated
Realize information fusion.
By above process, relatively accurate data are obtained.
After the accurate data that obtains that treated, need to detect that suspicious object, the present invention utilize Kalman filtering to calculate
Method predicts the track of ship.
It is predicted with track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship, comprising:
The dynamical equation for constructing the modeling of the track AIS, is located in geographic coordinate system, and the position latitude and longitude coordinates of ship are usedIt indicates, according to loxodrome boat method, the equation in coordinates at positioning node k moment is as follows:
Wherein,Indicate that the coordinate position of k -1 moment ship, ν (k-1) indicate the speed of k -1 moment ship, θ
(k-1) course at ship k -1 moment is indicated；T indicates time interval；Wλ(k) respectively indicate that mean value is zero, variance is
σW 2White Gaussian noise W (k) two quadrature components, any timeWλ(k) mutually indepedent；Sec indicates secant three
Angle function is the inverse of cosx；It indicatesWithAverage value；
It is shown in Figure 2, by the ship track recursive process based on Kalman filtering, obtain the ship at ship k moment
The geographical coordinate Z (k) of position:
Specific embodiment is applied in the present invention, and principle and implementation of the present invention are described, above embodiments
Explanation be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art,
According to the thought of the present invention, there will be changes in the specific implementation manner and application range, in conclusion in this specification
Appearance should not be construed as limiting the invention.
Claims (3)
1. a kind of vessel position recognition methods based on multisource data fusion characterized by comprising
Data calibration, receive radar send vessel position data and AIS send vessel position data, carry out the time move and
Coordinate transform, so that the vessel position data that vessel position data and AIS that radar is sent are sent form unified time and sky
Between reference point；
Data fusion, using normal state Subordinate Function to after data calibration radar measured data and AIS measurement data count
According to fusion；
Coordinate prediction, is predicted using track of the Kalman filtering algorithm to ship, estimates the coordinate position of ship.
2. the vessel position recognition methods according to claim 1 based on multisource data fusion, which is characterized in that using just
State Subordinate Function specifically includes radar measured data and AIS measurement data progress data fusion after data calibration:
1) fuzzy factor set is established, the fuzzy factor set v (ζ 1, ζ 2, ζ 3) including three set of factors is established；Wherein, ζ 1 indicates accommodation,
ζ 2 indicates ship's speed, and ζ 3 indicates course；
2) weight for determining fuzzy factor set, the method for determining weight include DELPHI method, expert survey and analytic hierarchy process (AHP)；
3) Euclidean distance of fuzzy factors is calculated, as follows:
31) Euclidean distance at accommodation is calculated, as follows:
ζ1(t)2=(xi (t)-xj (t))2+(yi(t)-yj(t))2
Wherein, xi (t) and yi (t) respectively indicates the longitude and latitude value by the AIS t moment obtained；Xj (t) and yj (t) points
The longitude and latitude value for the t moment that Biao Shi not be obtained by radar；
32) Euclidean distance of ship's speed is calculated, as follows:
ζ 2 (t)=| Vi (t)-Vj (t) |
Wherein, Vi (t) and Vj (t) respectively indicates the ship's speed of the t moment by AIS and radar acquisition；
33) Euclidean distance in course is calculated, as follows:
ζ 3 (t)=| Si (t)-Sj (t) |
Wherein, Si (t) and Sj (t) respectively indicates the course of the t moment by AIS and radar acquisition；
4) the subordinating degree function value of each fuzzy factors is calculated, the Europe of the Euclidean distance at the accommodation that step 3) is calculated, ship's speed
The Euclidean distance in formula distance and course substitutes into following formula as fuzzy factor to obtain the subordinating degree function value of each fuzzy factors:
ξ (ζ)=exp [- α (ζ/σ)]
Wherein, α indicates the weight in accommodation, ship's speed or course；ζ indicates the Euclidean distance at accommodation, the Euclidean distance of ship's speed or course
Euclidean distance；σ indicates the latitude of emulsion that fuzzy factors are concentrated；
5) weighted average calculation comprehensive similarity is pressed, as follows:
λ ij (t)=∑ α k ξ [ζ k (t)], i ∈ m, j ∈ n
Wherein, k indicates the weight of fuzzy factor set, and k (t) indicates the Euclidean distance of t time；
6) the track i from AIS node is established with the track j from radar node and is obscured m*n rank Correlation Moment based on λ ij (t)
Battle array λ (t), i.e.,
7) track related check, according to threshold values rule carry out track related check the step of it is as follows:
71) value for determining threshold values ε, takes ε >=1/2；
72) greatest member λ ij (t) is found out in matrix λ (t), if λ ij (t) >=ε, is determined as that track i is related to j inspection,
Row and column element where scratching λ ij (t) in matrix λ (t) obtains new depression of order fuzzy matrix λ 1 (t), the line number of original matrix
It is remained unchanged with row number；
73) process that step 72) is repeated to matrix λ 1 (t), obtains λ 2 (t) ... λ m (t), until all elements in λ m (t) are equal
Less than ε, then track representated by the corresponding line number of remaining element and row number is uncorrelated in t moment；
74) judge whether the track obtained from AIS dynamic data and the track obtained from radar are related at any time, if
It is to show that then their information is from same target ship；
75) track association emulate, radar detection to targetpath and AIS provided by targetpath be associated after realize
Information fusion.
3. the vessel position recognition methods according to claim 1 based on multisource data fusion, which is characterized in that use karr
Graceful filtering algorithm predicts the track of ship, estimates the coordinate position of ship, comprising:
The dynamical equation for constructing the modeling of the track AIS, is located in geographic coordinate system, and the position latitude and longitude coordinates of ship are usedIt indicates, according to loxodrome boat method, the equation in coordinates at positioning node k moment is as follows:
Wherein,Indicate that the coordinate position of k -1 moment ship, ν (k-1) indicate the speed of k -1 moment ship, θ (k-1) table
Show the course at ship k -1 moment；T indicates time interval；Wλ(k) respectively indicate mean value be zero, variance σW 2Height
Two quadrature components of this white noise W (k), any timeWλ(k) independently of each other,It indicates
WithAverage value；
By the ship track recursive process based on Kalman filtering, the vessel position geographical coordinate estimation Z at ship k moment is obtained
(k):
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