CN108334893A - A kind of underwater slender bodies characteristic recognition method of more bright spot clusterings - Google Patents
A kind of underwater slender bodies characteristic recognition method of more bright spot clusterings Download PDFInfo
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Abstract
The present invention relates to a kind of underwater slender bodies characteristic recognition methods of more bright spot clusterings, and this approach includes the following steps：Its length and the direction of motion are calculated using the bright spot spatial distribution of underwater slender bodies target；The cluster centre of target accumulation is obtained using the expectation maximum value EM algorithms under constraints in conjunction with clustering method, and course and the length of target can be estimated accordingly.Present invention employs the EM algorithms with Prescribed Properties to estimate the direction of advance (course) and scale of the submarine target with slender bodies shape using more bright point informations；And this method has the characteristics that simple and practicable, reliable operation.
Description
Technical field
The present invention relates to the underwater elongated body characteristics of submarine navigation device field more particularly to a kind of more bright spot clusterings to know
Other method
Background technology
Submarine target feature recognition is always one of the research emphasis in signal processing field, and main purpose is to obtain
Water intaking lower target property, including location information, speed, direction of advance and size etc..Detection to submarine target, generally use sound
It receives.The track algorithm of underwater manoeuvre target calculates the relative distance of target, direction and relatively fast according to target echo characteristic
Degree, then according to moving equation and filtering algorithm, estimates the motion feature of target.But due to the spy of underwater environment
Different property, it is larger to the evaluated error of the distance of submarine target, orientation and radial velocity by sonar echo；While sound is in water
Spread speed there was only 1500m/s or so, therefore less for the echo data that utilizes during entire tracking, target signature is estimated
It is not high to count precision.At this stage, the problem of underwater manoeuvre target following or one need further to be furtherd investigate.
The development of modern Autonomous Underwater Vehicle (AUV) technology, another thinking is provided for underwater target tracking.AUV
Submarine target can be carried out to continue tracking, compensate for the less problem of echo data to a certain extent by displacement；
On the other hand, by carrying out approaching scouting to target, the near-field information of target can be obtained, is conducive to improve target signature identification
Accuracy.But on AUV platforms using Kalman filtering (or Extended Kalman filter, particle filter etc.) to submarine target into
Line trace, it is necessary to which accurate navigation (position, speed etc.) information for knowing AUV itself, this is typically more tired for AUV platforms
Difficult.That is, the shortage of high-precision navigation information limits precision of the AUV platforms to target following.Especially when underwater
When target carries out motor-driven, it is then more difficult that high precision tracking is carried out to it, it usually needs carry out certain data accumulation, thus
Cause significantly estimation lag.The characteristics of how being directed to submarine target, proposes a kind of high-precision, effective target signature
Recognition methods has become the active demand in underwater target tracking field.
In recent years, clustering method is widely applied in Medical Image Processing and Radar Signal Processing.For
Medical image proposes a kind of innovatory algorithm based on fuzzy means clustering, can clearly rebuild bone three-dimensional structure.It is based on
The method of fuzzy cluster analysis has also been applied to the extraction of passive radar target identification and radar target three-dimensional scattering center.
Under normal circumstances, the shape of Large Underwater target is essentially all similar elliposoidal FORCES ON SLENDER BODY OF REVOLUTION.Although mesh
Target shape has a certain impact to bright spot distribution tool, and still, as long as the length-width ratio of target is quite big, this influence can be neglected
Slightly.At this point, the width of target is distributed no any effect to analysis bright spot.Therefore, to the large-scale scale with elongated bulk properties
For target, in the case of ignoring target width, regard target as a line segment, all reflection kernels of bright spot are conllinear
-- it is respectively positioned on target longitudinal axis.
K-means clustering algorithm (K-means) is a kind of widely applied clustering algorithm, and target is to distribute observation
It is different classes of to K.The algorithm computational efficiency is high, and can rapidly converge to local optimum.K-means clustering algorithm lacks
One of point is poor for the robustness of outlier.And underwater environment is complex, measurement noise non-gaussian and unknown, causes often
It will appear outlier, thus the data that environment obtains under water are not particularly suited for K- mean algorithms.
It is expected that-maximum value (EM) clustering algorithm is after finding maximal possibility estimation or the maximum of parameter in probabilistic model
The algorithm of probability is tested, main thought is the expectation boundary that the log-likelihood estimation of partial data is established by iteration, then
Maximize the log-likelihood function of deficiency of data.
It is expected that-maximum value (EM) clustering algorithm and K-means clustering algorithm the difference is that, EM algorithms are after being based on
The soft distribution cluster of probability is tested, and K- mean algorithms are to carry out hard distribution cluster to data point.To a certain extent, K-mean value
Clustering algorithm can be counted as the EM algorithms under limiting case.However, EM algorithm robustness is preferable, and can be more convenient
Clustering problem of the ground processing with constraint.
Invention content
The purpose of the present invention is being directed to the above feature, propose the clustering problem with binding feature to underwater slender bodies target
The algorithm for estimating of reflection kernel.
To achieve the above object, the present invention provides a kind of underwater slender bodies feature recognition sides of more bright spot clusterings
Method, this approach includes the following steps：Its length and the direction of motion are calculated using the bright spot spatial distribution of underwater slender bodies target；Knot
Clustering method is closed, using the expectation maximum value EM algorithms under constraints, obtains the cluster centre of target accumulation, and can evidence
This estimates the course of target and length.
Preferably, target accumulation is obtained using the expectation maximum value EM algorithms under constraints in conjunction with clustering method
Cluster centre, and the course of target and length step can be estimated accordingly, including：It is obtained without constraint EM clustering problems by solving
Obtain initial value；Estimate posterior probability by calculating；When cluster centre is kept fixed value, conllinear coefficient is updated；According to conllinear system
Number estimation cluster centre；It is calculated according to the initial value, the posterior probability, the conllinear coefficient and the cluster centre parameter
The course of the target and length.
Preferably, initial value is calculated by the following formula acquisition：
Wherein, μkFor initial value, NkIt is effective bright spot quantity of bright spot center k.
Preferably, posterior probability is calculated by the following formula acquisition：
Wherein, γ (Znk) it is posterior probability, πkEstimate for covariance.
Preferably, there are a constant t and a vector C for the conllinear coefficient；Wherein, vector C with formula by being obtained：
Wherein, M=[μ1,…μk],Then, normalization C ← C/ is done to C | | C | |2, it is ensured that | | C | |2
=1；Following formula is recycled to obtain constant t.
Preferably, cluster centre step is estimated according to conllinear coefficient, including：Construct the Lagrangian letter of log-likelihood function
Number：
L is to μKLocal derviation is sought, and is allowed to be equal to 0, then is had,
Above formula both sides while premultiplication Σk, then premultiplication CT, and utilize CTμk+ t=0 can be obtained,
Therefore, cluster centre is：
Wherein, NkIt is effective bright spot quantity of bright spot center k.
Preferably, the course of target is obtained by the following formula with length：
φ=± atan2 (C2,C1),
Wherein, C=[C1 C2]T, μkThe coordinate at bright spot center is represented, vector C represents the slope of straight line,
T represents straight line in the position of plane.
In terms of the present invention has the advantages that following three：
1. do not need the location information of submarine navigation device itself, high-precision course estimation is carried out to target, avoid by
Estimated result is influenced in the lower positioning accuracy of submarine navigation device itself.
2. target maneuver model need not be introduced, when target maneuver, not will produce estimation delay, because of the course of this method
Estimation is the instantaneous result being distributed based on current bright spot, any information before not being related to.
3. method proposed by the present invention can estimate the length of target.
The beneficial effects of the present invention are use the EM algorithms with Prescribed Properties, using more bright point informations, to having
The direction of advance (course) and scale of the submarine target of slender bodies shape are estimated；And this method has simple and practicable, work
Make reliable feature.
Description of the drawings
Fig. 1 is a kind of underwater slender bodies characteristic recognition method flow of more bright spot clusterings provided in an embodiment of the present invention
Schematic diagram；
Fig. 2 is the bright spot and reflection kernel schematic diagram of underwater slender bodies scaled target；
Fig. 3 is cluster centre estimation and its covariance；
Fig. 4 is submarine target and sonar position view；
Fig. 5 is different distance and the scale and course estimation error (σ in the case of the angle of sightR=10m, σα=0.1 °).
Specific implementation mode
After embodiments of the present invention are described in detail by way of example below in conjunction with attached drawing, of the invention its
His features, characteristics, and advantages will be more obvious.
For the large-scale scaled target with elongated bulk properties, in the case of ignoring target width, regard target as one
Line segment, all reflection kernels of bright spot are conllinear, have both been respectively positioned on target longitudinal axis.The emphasis that the present invention is paid close attention to is that have about
Estimation of the clustering problem of beam characteristic to large-scale target reflection kernel.
For the underwater slender bodies characteristic goal motion feature of determination, bright spot center at least needs 2, so as to determine one
The longitudinal axis baseline of submarine target.On the one hand the increase of bright spot centric quantity can increase constraints, estimation is made to have more robust
Property；But then, with the increase of bright spot quantity, if the distance between bright spot is too small (width for being even less than target), then
Target width be can not ignore, and the constraints of bright spot synteny is also no longer set up.Thus bright spot number needs consider.From reality
Border physical significance is set out, and bright spot is set as three, i.e. stem, casing and tail portion, a kind of preferable processing method of can yet be regarded as.
For the large-scale scale underwater movement objective with elongated shape, incident acoustic wave can be by shell and interior compartment
Room is reflected, but most reflected energy changes great target stem, casing, tail portion from incident angle.Many researchs
Show the fixation bright spot group that mobile bright spot and corner angle that Large Underwater target echo is mainly reflected to form by surface mirror reflect to form
At.It either moves bright spot and still fixes bright spot, they both correspond to some position in target.The highlight model of target, can
With equivalent at three rigid balls, i.e. three bright spots, stem, casing and the tail portion of underwater movement objective are respectively represented.Rigid ball
Different radii represents different target strengths.
Consider in conjunction with of both actual needs and theoretical research, the application assumes that large-scale target includes in three scatterings
The heart, respectively stem, casing and tail portion, as shown in Figure 2.
Underwater environment is complicated, is likely to outlier occur in multiple bright spots of target, it is necessary to it is stronger poly- to choose robustness
Class algorithm.According to the thinking being mentioned above, all bright spot center μ of submarine targetkThe strict difinition direction of advance of target.
From algorithm robustness and convenient for handling constraints these two aspects, EM algorithm ratio K- mean algorithms are more suitable for submarine target
Bright spot is handled and is analyzed.
For the submarine target with slender bodies, if ignoring the influence of flow, it is believed that y direction is target
Direction of advance, and target length can be estimated by longest distance between reflection kernel.
Distribution of the bright spot in space, each Gaussian Profile are indicated using the mixed Gauss model of K gauss component composition
The distribution of a reflection kernel periphery bright spot is represented,
(1) x ∈ R in formula2, N (x | μk,Σk) be 2 dimension spaces Gaussian Profile：
Wherein, μkIt is 2 fibrillar center, ΣkIt is 2 × 2 dimension covariance matrixes；πkIt indicates mixed coefficint, meets
In addition, for the large scale submarine target with slender bodies, as previously mentioned, its cluster centre is conllinear, that is, have one
An a constant t and vector C ∈ R2, meet condition
||C||2=1, and CTμk=t k=1 ... K (4)
Then Target Tracking Problem is converted into：Meeting conllinear constraints CTμkIn the case of=t, k=1 ... K, estimation ginseng
NumberAnd then estimate bogey heading and scale.
Fig. 1 is a kind of underwater slender bodies characteristic recognition method flow of more bright spot clusterings provided in an embodiment of the present invention
Schematic diagram.As shown in Figure 1
By solving μ can be obtained without constraint EM clustering problemsk(k=1 ... K) initial value, derivation be briefly described as
Under：
The log-likelihood function of bright spot is as follows：
Above formula is to μkLocal derviation can be written as：
Wherein,
So that above formula is constantly equal to 0, then have,
Therefore, μ can be obtainedkInitial value：
At this point, NkIt is effective bright spot quantity of bright spot center k.
πkMixed coefficint is estimated
Log-likelihood function value is to πkLocal derviation is sought, can be written as：
By using constraintsThe Lagrangian of log-likelihood function is constructed,
Then, it is assumed thatWe obtain following formula,
Therefore,
πkCovariance is estimated
Log-likelihood function value pairLocal derviation is sought, and is assumedIt can obtain,
Linear restriction is actually not equivalent to the conllinear constraint of cluster centre, thus with the cluster point collinearly constrained
Analysis problem is difficult to acquire analytic solutions.Then solve to ask with the clustering collinearly constrained using the iterative algorithm of similar EM algorithms
Topic.Estimation procedure can be divided into following two step：
Step I：
When cluster centre is kept fixed value, conllinear coefficient C and t is updated.
Assuming that all K cluster centres are conllinear, then there is an a constant t and vector C, following equalities is made to set up：
CTμk+ t=0, | | C | |2=1, k=1 ... K. (16)
Condition CTμk+ t=0 is equivalent to：
CTμk+ t~N (0, σ2), (17)
Wherein σ is arbitrarily small location constant.Therefore, vector C, such as following formula can be obtained by least square method：
Wherein, M=[μ1,…μk],Then, normalization C ← C/ is done to C | | C | |2, it is ensured that | | C | |2
=1.Can finally following formula be utilized to obtain t：
Step II：
It is assumed that C and t is it is known that can then estimate cluster centre μk.Construct the Lagrangian of log-likelihood function：
L is to μKLocal derviation is sought, and is allowed to be equal to 0, then is had,
Above formula both sides while premultiplication Σk, then premultiplication CT, and utilize CTμk+ t=0 can be obtained,
Therefore, cluster centre is：
Wherein, NkIt is defined by formula (10).
Algorithm is summarized
It is as follows to summarize this paper algorithms：
Using traditional EM algorithms, initial value, including μ, Σ, π are obtained.
Estimate posterior probability by formula (6).
Formula (18) is used respectively, and (19), (9), (15), (14) estimate parameter C, t, μk,Σk,πk。
2~3 are repeated, until algorithmic statement.
After obtaining the above parameter, target direction of advance φ and target length l can be calculated by following equation and be obtained,
φ=± atan2 (C2,C1), (24)
Wherein, C=[C1 C2]T
In above-mentioned algorithm, μkThe coordinate at bright spot center is represented, vector C represents the slope of straight line, and t represents straight line in plane
Position, (24) formula are that the formula in course is sought according to straight slope.
The bogey heading that the embodiment of the present application acquires might have the fuzzy of 180 degree, i.e.,：The simple space using bright spot point
Cloth information, can not resolution target be directed towards sonar or far from sonar navigate by water.It in this case, can be according to distance or radial speed
Information is spent, to determine the true course of target.It is to be noted that this is different from utilizing the distance and radial velocity repeatedly measured
Information carry out Kalman filtering algorithm.
The application is by using the EM algorithms with Prescribed Properties, using more bright point informations, to slender bodies shape
The direction of advance (course) and scale of submarine target are estimated；And this method has the characteristics that simple and practicable, reliable operation.
The specific implementation mode of the only invention described in specification.Although being described in conjunction with the accompanying the implementation of the present invention
Mode, but those ordinary skill in the art can make various deformations or amendments within the scope of the appended claims.
Proof of algorithm
Implement use-case：Target length is 120m, it is assumed that there are three bright spot center, target stem, target tail and casings.
All bright spots, the cluster centre of estimation, direction of advance (course) and length, and each cluster are shown in Fig. 3
The covariance (4 times of standard deviations) at center.Bright spot and cluster centre in figure are indicated with point and "+" respectively.As shown, of the invention
The algorithm of proposition ensures that cluster centre is in collinear position.Compared with actual target length 120m, the target estimated is long
Degree is 118m.In addition, from the figure not difficult to find, centrifugal pump is almost no impact estimation result, illustrate that the algorithm has
Preferable robustness.
Further to verify algorithm proposed in this paper, Monte Carlo simulation is carried out.Assuming that target length is 100m, course is
0 degree, the position at three of them bright spot center be respectively stem, away from 1/3 length of stem casing and tail portion.Sonar range tail portion
Distance be R, angle of sight α, as shown in Figure 4.If the range measurement (ranging) of sonar system and making an uproar for azimuthal measurement (direction finding)
Sound obeys zero-mean gaussian distribution, and standard deviation is respectively σRAnd σα, to the target at different location, according to calculation proposed in this paper
Method calculates its course and length, analyzes the error of algorithm, and error result is the flat of 1000 Monte Carlo simulation results
Mean value.
Fig. 5 is the emulation to target signature estimation in the case of sonar system direction finding precision is higher.Simulated conditions are as follows：
Ranging standard deviation 10m；
0.1 ° of direction finding standard deviation；
Distance 200m~800m；
10 °~90 ° of the angle of sight.
It can be seen from the figure that submarine target distance is closer, it is more accurate to the estimation in course；When the angle of sight is less than 70 °,
The estimated accuracy of target scale and distance relation are little, and only when the angle of sight is more than 70 °, target range just seriously affects scale
The precision of estimation.For this explanation when sonar direction finding precision is higher and abeam direction of the target not in point of observation, algorithm estimates scale
The precision of meter and distance and range accuracy relationship are little.
It is clear that under the premise of without departing from true spirit and scope of the present invention, invention described herein can be with
There are many variations.Therefore, all it will be apparent to those skilled in the art that change, be intended to be included in present claims
Within the scope of book is covered.Scope of the present invention is only defined by described claims.
Claims (7)
1. a kind of underwater slender bodies characteristic recognition method of more bright spot clusterings, which is characterized in that
Its length and the direction of motion are calculated using the bright spot spatial distribution of underwater slender bodies target；
The cluster centre of target accumulation is obtained using the expectation maximum value EM algorithms under constraints in conjunction with clustering method,
And course and the length of target can be estimated accordingly.
2. according to the method described in claim 1, it is characterized in that, the combination clustering method, using under constraints
Expectation maximum value EM algorithms, obtain the cluster centre of target accumulation, and course and the length of target, step can be estimated accordingly
Including：
By solving initial value is obtained without constraint EM clustering problems；
Estimate posterior probability by calculating；
When cluster centre is kept fixed value, conllinear coefficient is updated；
Estimate cluster centre according to conllinear coefficient；
The target is calculated according to the initial value, the posterior probability, the conllinear coefficient and the cluster centre parameter
Course and length.
3. according to the method described in claim 2, it is characterized in that, the initial value is calculated by the following formula acquisition：
Wherein, μkFor initial value, NkIt is effective bright spot quantity of bright spot center k.
4. according to the method described in claim 2, it is characterized in that, the posterior probability is calculated by the following formula acquisition：
Wherein, γ (Znk) it is posterior probability, πkEstimate for covariance.
5. according to the method described in claim 2, it is characterized in that, the conllinear coefficient there are a constant t and a vector
C；
Wherein, vector C with formula by being obtained：
Wherein,
Then, normalization C ← C/ is done to C | | C | |2, it is ensured that | | C | |2=1；Following formula is recycled to obtain constant t.
6. according to the method described in claim 2, it is characterized in that, the conllinear coefficient of the basis estimates cluster centre step, packet
It includes：
Construct the Lagrangian of log-likelihood function：
L is to μKLocal derviation is sought, and is allowed to be equal to 0, then is had,
Above formula both sides while premultiplication Σk, then premultiplication CT, and utilize CTμk+ t=0 can be obtained,
Therefore, cluster centre is：
Wherein, NkIt is effective bright spot quantity of bright spot center k.
7. according to the method described in claim 2, it is characterized in that, the course of the target is obtained with length by following formula
It takes：
φ=± atan2 (C2,C1),
Wherein, C=[C1 C2]T, μkThe coordinate at bright spot center is represented, vector C represents the slope of straight line,
T represents straight line in the position of plane.
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