CN107063267A - A kind of quickly localization method based on sun shadow information - Google Patents
A kind of quickly localization method based on sun shadow information Download PDFInfo
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- CN107063267A CN107063267A CN201710317165.5A CN201710317165A CN107063267A CN 107063267 A CN107063267 A CN 107063267A CN 201710317165 A CN201710317165 A CN 201710317165A CN 107063267 A CN107063267 A CN 107063267A
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- 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
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- 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
- G01C21/02—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by astronomical means
Abstract
The present invention relates to the localization method quickly based on sun shadow information, comprise the following steps：1), user submits data (picture, video, or the array data of shadow length) to be positioned；2) data (picture, video, or the array data of shadow length) for, providing user are uniformly converted into sun altitude array data by three-dimensional reduction calibration algorithm；3) the mathematical modeling environment of solution, is established；The beneficial effects of the invention are as follows：Fish-swarm algorithm can make it in speed and can reach outstanding level in precision, possess quick, the stable and less feature of error, performance is better than other algorithms.
Description
Technical field
The present invention relates to a kind of localization method, and in particular to a kind of quickly localization method based on sun shadow information.
Background technology
Existing technology is all that the positioning of sun shadow information is carried out by trellis traversal method, does not account for solution and asks
Topic can be used carries out location Calculation with enlightening method, and the time complexity of calculating is very huge to calculate slow.Can not
The solution of complete dual problem can not particularly be applied to the Real-time solution of mobile terminal in a short time.
The content of the invention
The purpose of the present invention is to overcome deficiency of the prior art to quickly finish sun shadow information there is provided one kind
Localization method.
This quickly localization method based on sun shadow information, comprises the following steps：
1), user submits data (picture, video, or the array data of shadow length) to be positioned；
2) data (picture, video, or the array data of shadow length) for, providing user are calculated by three-dimensional reduction demarcation
Method is uniformly converted into sun altitude array data；
2.1) region segmentation, is carried out to picture；
2.2) binaryzation denoising, is carried out to the effective coverage after segmentation, cromogram changed into black and white black
Bai Tu；
2.3), by the length in pixels and the length in pixels of shadow of extracting object, and according to corresponding length in pixels and three
Tie up projection formula and calculate sun altitude, solar azimuth；
2.4) the solar direction angle and then will conversion formed is exported；
3) the mathematical modeling environment of solution, is established；
Shadow length obviously with latitude, longitude, date, four variables such as (unit is minute) are closely related during clock；Solve
The problem of spot for photography, is converted into following four parametric programmings problem；The target of planning is to find the error sum of squares minimum of shadow length
The optimum combination of tetra- parameters of x, y, n, t；
s.t.：0≤x≤180.0；0≤y≤180.0；0≤n≤360；0≤t≤1440 (1)
Wherein E represents the accumulative quadratic sum of the long error of shadow, x, y, n represent required spot for photography longitude, latitude, the date and
Zhong Shi, k represent the number of data available；G (x, y, n, t) is the long data of shadow extracted from picture；F (x, y, n, t) represent with
X, y, n, t are raw material, and long according to the shadow that mathematical formulae calculating is obtained, calculation formula is as follows：
F (x, y, n, t)=L/tan (arcsin (sin α sin y+cos α cos ycos ω)) (2)
Sin α=0.39795cos [0.98563 (N-173)] (3)
ω=15* (T+ (120 ° of-x)/15 ° -12) (4)
Wherein (2) formula, which is defined in the ratio of a length of actual bar length of shadow and local time sun altitude, L representative images, shadow
The actual height of object；α represents sun altitude, and (3), (4) formula are used as sun altitude for the declination and solar hour angle of the sun
Calculation formula；α represents the date corresponding solar declination in formula, and ω represents solar hour angle at that time；
4), camera site is positioned using fast and effectively heuristic fish-swarm algorithm；
4.1), initialize：Determine population scale N, set Artificial Fish visual range, Artificial Fish step-length, the crowding factor, and
The maximum exploration number of times that Artificial Fish is looked for food.Generate individual at random in feasible zone, and number of times is soundd out as maximum；
4.2), take optimal Artificial Fish state and be assigned to bulletin board, algorithm terminates if satisfied；
4.3) operation of bunching, is performed；
4.4) operation of knocking into the back, is performed；
4.5) operation of looking for food, is performed；
4.6), it is recycled back into step 2.2)；
5), the position of all Artificial Fishs is determined and the position of classic Artificial Fish is compared as optimal
Solution；
6), by final optimal solution, solution procedure amount drawing process curve feedback is to user.
As preferred：Step 2.3) concretely comprise the following steps：OH is rod, and HA is light, and OA is that rod irradiates in sunshine
The shadow of lower formation, Δ x, Δ y represent the variable quantity of transverse and longitudinal coordinate.TOAFor OA projected length.It is assumed that camera is to object
Distance is J, in the case that the height of camera is n, can be obtained：
As preferred：Step 4.3) concretely comprise the following steps：The current state of Artificial Fish is Pa, detect in its adjacent Artificial Fish
P in the best statemax, and partner's number, ifLess than congestion quotiety σ, and Ya＜ Ymax, show that it nearby has more worth visit
The region (the smaller possibility longitude and latitude of error is more likely found in region) of rope, and entirely Artificial Fish population is explored also to it
It is insufficient to, then to PcTake a step forward：
Otherwise this behavior is not performed.
As preferred：Step 4.4) concretely comprise the following steps：Assuming that the current state of Artificial Fish is Pa, detect the P of its neighborhoodb
The long deviation accumulation quadratic sum E of shadow that position is calculatedb, and partner number N, ifLess than congestion quotiety σ, and Eb＜ Ea, table
Its bright neighbouring region (the smaller possibility longitude and latitude of error is more likely found in region) that there is more worth exploration, and entirely
Artificial Fish population is explored to it to be also insufficient to, then to PcTake a step forward：
Otherwise this behavior is not performed.
As preferred：Step 4.5) concretely comprise the following steps：The position of current manual fish represents the longitude and latitude currently explored
Position, is designated as Pa, when being looked for food, in its field range VaInterior random one longitude and latitude P of selectionbIf, PbPosition is calculated
The long deviation accumulation quadratic sum E of shadowbLess than the long deviation accumulation quadratic sum E of shadow that position is calculateda, then show in PbPosition more has
It is probably the solution that we find, that is, is more suitable for its existence for Artificial Fish, Artificial Fish can be to PbPosition is moved, by such as
Lower equation：
Wherein, η is mobile step-length, and rand () is the random number of generation, the randomness of performance Artificial Fish movement；If with
The position P of machine selectionbE can not be madebLess than Ea, then reselect Pb, until reaching certain times N, or successfully move one
Step.
The beneficial effects of the invention are as follows：Fish-swarm algorithm can make it in speed and can reach outstanding water in precision
It is flat, possess quick, the stable and less feature of error, performance is better than other algorithms.
Brief description of the drawings
Fig. 1 is sun shadow formation schematic diagram；
Fig. 2 is that two-dimension picture reduces three-dimensional information three-view diagram；
Fig. 3 is the pseudo- representation figure of fish-swarm algorithm；
Fig. 4 to Fig. 6 is searching times and fish school location schematic diagram；
Fig. 7 is accuracy iterations analysis chart.
Embodiment
The present invention is described further with reference to embodiment.The explanation of following embodiments is only intended to help and understands this
Invention.It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention, also
Some improvement and modification can be carried out to the present invention, these are improved and modification also falls into the protection domain of the claims in the present invention
It is interior.
1 principle summary
Fast and effeciently determine that the spot for photography of video or picture is collected evidence for judicial authority, there is the automatic classification of shadow object
Carry out verifying there is very big practical significance with other alignment systems to GPS etc..But be not molded outside Current Domestic from
The software or product of effective longitude and latitude degrees of data are extracted in picture or continuous real video.Pass through the change rail of sun shade
Mark to shooting the idea estimated of approximate location of picture, video, by have the geometrical relationship of shadow object and shadow track come
Estimation shoots the position of photo.Propose that the position that object is shot depends on projected objects place Position Latitude and sunshine direction
Geometrical relationship between projection plane.
2 algorithm principle explanations
Shown in Fig. 1, if the shooting time of continuous videos is longer, with the passage of time, on level ground, sunshine production
Raw shadow can be produced with the change of the sun in length and angle on change, while the length and angle change of shadow
Amount is relevant with spot for photography (i.e. longitude and latitude).So can utilize the change of shadow is counter to push away spot for photography in theory.The application couple
Mathematical modeling, the storage mode of data are simplified, and be instead of traditional algorithm using didactic fish-swarm algorithm, are made model
Operation time compares conventional method and shortens 80%.
The derivation of 3 positioning mathematical modelings
Shadow length obviously with latitude, longitude, date, four variables such as (unit is minute) are closely related during clock.Solve
The problem of spot for photography, can be converted into following four parametric programmings problem.The target of planning be find shadow length error sum of squares most
The optimum combination of small tetra- parameters of x, y, n, t.
s.t.：0≤x≤180.0；0≤y≤180.0；0≤n≤360；0≤t≤1440 (9)
Wherein E represents the accumulative quadratic sum of the long error of shadow, x, y, n represent required spot for photography longitude, latitude, the date and
Zhong Shi, k represent the number of data available.G (x, y, n, t) is the long data of shadow extracted from picture.F (x, y, n, t) represent with
X, y, n, t are raw material, and long according to the shadow that mathematical formulae calculating is obtained, calculation formula is as follows：
F (x, y, n, t)=L/tan (arcsin (sin α sin y+cos α cos ycos ω)) (10)
Sin α=0.39795cos [0.98563 (N-173)] (11)
ω=15* (T+ (120 ° of-x)/15 ° -12) (12)
Wherein (10) formula is defined has in the ratio of a length of actual bar length of shadow and local time sun altitude, L representative images
The actual height of shadow object.α represents sun altitude, and the calculating of sun altitude is having detailed introduction, here without superfluous
State.(11), (12) formula for the sun declination and solar hour angle be used as sun altitude calculation formula.α represents the date in formula
Corresponding solar declination, ω represents solar hour angle at that time.
In order to successfully solve the planning, it is necessary to carry out extracting the data used from picture or video, then in thing
Under the hypothesis that body is upright and object distance can be estimated, coordinate length conversion as shown in Figure 2 is done.
Wherein OH is rod, and HA is light, and OA is the shadow that rod is formed under sunshine irradiation, and Δ x, Δ y represent horizontal
The variable quantity of ordinate.TOAFor OA projected length.It is assumed that camera is J to object distance, the height of camera is n.'s
In the case of, it can obtain：
3 fish-swarm algorithms realize step
Fish-swarm algorithm is the strategy for imitating the commune optimization that fish in groups take when looking for food, and its basic thought is,
In the environment of Fish Survival, food most abundant place, is also often the most place of fish collection.Fish-swarm algorithm is according to fish
Class is looked for food, and is bunched, and the behavior such as knock into the back creates some i.e. self-controls and can carry out the Artificial Fish of effective information exchange.Each
The position of Artificial Fish all represents an effectively solution, and the waters of Artificial Fish existence has corresponded to the solution space of optimization problem, food
Concentration has corresponded to the value of object function, travelling in target water by Artificial Fish, describes whole searching process.
The pseudo- representation of algorithm is as shown in Figure 3：
1) initialize：Population scale N is determined, Artificial Fish visual range, Artificial Fish step-length, the crowding factor, and people is set
The maximum exploration number of times that work fish is looked for food.Generate individual at random in feasible zone, and number of times is soundd out as maximum.
2) take optimal Artificial Fish state and be assigned to bulletin board, algorithm terminates if satisfied.
3) by looking for food, bunch, the behavior of knocking into the back carries out the respective optimizing of each Artificial Fish
4) return to step 2)
The method that algorithm is applied into solution video location scene is as follows：
Desirable interval：
The movable waters scope of desirable interval as Artificial Fish, because the involved data of this paper are gathered in China, so using
0~180 ° of east longitude, 0~180 ° of north latitude as may be interval, that is, Artificial Fish movable waters.
Foraging behavior：
The position of current manual fish represents the longitude and latitude position currently explored, and is designated as Pa, when being looked for food, in its visual field
Scope VaInterior random one longitude and latitude P of selectionbIf, PbThe long deviation accumulation quadratic sum E of shadow that position is calculatedbLess than position
The long deviation accumulation quadratic sum E of shadow calculateda, then show in PbPosition is more likely the solution that we find, that is, to artificial
It is more suitable for its existence for fish, Artificial Fish can be to PbPosition is moved, and passes through equation below：
Wherein, η is mobile step-length, and rand () is the random number of generation, the randomness of performance Artificial Fish movement.If with
The position P of machine selectionbE can not be madebLess than Ea, then reselect Pb, until reaching certain times N, or successfully move one
Step.
Bunch behavior：
In view of in reality, functional relation when error E is with latitude, longitude date clock is continuous in most of solution room
, then the possibility of the optimal solution to problem on the side beside the less longitude and latitude of error is bigger, is worth taking great pains to visit
Rope.Assuming that the current state of Artificial Fish is Pa, detect the P of its neighborhoodbThe long deviation accumulation quadratic sum E of shadow that position is calculatedb, with
Partner number N, ifLess than congestion quotiety σ, and Eb＜ Ea, show that it nearby has the region of more worth exploration (in region
More likely find the smaller possibility longitude and latitude of error), and whole Artificial Fish population explores to it and is also insufficient to, then to PcBefore
Further：
Otherwise this behavior is not performed.
Knock into the back behavior：
Similar with behavior of bunching, the side of the longitude and latitude of the error minimum in whole population is equally worth exploring.Manually
The current state of fish is Pa, detect P in the best state in its adjacent Artificial Fishmax, and partner's number, ifLess than congestion quotiety
σ, and Ya＜ Ymax, show that it nearby (more likely finds the smaller possibility of error in the presence of the region of more worth exploration in region
Longitude and latitude), and whole Artificial Fish population explores to it and is also insufficient to, then to PcTake a step forward：
Otherwise this behavior is not performed.
The solving result example of fish-swarm algorithm：
The search result of fish-swarm algorithm is as shown in Figures 4 to 6.Clean and tidy for picture, we are by longitude, latitude and date
3-D view be reduced to the two-dimensional search image of longitude, latitude.
When wherein (a) figure (b) figure (c) figure represents beginning state, during by 20 iteration and by 300 iteration, at random
The location of Artificial Fish of generation.Abscissa is longitude in each pictures, and ordinate is latitude, the hexagon table of black
Show the position of Artificial Fish, the position of the correct object of hexagonal star representation of black.It will be clear that when iteration time in figure
When number reaches 300 times, it is already possible to obtain outstanding solution.
For more preferable analytical precision and the relation of iterations, image such as Fig. 7 of accuracy and iterations：
Picture shows that accuracy rapid growth before the 50th convergence reaches the level close to 1.Therefore can consider
Fish-swarm algorithm pair is effective with solving this problem.
The speed ratio of fish-swarm algorithm and traditional algorithm compared with：
In order to show the rapidity that fish-swarm algorithm solves this problem, pass through grid-search algorithms, greedy algorithm, two sections of greediness
Method and the comparison data of fish-swarm algorithm are illustrated.
Grid-search algorithms are that parameter to be searched is divided into grid in certain spatial dimension, by traveling through in grid
All point finds optimized parameter.This method is interval sufficiently large and can find out in the case that step pitch is sufficiently small complete in optimizing
Office's optimal solution.
Greedy algorithm refers to, when to problem solving, always makes and is currently appearing to be best selection.That is,
Greedy algorithm will not overall thinking algorithm superiority-inferiority, can only obtain locally optimal solution in some sense.
Two sections of greedy rules the characteristics of both to being merged.First stage determines big position approximate by quick greedy method
Put, final solution is then obtained using Gird Search.
1 three kinds of algorithm positioning results of table compare
Form shown, several algorithms studied are divided into two classes by us, traditional algorithm (including Gird Search algorithm,
Greedy algorithm, two sections of greedy methods) and intelligent algorithm (including genetic algorithm, particle cluster algorithm, fish-swarm algorithm).It is generally speaking traditional
There is the slow slow defect of speed in algorithm：Although wherein grid rope search algorithm is adjustable in precision, the time ten of computing
Divide huge；There is very big uncertainty in greedy algorithm, its success or not searched for does not depend entirely on its initial position to most
It whether there is locally optimal solution between excellent solution；Although two sections of greedy methods make improvement to above two algorithm to a certain extent,
But perfect condition can not be reached in performance.
The application of intelligent algorithm can solve this problem well, and solve the effect of problem and be better than traditional algorithm,
But the adjustment of corresponding calculating parameter is all relied on, and influenceed to a certain extent by randomness.
The convergence error of genetic algorithm is smaller from the point of view of three kinds of intelligent algorithm lateral comparisons, but the time of search is relatively
It is long, and in test it is observed that it is higher for the sensitiveness of initial population, that is to say, that for generating at random not
For initial population, convergent lead time is larger.
Particle cluster algorithm is better than genetic algorithm in speed, but its precision has larger error.By the adjustment of parameter,
It was found that fish-swarm algorithm can make it in speed and can reach outstanding level in precision, possess quick, stabilization is simultaneously
And the less feature of error, performance is better than remaining two kinds of intelligent algorithm.For this problem solution can fish-swarm algorithm can be 30
Optimal solution is drawn in second.
Claims (5)
1. a kind of quickly localization method based on sun shadow information, it is characterised in that comprise the following steps：
1), user submits data (picture, video, or the array data of shadow length) to be positioned；
2) data (picture, video, or the array data of shadow length) for, providing user are united by three-dimensional reduction calibration algorithm
One is converted into sun altitude array data；
2.1) region segmentation, is carried out to picture；
2.2) binaryzation denoising, is carried out to the effective coverage after segmentation, cromogram is changed into black and white artwork master；
2.3), by the length in pixels and the length in pixels of shadow of extracting object, and thrown according to corresponding length in pixels with three-dimensional
Shadow formula calculates sun altitude, solar azimuth；
2.4) the solar direction angle and then will conversion formed is exported；
3) the mathematical modeling environment of solution, is established；
Shadow length obviously with latitude, longitude, date, four variables such as (unit is minute) are closely related during clock；Solve and shoot
The problem of place, is converted into following four parametric programmings problem；The target of planning be find the minimum x of error sum of squares of shadow length, y,
The optimum combination of tetra- parameters of n, t；
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Wherein E represents the accumulative quadratic sum of the long error of shadow, and x, y, n represents required spot for photography longitude, latitude, date and clock
When, k represents the number of data available；G (x, y, n, t) is the long data of shadow extracted from picture；F (x, y, n, t) is represented with x,
Y, n, t are raw material, and long according to the shadow that mathematical formulae calculating is obtained, calculation formula is as follows：
F (x, y, n, t)=L/tan (arcsin (sin α siny+cos α cosycos ω)) (2)
Sin α=0.39795cos [0.98563 (N-173)] (3)
ω=15* (T+ (120 ° of-x)/15 ° -12) (4)
Wherein (2) formula, which is defined in the ratio of a length of actual bar length of shadow and local time sun altitude, L representative images, shadow object
Actual height；α represents sun altitude, and (3), (4) formula are that the declination and solar hour angle of the sun are used as the meter of sun altitude
Calculate formula；α represents the date corresponding solar declination in formula, and ω represents solar hour angle at that time；
4), camera site is positioned using fast and effectively heuristic fish-swarm algorithm；
4.1), initialization：Determine population scale N, set Artificial Fish visual range, Artificial Fish step-length, the crowding factor, and manually
The maximum exploration number of times that fish is looked for food.Generate individual at random in feasible zone, and number of times is soundd out as maximum；
4.2), take optimal Artificial Fish state and be assigned to bulletin board, algorithm terminates if satisfied；
4.3) operation of bunching, is performed；
4.4) operation of knocking into the back, is performed；
4.5) operation of looking for food, is performed；
4.6), it is recycled back into step 2.2)；
5), the position of all Artificial Fishs is determined and the position of classic Artificial Fish is compared as optimal solution；
6), by final optimal solution, solution procedure amount drawing process curve feedback is to user.
2. the quickly localization method based on sun shadow information according to claim 1, it is characterised in that step 2.3)
Concretely comprise the following steps：OH is rod, and HA is light, and OA is the shadow that rod is formed under sunshine irradiation, and Δ x, Δ y represent horizontal
The variable quantity of ordinate.TOAFor OA projected length.It is assumed that camera is J to object distance, the height of camera is n feelings
Under condition, it can obtain：
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3. the quickly localization method based on sun shadow information according to claim 1, it is characterised in that：Step 4.3)
Concretely comprise the following steps：The current state of Artificial Fish is Pa, detect P in the best state in its adjacent Artificial Fishmax, and partner's number, such as
ReallyLess than congestion quotiety σ, and Ya＜ Ymax, show that it nearby (is more likely looked in the presence of the region of more worth exploration in region
To the smaller possibility longitude and latitude of error), and whole Artificial Fish population explores to it and is also insufficient to, then to PcTake a step forward：
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<mo>|</mo>
<msub>
<mi>P</mi>
<mi>max</mi>
</msub>
<mo>-</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
<mo>|</mo>
<mo>|</mo>
</mrow>
</mfrac>
<mi>&eta;</mi>
<mo>&CenterDot;</mo>
<mi>r</mi>
<mi>a</mi>
<mi>n</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Otherwise this behavior is not performed.
4. the quickly localization method based on sun shadow information according to claim 1, it is characterised in that：Step 4.4)
Concretely comprise the following steps：
Assuming that the current state of Artificial Fish is Pa, detect the P of its neighborhoodbThe long deviation accumulation quadratic sum E of shadow that position is calculatedb, with
Partner number N, ifLess than congestion quotiety σ, and Eb＜ Ea, show that it nearby has the region of more worth exploration (in region
More likely find the smaller possibility longitude and latitude of error), and whole Artificial Fish population explores to it and is also insufficient to, then to PcBefore
Further：
<mrow>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mi>c</mi>
</msub>
<mo>-</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
</mrow>
<mrow>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>P</mi>
<mi>c</mi>
</msub>
<mo>-</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
<mo>|</mo>
<mo>|</mo>
</mrow>
</mfrac>
<mi>&eta;</mi>
<mo>&CenterDot;</mo>
<mi>r</mi>
<mi>a</mi>
<mi>n</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Otherwise this behavior is not performed.
5. the quickly localization method based on sun shadow information according to claim 1, it is characterised in that：Step 4.5)
Concretely comprise the following steps：The position of current manual fish represents the longitude and latitude position currently explored, and is designated as Pa, when being looked for food,
Its field range VaInterior random one longitude and latitude P of selectionbIf, PbThe long deviation accumulation quadratic sum E of shadow that position is calculatedbIt is small
The long deviation accumulation quadratic sum E of shadow calculated in positiona, then show in PbPosition is more likely the solution that we find, that is,
It is more suitable for its existence for Artificial Fish, Artificial Fish can be to PbPosition is moved, and passes through equation below：
<mrow>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mrow>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>=</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
<mo>+</mo>
<mfrac>
<mrow>
<msup>
<msub>
<mi>P</mi>
<mi>b</mi>
</msub>
<mi>t</mi>
</msup>
<mo>-</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
</mrow>
<mrow>
<mo>|</mo>
<mo>|</mo>
<msup>
<msub>
<mi>P</mi>
<mi>b</mi>
</msub>
<mi>t</mi>
</msup>
<mo>-</mo>
<msup>
<msub>
<mi>P</mi>
<mi>a</mi>
</msub>
<mi>t</mi>
</msup>
<mo>|</mo>
<mo>|</mo>
</mrow>
</mfrac>
<mi>&eta;</mi>
<mo>&CenterDot;</mo>
<mi>r</mi>
<mi>a</mi>
<mi>n</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, η is mobile step-length, and rand () is the random number of generation, the randomness of performance Artificial Fish movement；If random choosing
The position P selectedbE can not be madebLess than Ea, then reselect Pb, until reaching certain times N, or successfully shifting moves a step.
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| CN108801220A (en) * | 2018-05-31 | 2018-11-13 | 西南交通大学 | The processing method of vedio data based on sun shadow positioning |
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