CN102840860A - Hybrid particle swarm algorithm-based star graph recognition method - Google Patents
Hybrid particle swarm algorithm-based star graph recognition method Download PDFInfo
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- CN102840860A CN102840860A CN2012103151841A CN201210315184A CN102840860A CN 102840860 A CN102840860 A CN 102840860A CN 2012103151841 A CN2012103151841 A CN 2012103151841A CN 201210315184 A CN201210315184 A CN 201210315184A CN 102840860 A CN102840860 A CN 102840860A
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Abstract
The invention discloses a hybrid particle swarm algorithm-based star graph recognition method, which comprises two steps of: establishing a navigation star database and performing star graph recognition based on a hybrid particle swarm algorithm, wherein the radius r of a circle is determined through an adaptive adjustment method, and quick path optimization is performed by using the hybrid particle swarm algorithm. According to the hybrid particle swarm algorithm-based star graph recognition method provided by the invention, the defects of low star graph recognition rate and poor noise robustness under conditions of a large visual field and a high-sensitivity fixed star sensor are overcome; and quick matching recognition of a navigation star is realized, the recognition rate is increased, and the noise robustness is better.
Description
Technical field
The present invention relates to a kind of method for recognising star map, belong to spacecraft Navigation, Guidance and Control field based on Hybrid Particle Swarm.
Background technology
Star sensor is based on the main working parts of the celestial navigation system of star chart coupling recognition technology, and star map matching recognizing method is the core of star sensor technology, can obtain the attitude information of carrier rapidly and accurately.Therefore, it is fast to study a kind of recognition speed, and the star Pattern Recognition Algorithm that recognition success rate is high has important theory and realistic meaning.
Many comparatively ripe star map matching recognizing methods have been had at present; Like the triangle algorithm, convex polygon coupling recognizer, grid algorithm, based on Delaunay triangulation method of identification, based on the star Pattern Recognition Algorithm of neural network, based on the method for recognising star map of genetic algorithm and based on the method for recognising star map of ant group algorithm etc.These star Pattern Recognition Algorithm have all reached comparatively good effect, but they respectively have quality in recognition speed, recognition success rate, database size and on to the robustness of noise; Under big visual field, highly sensitive Star Sensor condition,, asterism barycenter many owing to big, the responsive asterism of optical system distortion extracts reasons such as difficulty especially, and recognition speed and success ratio obviously reduce especially.
Summary of the invention
The objective of the invention is to solve the slow and low problem of recognition success rate of importance in star map recognition speed of Star Sensor under the large viewing field condition.The present invention proposes a kind of method for recognising star map, improved the success ratio of importance in star map recognition significantly and the robustness of noise based on Hybrid Particle Swarm.
A kind of method for recognising star map based on Hybrid Particle Swarm that the present invention proposes comprises following step:
Step 1: set up navigation star database, realize the structure of star Pattern Recognition Algorithm navigation star database;
Step 2: the identification of star chart coupling, utilize Hybrid Particle Swarm to be nautical star structural attitude pattern, adopt binary chop that nautical star is mated identification then.
The invention has the advantages that:
(1) method for recognising star map disclosed by the invention adopts Hybrid Particle Swarm that asterism is carried out optimum path search; Carry out index based on the optimal path length value; And utilize that the angular distance between nautical star mates identification in the optimal path, and improved the discrimination of method, reduced match time;
(2) method for recognising star map disclosed by the invention has proposed the method that a kind of self-adaptation is confirmed radius of circle r, and the optimizing scale that makes this recognition methods has guaranteed the real-time of method in the reasonable scope.
(3) star Pattern Recognition Algorithm disclosed by the invention has added nautical star checking link, has improved the accuracy of recognition result, has reduced redundant coupling simultaneously.
Description of drawings
Fig. 1 is a method flow diagram of the present invention;
Fig. 2 is radius of circle r of the present invention when getting 4 °, and the asynchronous nautical star number of asterism number distributes in the circle;
Fig. 3 is that the present invention is less than 6 nautical star to asterism in the r=4 ° of circle, when getting radius r=5 ° in the circle asterism number distribute;
Fig. 4 is the present invention to asterism in the r=4 ° of circle more than 25 nautical star, when getting radius r=2.5 ° in the circle asterism number distribute;
Fig. 5 be the present invention propose based on asterism optimum path search figure in the circle of Hybrid Particle Swarm;
Fig. 6 is the method for recognising star map flow process based on Hybrid Particle Swarm that the present invention proposes;
Fig. 7 is the method discrimination that proposes of the present invention and the graph of a relation of asterism site error.
Embodiment
To combine accompanying drawing and embodiment that the present invention is done further detailed description below.
The present invention is a kind of method for recognising star map based on Hybrid Particle Swarm, and method flow is as shown in Figure 1, comprises following step:
Step 1: set up navigation star database, realize the structure of method for recognising star map navigation star database
To the method for recognising star map based on Hybrid Particle Swarm, the building method of its navigation star database is following:
The first step: choose nautical star, make up the basic navigation star catalogue
From fundamental catalog, choose nautical star; Angular distance between fixed star is rejected less than the double star of a certain value (generally getting 3-5 pixel); Obtain being used for the basic navigation star catalogue of importance in star map recognition, the basic navigation star catalogue comprises numbering, magnitude, right ascension and the declination information of each nautical star.
Second step: the characteristic set of constructing every nautical star
To every nautical star in the basic navigation star catalogue, be the center of circle with the nautical star, carry out the picture circle with radius r, all nautical stars in the circle are formed the characteristic set of center of circle nautical star.
Importance in star map recognition is a kind of feature matching method, need be every unique feature mode of nautical star structure, utilizes the coupling identification of the characteristic realization nautical star of structure then.When the structural attitude pattern, need given radius of circle r based on the method for recognising star map of Hybrid Particle Swarm, and the number of nautical star in the size of the radius of circle r influence circle, and then the complexity of decision Hybrid Particle Swarm optimum path search.For accuracy and the real-time that improves method for recognising star map, the present invention proposes a kind of self-adapting regulation method and confirm radius of circle r, with the optimizing scale that guarantees Hybrid Particle Swarm in the reasonable scope, concrete steps comprise:
(1) is the center with a nautical star, gets radius of circle r=4 °, the asterism number in the statistics circle; When the asterism number less than 6 the time, get into step (2), when the asterism number greater than 25 the time; Get into step (3), when the asterism number more than or equal to 6 and smaller or equal to 25 the time, confirm radius of circle r=4 °;
For guaranteeing the accuracy and the real-time of method for recognising star map, the present invention adjusts the radius of circle r of above two kinds of situation;
(2) the asterism number in circle is less than 6 the time, and increase radius of circle r is 5 °;
(3) the asterism number in circle is greater than 25 the time, and reducing radius of circle r is 2.5 °.
The 3rd step: choose a nautical star in the basic navigation star catalogue; Obtain in the characteristic set angular distance between fixed star in twos, the nautical star with circle centre position in the characteristic set is a starting point then, utilizes Hybrid Particle Swarm to carry out the fast path optimizing; Obtain the optimal path of characteristic set; Obtain the length of optimal path, and to select in the optimal path from the nearest nautical star in the center of circle be the working direction of optimal path, obtain first three nautical star of optimal path.
The fast path optimizing step of said Hybrid Particle Swarm is following:
(1) supposes total m nautical star in the characteristic set, at first the nautical star in the characteristic set encoded that the order that particle is traveled through all nautical stars is arranged with natural number, as separating of particle; The parameter of initialization Hybrid Particle Swarm then, the setting particle number is n, the iterations maximal value is Nmax; Produce n initial solution (initial path) then at random; And particle traveled through the fitness function of the path sum of all nautical stars as particle; Calculate fitness value according to the current location of particle then, set the individual extreme value of each particle, and the global extremum of all particles.
(2) current the separating with individual extreme value of i particle intersected; Concrete cross method is: separate for the current of particle; Select an intersection region (one piece of data in separating) at random; With the identical element in individual extreme value intersection region, then the element in the individual extreme value intersection region is inserted the current of particle and separate during deletion is separated then, obtain the new explanation of i particle.
(3) new explanation that step (2) is obtained intersects with global extremum; Concrete cross method is: separate for the current of particle; Select an intersection region at random; The element identical with the global extremum intersection region during deletion is separated inserts the current of particle with the element in the global extremum intersection region then and separates, and obtains the new explanation of i particle.
(4) new explanation that step (3) is obtained makes a variation; For making the path sum reach minimum; Adopt the roulette back-and-forth method; Choose in the path two big nautical stars of angular distance between adjacent nautical star with bigger probability (nautical star that probability is big more is prone to be selected), between them, insert other nautical star, the new explanation after obtaining making a variation then.
Concrete grammar is: the angular distance in the new explanation that obtaining step (3) obtains between adjacent nautical star is designated as l (k) respectively, k=1; 2 ..., m; Wherein, the angular distance during l (1) expression is separated between first and second nautical star, by that analogy; During l (m-1) expression is separated m-1 with m nautical star between angular distance, the angular distance during l (m) representes to separate between m and first nautical star, then every selecteed probability of nautical star is:
According to the probability that following formula is tried to achieve, adopt the roulette back-and-forth method to choose nautical star i
1When adopting the roulette back-and-forth method, the fritter that every nautical star in separating is similar in the wheel disc is fan-shaped, and fan-shaped area size is directly proportional with the selecteed probability of this nautical star, and sectorial area is big more, and then the selecteed probability of this nautical star is also big more.
Chosen nautical star i
1After, choose from asterism i with bigger probability
1Nearer asterism is as the next one traversal point of particle.If d is (i
1, nautical star i during j) expression is separated
1And the distance between nautical star j then can calculate from nautical star i
1The distance of nautical star is d farthest
Max, wherein
K=1 ..., m and k ≠ i
1, for preventing to travel through nautical star i
1After next nautical star be i
1Itself makes d (i
1, i
1)=d
Max, then every nautical star is chosen as the probability of next traversal nautical star and is:
According to the probability that following formula is tried to achieve, adopt the roulette back-and-forth method to choose asterism j
1, the asterism that promptly probability is big more is prone to be selected, then nautical star j
1Be arranged into nautical star i
1Afterwards, all the other are constant, the new explanation after obtaining making a variation.
(5) carry out the renewal of individual extreme value; Concrete grammar is: the fitness value that at first calculates i particle variation back new explanation; And compare with the fitness value of the individual extreme value of this particle, obtain the fitness value variable quantity of i particle, adopt mechanism of Simulated Annealing to determine whether to accept this new explanation then; If accept, the individual extreme value of new particle more then.
Judge whether the individual extreme value of all particles is all upgraded,, get into step (6), otherwise return step (2) if all upgrade.
(6) carry out the renewal of global extremum,, confirm global extremum and global extremum path according to the individual extreme value of all particles; Judge that whether iterations is greater than iterations maximal value Nmax; If greater than, then optimum path search finishes, and the global extremum path is optimal path; Otherwise, the step of returning (2).
The 4th step: the information of obtaining first three nautical star in the optimal path; Promptly obtain first nautical star, second nautical star and the 3rd the pairing right ascension of nautical star, declination information and magnitude respectively, the angular distance information between first nautical star and second nautical star, between second nautical star and the 3rd nautical star, between first nautical star and the 3rd nautical star.
Angular distance information between first nautical star and second nautical star, between second nautical star and the 3rd nautical star; First nautical star, second nautical star and the 3rd right ascension, the declination information that nautical star is corresponding respectively are as the basic identifying information of nautical star.
The angular distance between first nautical star and the 3rd nautical star and the magnitude of first nautical star, second nautical star and the 3rd nautical star are as the authorization information of importance in star map recognition.
The 5th step: repeated for the 3rd step and the 4th step, obtain the characteristic information of each nautical star in the navigational star table, characteristic information comprises the basic identifying information of optimal path length, nautical star of nautical star and the authorization information of importance in star map recognition.Nautical star identifying information for nautical star radius r=5 ° is arranged according to optimal path length ascending order; Constitute the first of navigation star database; Nautical star identifying information for radius r=4 ° is arranged according to optimal path length ascending order, constitutes the second portion of navigation star database, arranges according to optimal path length ascending order for the nautical star identifying information of radius r=2.5 °; Constitute the third part of navigation star database, this three part constitutes a complete navigation star database at last.
Step 2: carry out importance in star map recognition based on Hybrid Particle Swarm;
For a width of cloth star chart; Extract through star chart pre-service, celestial body, obtain the position coordinates and the half-tone information of each asterism in the star chart, carry out the identification of star chart coupling then; As shown in Figure 6, specifically comprise following step based on the method for recognising star map of Hybrid Particle Swarm:
The first step: confirm that the candidate discerns primary; The identification primary of choosing should have brightness big and from the field of view edge angular distance all greater than the characteristics of radius of circle r; Choosing method is following: calculate in the star chart all from the field of view edge angular distance all greater than the average gray value of the asterism of radius of circle r; Choose gray-scale value then and discern primary as the candidate, obtain the candidate and discern the primary set greater than the asterism of average gray value.
The maximal value that the user is provided with the recognition failures number of times is N, and the identification frequency of equipment, method is n, and the initial value of n is made as 0.
Second step: carry out the optimizing of asterism fast path, in the candidate discerns primary set, at first choose the near asterism of from the center, visual field n+1 as the center of circle (when promptly discern the first time; Select nearest asterism, when discerning for the second time, select time near asterism; And the like); Carry out picture circle with radius r then, said method of second step is identical in definite method of radius r and the step 1, with all the asterism constitutive characteristic data acquisitions in the circle; As shown in Figure 5, the gray-scale value that wherein white asterism is represented this asterism is greater than average gray value.
Interasteric in twos angular distance in the calculated characteristics data acquisition; Asterism with circle centre position in the characteristic set is a starting point then; Utilize Hybrid Particle Swarm to carry out the fast path optimizing, obtain the optimal path of asterism characteristic set, obtain the length of optimal path; Selecting to leave the nearest asterism in the center of circle in the optimal path is the working direction of optimal path, obtains first three asterism in the optimal path.
Wherein, it is identical with the fast path optimization method of Hybrid Particle Swarm described in the 3rd step of step 1 to utilize Hybrid Particle Swarm to carry out the fast path optimizing, the characteristic set of asterism is substituted the data characteristics set of nautical star.
Obtain the information of first three asterism in the optimal path, promptly obtain first asterism, second asterism and the 3rd the pairing magnitude of asterism respectively, first asterism and second interasteric angular distance d
12, second asterism and the 3rd interasteric angular distance d
23, first asterism and the 3rd interasteric angular distance d
13
The 3rd step: carry out the identification of star chart coupling,, confirm the part of this asterism respective radii r in navigation star database according to the radius r of asterism; Go on foot the optimal path length that obtains according to second; Adopt binary chop, search in the navigation star database and the close nautical star of this asterism optimal path length, and discern star to them as the candidate; Then, with the d that obtains in second step
12And d
23The basic identifying information of discerning star with the candidate matees, and obtains matching result.Matching result is for no nautical star matees, only perhaps there are many nautical stars couplings in a nautical star coupling.
The 4th step: the checking of nautical star recognition result, the matching result that the 3rd step obtained is verified concrete verification method is following:
(1) if only a nautical star matees, then utilize second to go on foot the angular distance d that obtains
13Mate with the importance in star map recognition authorization information of nautical star, if on the coupling, then this width of cloth importance in star map recognition success, if on not mating, then star chart coupling failure this time, importance in star map recognition frequency of failure n adds 1;
(2) if do not have on the nautical star coupling, then it fails to match for this star chart, and importance in star map recognition frequency of failure n adds 1;
(3) if exist on many nautical star couplings, then utilize second to go on foot the angular distance d that obtains
13Mate with the importance in star map recognition authorization information of many nautical stars, obtain further matching result.If there is not the nautical star coupling, then it fails to match for this star chart, and importance in star map recognition frequency of failure n adds 1; An if only nautical star coupling, then this width of cloth importance in star map recognition success; If still there are many nautical star couplings; Then utilize the corresponding respectively magnitude of first asterism in the second step optimal path, second asterism and the 3rd asterism and the authorization information of importance in star map recognition to mate; If no nautical star mates or still have many nautical stars to mate; It fails to match then to think this star chart, and importance in star map recognition frequency of failure n adds 1, otherwise this width of cloth importance in star map recognition success.
The 5th step: as frequency of failure n during, return step the second step less than recognition failures number of times maximal value N, otherwise, this width of cloth star chart coupling failure, this width of cloth importance in star map recognition finishes.
Embodiment:
Selecting nautical star, during the structure navigational star table, the maximum magnitude of choosing nautical star is 6.5, and angular distance is less than the double star of a certain value (getting 3 pixels) between the rejecting fixed star, and the basic navigation star catalogue that obtains at last has 8996 nautical stars.When definite radius of circle r, get radius of circle r=4 °, the approximate Normal Distribution of the nautical star number when the asterism number is got different value in the circle, the asterism number has 404 less than 6 nautical star in the circle, and the nautical star greater than 25 has 628, and is as shown in Figure 2.For the navigation star number was less than 6 o'clock in the circle, increasing radius of circle r is 5 °, and the nautical star of different asterism numbers distributes as shown in Figure 3 in the circle; For the navigation star number was greater than 25 o'clock in the circle, reducing radius of circle r is 2.5 °, and the nautical star of different asterism numbers distributes as shown in Figure 4 in the circle.
Based on the method for recognising star map of Hybrid Particle Swarm, at first be configured to the navigation star database of importance in star map recognition according to step 1, the navigation star database of structure is made up of optimal path lengths table, angular distance identification star catalogue and identification checking star catalogue.The optimal path lengths table has been stored the length value of optimal path; Angular distance identification star catalogue comprises in the optimal path angular distance and this three the pairing right ascension declination of the nautical star information between angular distance between first nautical star and second nautical star, second nautical star and the 3rd nautical star; Identification checking star catalogue comprises in the optimal path magnitude of first three nautical star in angular distance and the optimal path between first nautical star and the 3rd nautical star, and the memory contents of three tables is respectively shown in table 1, table 2 and table 3 in the navigation star database.Realize the coupling identification of nautical star then according to step 2, the user sets up recognition failures number of times maximal value (generally getting 4 with interior positive integer) on their own, and the identification process of method is as shown in Figure 6.
Table 1 optimal path lengths table
| The optimal path value (°) |
| ... |
| 11.030192 |
| 11.453998 |
| 11.607923 |
| 12.864802 |
| ... |
Table 2 star chart angular distance Identification Lists
| Angular distance 1 (°) | Angular distance 2 (°) | Right ascension 1 (°) | Declination 1 (°) | Right ascension 2 (°) | Declination 2 (°) | Right ascension 3 (°) | Declination 3 (°) |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2.682591 | 3.831108 | 48.137904 | -57.321636 | 45.903517 | -59.737742 | 53.214933 | -61.016722 |
| 2.761853 | 5.601960 | 47.819796 | -16.025144 | 47.771804 | -13.263681 | 49.671696 | -18.559589 |
| 1.184397 | 1.059622 | 213.017000 | 69.432614 | 210.460967 | 68.678717 | 207.745563 | 68.314947 |
| 2.760843 | 1.592563 | 144.260825 | 16.437953 | 141.385337 | 16.585803 | 140.314033 | 15.371067 |
| ... | ... | ... | ... | ... | ?... | ... | ... |
Table 3 identification proof list
| Angular distance (°) | |
|
|
| ... | ... | ... | ... |
| 4.516492 | 5.7 | 5.2 | 6.3 |
| 3.090185 | 6.3 | 6.5 | 5.8 |
| 2.203262 | 5.4 | 6.4 | 6.4 |
| 3.942676 | 5.9 | 6.3 | 6.5 |
| ... | ... | ... | ... |
Use the method for recognising star map that the present invention proposes, can significantly improve the recognition success rate of method under big visual field, the high sensitivity Star Sensor condition and the robustness of noise based on Hybrid Particle Swarm.Adopt the method for recognising star map based on Hybrid Particle Swarm of the present invention, traditional triangle shape algorithm and improved neural network star Pattern Recognition Algorithm; Respectively 1000 width of cloth star charts that generate are at random discerned; The recognition failures number of times maximal value of wherein choosing based on the method for recognising star map of Hybrid Particle Swarm is 1, promptly only carries out one time optimum path search.When discerning, three kinds of methods are all based on following condition: the visual field size of star sensor is 20 ° * 20 °, and star sensor face battle array size is 1024 * 1024, and pixel dimension is 15 μ m * 15 μ m.When carrying out emulation, it is 0 that the asterism site error is got average, and variance is 0 to 2.5 pixel, and to all emulation 1000 times under diverse location error condition of three kinds of method for recognising star map, the discrimination of method is as shown in Figure 7.
As can beappreciated from fig. 7, under the situation of variance less than 2 pixels of asterism site error, method for recognising star map recognition success rate of the present invention is the highest, and improved neural network algorithm discrimination takes second place, and the discrimination of traditional triangle shape algorithm is minimum.When asterism site error variance during, begin less than improved neural network recognition method based on discrimination of the present invention, but be superior to traditional triangle shape algorithm greater than 2 pixels.
For the recognition speed of the method for the invention, on Intel Core i3 processor 2.39GHz PC, adopt VS2010 to test, method of the present invention is from the coupling identification that is configured to star chart of feature mode, and be 0.15s required averaging time.Because the characteristic in the navigation star database is the optimal path value that is obtained by the Hybrid Particle Swarm optimum path search, adopts the ascending order mode to store, thereby can utilize binary chop to search for and mate.Its retrieval rate is faster than traditional triangle shape algorithm; Suitable with improved neural network star Pattern Recognition Algorithm, but the speed of three kinds of recognition methods structural attitudes is traditional triangle shape algorithm, improved neural network method for recognising star map and based on the method for recognising star map of population from small to large successively.
Claims (4)
1. the method for recognising star map based on Hybrid Particle Swarm is characterized in that, comprises following step:
Step 1: set up navigation star database
Specifically comprise following step:
The first step: choose nautical star, make up the basic navigation star catalogue
From fundamental catalog, choose nautical star, angular distance between fixed star is rejected less than the double star of 3～5 pixels, obtain being used for the basic navigation star catalogue of importance in star map recognition, the basic navigation star catalogue comprises numbering, magnitude, right ascension and the declination information of every nautical star;
Second step: the characteristic set of constructing every nautical star
To every nautical star in the basic navigation star catalogue, be the center of circle with every nautical star respectively, carry out the picture circle with radius r, all nautical stars in the circle are formed the characteristic set of center of circle nautical star;
The 3rd step: choose a nautical star in the basic navigation star catalogue; Obtain in the characteristic set angular distance between fixed star in twos, the nautical star with circle centre position in the characteristic set is a starting point then, utilizes Hybrid Particle Swarm to carry out the fast path optimizing; Obtain the optimal path of characteristic set; Obtain the length of optimal path, and to select in the optimal path from the nearest nautical star in the center of circle be the working direction of optimal path, obtain first three nautical star in the optimal path;
The 4th step: the information of obtaining first three nautical star in the optimal path; Promptly obtain first nautical star, second nautical star and the 3rd the pairing right ascension of nautical star, declination information and magnitude respectively, the angular distance information between first nautical star and second nautical star, between second nautical star and the 3rd nautical star, between first nautical star and the 3rd nautical star;
Angular distance information between first nautical star and second nautical star, between second nautical star and the 3rd nautical star; First nautical star, second nautical star and the 3rd right ascension, the declination information that nautical star is corresponding respectively are as the basic identifying information of nautical star;
The angular distance between first nautical star and the 3rd nautical star and the magnitude of first nautical star, second nautical star and the 3rd nautical star are as the authorization information of importance in star map recognition;
The 5th step: repeated for the 3rd step and the 4th step; Obtain the characteristic information of each nautical star in the navigational star table; Characteristic information comprises the optimal path length of nautical star, the basic identifying information of nautical star and the authorization information of importance in star map recognition; The characteristic information of every nautical star is arranged according to the ascending order mode of optimal path length, and constitutes navigation star database;
Step 2: carry out importance in star map recognition based on Hybrid Particle Swarm
For a width of cloth star chart, extract through star chart pre-service, celestial body, obtain the position coordinates and the half-tone information of each asterism in the star chart, carry out the identification of star chart coupling then, following based on the method for recognising star map concrete steps of Hybrid Particle Swarm:
The first step: confirm that the candidate discerns primary; The identification primary of choosing should have brightness big and from the field of view edge angular distance all greater than the characteristics of radius of circle r; Choosing method is following: calculate in the star chart all from the field of view edge angular distance all greater than the average gray value of the asterism of radius of circle r; Choose gray-scale value then and discern primary as the candidate, obtain the candidate and discern the primary set greater than the asterism of average gray value;
The maximal value that the user is provided with the recognition failures number of times is N, and establishing identification frequency is n, and the initial value of n is made as 0;
Second step: carry out the optimizing of asterism fast path, in the candidate discerns primary set, at first choose the near asterism of from the center, visual field n+1, carry out the picture circle with radius r then, with all the asterism constitutive characteristic data acquisitions in the circle as the center of circle;
Interasteric in twos angular distance in the calculated characteristics data acquisition; Asterism with circle centre position in the characteristic set is a starting point then; Utilize Hybrid Particle Swarm to carry out the fast path optimizing, obtain the optimal path of asterism characteristic set, obtain the length of optimal path; Selecting to leave the nearest asterism in the center of circle in the optimal path is the working direction of optimal path, obtains first three asterism in the optimal path;
Obtain the information of first three asterism in the optimal path, promptly obtain first asterism, second asterism and the 3rd the pairing magnitude of asterism respectively, first asterism and second interasteric angular distance d
12, second asterism and the 3rd interasteric angular distance d
23, first asterism and the 3rd interasteric angular distance d
13
The 3rd step: carry out the identification of star chart coupling; According to the radius r of asterism, confirm the part of this asterism respective radii r in navigation star database, go on foot the optimal path length that obtains according to second; Adopt binary chop; Search in the navigation star database and the close nautical star of this asterism optimal path length, and discern star to them as the candidate, utilize the d that obtains in second step then
12And d
23The basic identifying information of discerning star with the candidate matees, and obtains matching result; Matching result is for no nautical star matees, only perhaps there are many nautical stars couplings in a nautical star coupling;
The 4th step: the checking of nautical star recognition result, the matching result that the 3rd step obtained is verified concrete verification method is following:
(1) if only a nautical star matees, then utilize second to go on foot the angular distance d that obtains
13Mate with the importance in star map recognition authorization information of nautical star, if on the coupling, then this width of cloth importance in star map recognition success, if on not mating, then star chart coupling failure this time, importance in star map recognition frequency of failure n adds 1;
(2) if do not have on the nautical star coupling, then it fails to match for this star chart, and importance in star map recognition frequency of failure n adds 1;
(3) if exist on many nautical star couplings, then utilize second to go on foot the angular distance d that obtains
13Mate with the importance in star map recognition authorization information of many nautical stars, obtain further matching result; If there is not the nautical star coupling, then star chart coupling failure this time, importance in star map recognition frequency of failure n adds 1; If only a nautical star matees, then importance in star map recognition success; If still there are many nautical star couplings; Then utilize the corresponding respectively magnitude of first asterism in the second step optimal path, second asterism and the 3rd asterism and the authorization information of importance in star map recognition to mate; If no nautical star matees or still have many nautical stars to mate; Think that then this star chart matees failure, importance in star map recognition frequency of failure n adds 1, otherwise this width of cloth importance in star map recognition success;
The 5th step: as frequency of failure n during, return step the second step less than recognition failures number of times maximal value N, otherwise, this width of cloth star chart coupling failure, this width of cloth importance in star map recognition finishes.
2. a kind of method for recognising star map based on Hybrid Particle Swarm according to claim 1 is characterized in that, step 1 is in second step, in the step 2 first step, and the choosing method of said radius of circle r is specially:
(1) is the center with a nautical star, gets radius of circle r=4 °, the asterism number in the statistics circle; When the asterism number less than 6 the time, get into step (2), when the asterism number greater than 25 the time; Get into step (3), when the asterism number more than or equal to 6 and smaller or equal to 25 the time, confirm radius of circle r=4 °;
(2) the asterism number in circle is less than 6 the time, and increase radius of circle r is 5 °;
(3) the asterism number is greater than 25 the time in circle, and reducing radius of circle r is 2.5 °.
3. a kind of method for recognising star map based on Hybrid Particle Swarm according to claim 1 is characterized in that, step 1 the 3rd in the step, step 2 are in second step, and the fast path optimizing step of described Hybrid Particle Swarm is following:
(1) supposes total m nautical star in the characteristic set, at first the nautical star in the characteristic set encoded that the order that particle is traveled through all nautical stars is arranged with natural number, as separating of particle; The parameter of initialization Hybrid Particle Swarm then, the setting particle number is n, the iterations maximal value is Nmax; Produce n initial solution then at random, and particle is traveled through the fitness function of the path sum of all nautical stars as particle; Calculate its fitness value according to the current location of particle then, set the individual extreme value of each particle, and the global extremum of all particles;
(2) current the separating with individual extreme value of i particle intersected; Concrete cross method is: separate for the current of particle; Select an intersection region at random; With the identical element in individual extreme value intersection region, then the element in the individual extreme value intersection region is inserted the current of particle and separate during deletion is separated then, obtain the new explanation of i particle;
(3) new explanation that step (2) is obtained intersects with global extremum; Concrete cross method is: separate for the current of particle; Select an intersection region at random; The element identical with the global extremum intersection region during deletion is separated inserts the current of particle with the element in the global extremum intersection region then and separates, and obtains the new explanation of i particle;
(4) new explanation that step (3) is obtained makes a variation; For making the path sum reach minimum, adopt the roulette back-and-forth method, choose in the path two bigger nautical stars of angular distance between adjacent nautical star with bigger probability; Between them, insert other nautical star, the new explanation after obtaining making a variation then;
Concrete grammar is: the angular distance in the new explanation that obtaining step (3) obtains between adjacent nautical star is designated as l (k) respectively, k=1; 2 ..., m; Wherein, the angular distance during l (1) expression is separated between first and second nautical star, by that analogy; During l (m-1) expression is separated m-1 with m nautical star between angular distance, the angular distance during l (m) representes to separate between m and first nautical star, then every selecteed probability of nautical star is:
According to the probability that following formula is tried to achieve, adopt the roulette back-and-forth method to choose nautical star i
1When adopting the roulette back-and-forth method; The fritter that every nautical star separating is similar in the wheel disc is fan-shaped; Fan-shaped area size is directly proportional with the selecteed probability of this nautical star, and sectorial area is big more, and then to be chosen as the next probability that travels through nautical star also big more for this nautical star;
Chosen nautical star i
1After, choose from asterism i with bigger probability
1Nearer asterism is as the next one traversal point of particle; If d is (i
1, nautical star i during j) expression is separated
1And the distance between nautical star j then can calculate from nautical star i
1The distance of nautical star is d farthest
Max, wherein
K=1 ..., m and k ≠ i
1, for preventing to travel through nautical star i
1After next nautical star be i
1Itself makes d (i
1, i
1)=d
Max, then every nautical star is chosen as the probability of next traversal nautical star and is:
According to the probability that following formula is tried to achieve, adopt the roulette back-and-forth method to choose asterism j
1, the asterism that promptly probability is big more is prone to be selected, then nautical star j
1Be arranged into nautical star i
1Afterwards, all the other are constant, the new explanation after obtaining making a variation;
(5) carry out the renewal of individual extreme value; Concrete grammar is: the fitness value that at first calculates i particle variation back new explanation; And compare with the fitness value of the individual extreme value of this particle, obtain the fitness value variable quantity of i particle, adopt mechanism of Simulated Annealing to determine whether to accept this new explanation then; If accept, then upgrade the individual extreme value of this particle;
Judge whether the individual extreme value of all particles is all upgraded,, get into step (6), otherwise return step (2) if all upgrade;
(6) carry out the renewal of global extremum,, confirm global extremum and global extremum path according to the individual extreme value of all particles; Judge that whether iterations is greater than iterations maximal value Nmax; If greater than, then optimum path search finishes, and the global extremum path is optimal path; Otherwise, the step of returning (2).
4. a kind of method for recognising star map according to claim 1 based on Hybrid Particle Swarm; It is characterized in that; Step 1 is in the 5th step; Described navigation star database constructive method is: the nautical star identifying information for radius r=5 ° is arranged according to optimal path length ascending order, constitutes the first of navigation star database; Nautical star identifying information for radius r=4 ° is arranged according to optimal path length ascending order, constitutes the second portion of navigation star database; Nautical star identifying information for radius r=2.5 ° is arranged according to optimal path length ascending order, constitutes the third part of navigation star database, and this three part constitutes a complete navigation star database at last.
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