CN102706352B - Vector map matching navigation method for linear target in aviation - Google Patents

Abstract

The invention discloses a vector map matching navigation method for a linear target in aviation, which comprises the following steps: preparing a base vector diagram and a template vector diagram for map matching, unifying the base vector diagram and the template vector diagram under a same vector precision level, and extracting included angle characteristics of the template vector diagram and the base vector diagram; sequentially utilizing a distance constraint condition, a sequence constraint condition and an error constraint condition to gradually limit size of a feasible solution space; clustering pose parameters of the feasible solution space, taking a class containing most pose parameters as a correct matching relationship; and further computing the pose parameters to be errors for correcting an inertial navigation system by matching positioning results. The method provided by the invention can overcome shortages of space efficiency and time efficiency in the existing grid image matching navigation method for aviation and avoids the problems of large data volume, low efficiency and the like in the grid image matching; in addition, the method provided by the invention overcomes influence of small-scale change on a vector map and can achieve vector map matching under rotation, translation and small-scale change.

Description

The map vector matching navigation method of linear target for a kind of aviation

Technical field

The present invention relates to a kind of map vector coupling navigation (Vector Map Matching Navigation) method, particularly the map vector matching navigation method of linear target for a kind of aviation.

Background technology

The outstanding advantages such as scene matching aided navigation technology is high with its measuring accuracy, independence is strong, antijamming capability is outstanding, system energy consumption is low, navigation accuracy is high, have been successfully applied in cruise missile terminal guidance system.Because scene matching aided navigation often adopts grating image as matched data source, its airborne reference database General Requirements memory space is larger, and image matching algorithm is conventionally consuming time longer, and the real-time of navigational system is had a certain impact.

Adopt grating image coupling location different from scene matching aided navigation navigation, the navigation of map vector coupling is the location technology based on vector image coupling.Its basic process is: utilize airborne imageing sensor to gather ground image, and mate with pre-stored map vector data storehouse in navigational computer after processing by images such as denoising, vector quantizations, thereby obtain the navigational parameter of aircraft.With respect to grating image coupling, the navigate mode based on map vector coupling has following advantage:

(1) non-loss transformation.Because image to be matched and reference picture all consist of vector, therefore can be rotated, the harmless mathematic(al) manipulation such as convergent-divergent, perspective, and grating image convergent-divergent can produce the discontinuous ladder phenomenon of pixel.

(2) space efficiency is high.Vector image adopts few point coordinate to represent complicated X-Y scheme, and than grating image, its expression way data volume is little, and the memory capacity that therefore airborne chart datum database needs is also just less.

(3) time efficiency is high.Grating image coupling often needs to scan whole image-region, and calculated amount is large, and therefore the point that vector image often only needs processing vector key element to comprise can realize the quick computing of images match process.

Meanwhile, the navigation of map vector coupling exists vector image and expresses not shortcoming intuitively, can not directly utilize coordinate figure to mate, and matching algorithm is comparatively complicated.Conventionally can select other characteristic quantities to mate, for linear target, conventional geometric properties comprises length, concavity, angle, position angle etc., and some scholar also proposes to adopt the characteristic quantities such as curvature, described function to carry out line target coupling.For example, Wang Wencheng etc. are by carrying out segmentation to outline line, the curvature that has proposed to using between characteristic segments is as matching characteristic, by to Tibet, Qinghai, Sichuan, the splicing of four province's maps such as Yunnan, verified that the feasibility of the method (is shown in Wang Wencheng, Li Xiaowei, intelligence is good, Deng, the shape matching > > of < < based on Hausdorff distance, the journal > > of < < Xian Institute of Posts and Telecoms, 2007, 12 (3): 91-94).Also have, Fu Zhongliang etc. adopt the function of describing the shape of positive tangent space, the normalization distance of take is independent variable, the corner of each periphery broken line is dependent variable, this description form also can be used for the coupling of wire vector element, embodied the continuity of vector line segment, the accuracy rate problem that can solve preferably coupling (is shown in and pays Zhong Liang, Shao Shiwei, the quick shape matching method > of the complicated planar vector element of < < >, < < mapping circular > >, 2011, (3): 26-28).

At present, what aviation adopted substantially with map Matching Navigation System is scene matching aided navigation mode, and vector matching technology is mainly used in GIS field, the maintenance and the aspect such as renewal, location-based navigation Service that comprise the integrated or fusion of spatial data, multiscale space data, be not well used in the navigation field of aircraft.In addition, in the vector graphics coupling in GIS field, mostly in situation the geometric elements in two coupling sources be complete and shape identical, and use in map vector Matching Navigation System in aviation, imageing sensor gathers the geometric element of image after a vector quantization part for ground geometric element often, and the vector matching under two kinds of situations exists larger difference.The navigation that map vector coupling navigate mode is aircraft provides a kind of new thinking, has good development prospect, and along with the development of vector matching technology, its advantage also will be more and more obvious, and this case produces based on aforementioned thinking.

Summary of the invention

Object of the present invention, be to provide the map vector matching navigation method of a kind of aviation by linear target, it can overcome the existing aviation deficiency of grating image matching navigation method in space efficiency and time efficiency, avoid grating image coupling problem, the problem includes: the problem such as data volume is large, efficiency is low, and overcome the impact of small scale variation on map vector, can complete in the map vector coupling having under rotation, translation and small scale conversion.

In order to reach above-mentioned purpose, solution of the present invention is:

A map vector matching navigation method for linear target for aviation, comprises the steps:

(1) judge whether to start navigation by map-matching system, if started, prepare base vector figure and the template vector figure of map match;

(2) template vector figure and base vector figure unification are arrived under same vector accuracy rank, and extract the angle feature of template vector figure and base vector figure;

(3) the template vector figure obtaining according to step (2) and the angle feature of base vector figure, utilize successively distance restraint condition, order constrained condition and three constraint conditions of error constraints condition progressively to limit the size of solution space, finally obtain the 3rd layer of solution space;

(4) the pose parametric solution of the 3rd layer of solution space is carried out to cluster, to comprise class that pose parametric solution is maximum as correct matching relationship, and then utilize pose algorithm for estimating to calculate pose parameter, as coupling positioning result, proofread and correct the error of inertial navigation system.

The particular content of above-mentioned steps (1) is:

Step 101, according to inertial navigation system outgoing position, judge whether to enter navigation by map-matching region, if not, do not start navigation by map-matching system; If so, start navigation by map-matching system, allow aircraft keep horizontal attitude, and utilize airborne imageing sensor to gather ground image, enter step 102;

Step 102, be written near the map vector data storehouse based on wire key element inertial navigation system outgoing position, as the base vector figure of map match;

Step 103, for the linear target in Real-time Collection ground image, by vector technology, obtain the real-time polar plot based on wire key element, the coordinate system of this real-time polar plot adopt initial point in the image upper left corner, the pixel of the take image coordinate system that is unit;

Step 104, by the coordinate transform of real-time polar plot under geographic coordinate yardstick, as the template vector figure of map match.

The particular content of above-mentioned steps (2) is:

Step 201, employing Douglas-Peucker method are compressed template vector figure and base vector figure, use identical distance threshold value D in compression process ₁thereby, template vector figure and base vector figure is unified under same vector accuracy rank;

Step 202, for step 201, compression obtains respectively real-time polar plot and base vector figure, all broken lines in sweep vector figure, and calculate corresponding broken line angle sequence.

The particular content of above-mentioned steps (3) is:

Step 301, for the angle sequence of each broken line in template vector figure, calculate the unidirectional Hausdorff distance between all broken line angle sequences on itself and base vector figure, retain unidirectional Hausdorff distance on base vector figure and be less than predetermined threshold value D ₂all broken lines, as ground floor solution space;

In step 302, the ground floor solution space that obtains in step 301, according to broken line angle, sequentially reject the broken line not satisfying condition, to the broken line satisfying condition, find out point correspondence possible between template vector figure and base vector figure, and then set up second layer solution space;

Step 303, for all possible point correspondence in second layer solution space, utilize the position and orientation estimation method of anti-change of scale to solve rotation angle, side-play amount and scale factor, and utilize the rotation angle and the side-play amount that obtain to carry out after pose conversion template vector figure, with base vector figure compare and solve square mean error amount a little, square mean error amount is greater than to predetermined threshold value D ₅point correspondence from second layer solution space, reject away, thereby set up the 3rd layer of solution space.

The particular content of above-mentioned steps 301 is: on note template vector figure, the angle sequence of certain broken line is A=[α ₁, α ₂..., α _n], on base vector figure, certain broken line angle sequence is k represents the numbering of this broken line on base vector figure, unidirectional Hausdorff distance h (A, B ^k) computing formula as follows:

$h(A,B^k)=\underset{{\mathrm{\&alpha;}}_i\&Element;A}{\max }\underset{{{\mathrm{\&beta;}}_j}^k\&Element;B^k}{\min }|{\mathrm{\&alpha;}}_i-{{\mathrm{\&beta;}}_j}^k|$

All angles in above formula reflection sequence A are to sequence B ^kminimal difference, as A and B ^kunidirectional Hausdorff distance h (A, B ^k) be greater than and preset predetermined threshold value D ₂time, judgement B ^kthe corresponding broken line broken line corresponding with A do not mate.

The particular content of above-mentioned steps 302 is: in the ground floor solution space that certain broken line is corresponding in template vector figure, each end points on a base vector figure broken line of usining is respectively set up the to be selected corresponding broken line identical with template vector figure broken line number of endpoint as starting point; Then, this broken line of template vector figure is compared with each angle of base vector figure corresponding broken line to be selected, note difference is at threshold value D ₃the interior angle number of take is N _s, total angle number is N, if ratio N _s/ N is greater than predetermined threshold value D ₄, think that the corresponding broken line of this base vector figure meets order constrained condition, and record this group point correspondence in solution space for the second time.

The particular content of above-mentioned steps 303 is: suppose in second layer solution space, a certain group of point correspondence is: the coordinate of template vector figure point set P is p _i=[p _ix, p _iy] ^t(i=1,2 ..., n), the coordinate of corresponding base vector figure point set Q is q _i=[q _ix, q _iy] ^t, it is q=sRp+t that the coordinate transform between note template vector figure and base vector figure is closed, and wherein, t is side-play amount, and s is scale factor, and R is rotation matrix, the detailed process of the position and orientation estimation method of anti-change of scale is as follows:

(1) according to the following formula, the center point coordinate of calculating point set P and point set Q is:

$\stackrel{\&OverBar;}{p}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{p}_i,\stackrel{\&OverBar;}{q}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{q}_i$

(2) according to the following formula, estimate scale factor for:

$\hat{s}=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-\stackrel{\&OverBar;}{q}\left|\right|}^2/\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|p_i-\stackrel{\&OverBar;}{p}\left|\right|}^2}$

(3) according to the following formula, estimate the anglec of rotation for:

$a=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{iy}}-{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{ix}}),b=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{ix}}+{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{iy}})$

(4) according to the following formula, estimate side-play amount for:

$\hat{t}=\stackrel{\&OverBar;}{q}-\hat{s}\hat{R}\stackrel{\&OverBar;}{p}$

Rotation matrix wherein estimated value can obtain according to following formula:

$\hat{R}=\left[\begin{array}{cc}\cos \left(\widehat{\mathrm{\&theta;}}\right)& -\sin \left(\widehat{\mathrm{\&theta;}}\right)\\ \sin \left(\widehat{\mathrm{\&theta;}}\right)& \cos \left(\widehat{\mathrm{\&theta;}}\right)\end{array}\right]$

(5) calculate square error e:

$e=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-(\hat{s}\hat{R}{p}_i+\hat{t})\left|\right|}^2}.$

The particular content of above-mentioned steps (4) is:

Step 401, for each pose parametric solution, set up a class, and calculate and other all pose parametric solutions between difference, if angle difference, position difference and yardstick difference are all less than respectively predetermined threshold value D ₆, D ₇, D ₈, this pose parametric solution is deposited in class;

Step 402, find out the maximum class of pose parametric solution number, and find out the point correspondence of such all pose parametric solution representatives, according to all corresponding point, utilize the pose algorithm for estimating of anti-change of scale to calculate pose parameter, and as the output of final coupling positioning result, in order to proofread and correct the error of inertial navigation system.

Adopt after such scheme, the present invention adopts map vector as matched data source, the angle of broken line is as matching characteristic amount, and the thought based on processing by different level, according to distance restraint condition, order constrained condition and error constraints condition, reduce successively the size of solution space, thereby fast and reliable ground obtains navigation by map-matching result.

Accompanying drawing explanation

Fig. 1 is that map vector Matching Navigation System of the present invention forms schematic diagram;

Fig. 2 is the algorithm flow chart of map vector matching navigation method of the present invention;

Fig. 3 is vector compression Douglas-Peucker ratio juris schematic diagram of the present invention;

Fig. 4 is broken line angle sequence schematic diagram of the present invention;

Fig. 5 is verification of correctness test simulation result figure of the present invention;

Fig. 6 is the simulation result figure of certain reliability testing test of the present invention;

Fig. 7 is the anglec of rotation Error Graph of reliability testing test of the present invention;

Fig. 8 is the site error figure of reliability testing test of the present invention.

Embodiment

Below with reference to accompanying drawing, technical scheme of the present invention is elaborated.

Figure 1 shows that map vector Matching Navigation System forms schematic diagram, the navigation of map vector coupling utilizes airborne imageing sensor to gather ground or target area image, the real-time map vector that image processing is obtained mates with pre-stored map vector data storehouse in navigational computer, thereby obtains the navigational parameter of aircraft.Map vector Matching Navigation System mainly comprises image processing, extracted region, map vector matching algorithm and four parts of navigation calculation.

(1) image is processed: according to Real-time Collection grating image, utilize image denoising, yardstick unification, vector quantization etc. to process the rear corresponding real-time polar plot that obtains, as the input source of matching algorithm.

(2) extracted region: the reference position providing according to other navigate modes such as INS and site error scope, extract the vector data in navigation area in map vector data storehouse, form base vector figure, as the reference source of matching algorithm.

(3) map vector matching algorithm: based on real-time polar plot and base vector figure, determine the position of real-time polar plot in base vector figure, anglec of rotation etc. according to the process of search, comparison, optimizing.

(4) navigation calculation: according to coordinate transform relation, determine the navigational parameter such as geographic position, course angle of carrier, and then INS navigation error is proofreaied and correct.

Shown in Fig. 2, be the process flow diagram of the map vector matching navigation method of linear target for a kind of aviation provided by the present invention, specifically comprise the steps:

Step 1, algorithm initialization: judge whether to start navigation by map-matching system, if started, prepare base vector figure and the template vector figure of map match.

Step 101, according to inertial navigation system outgoing position, judge whether to enter navigation by map-matching region, if not, do not start navigation by map-matching system; If so, start navigation by map-matching system, allow aircraft keep horizontal attitude, and utilize airborne imageing sensor to gather ground image, enter step 102;

Step 102, be written near the map vector data storehouse based on wire key element inertial navigation system outgoing position, as the base vector figure of map match.

The linear targets such as step 103, the river in Real-time Collection ground image, road, mountain range, obtain the real-time polar plot based on wire key element by vector technology.The coordinate system of this real-time polar plot adopt initial point in the image upper left corner, the pixel of the take image coordinate system that is unit;

Step 104, by the coordinate transform of real-time polar plot to geographic coordinate system, as the template vector figure of map match.Suppose that aircraft is h at reference altitude ₀and during horizontal flight, the ingestible ground of imageing sensor scope is x ₀* y ₀, the image size of collection is a * b(pixel), the current geography of navigational system indication is highly h, the coordinate range of x in real-time polar plot is transformed to from [0, b] [0, (h/h ₀) * x ₀], the coordinate range of y from [0, a], transform to [0, (h/h ₀) * y ₀].

Step 2, feature extraction: template vector figure and base vector figure unification are arrived under same vector accuracy rank, and extract the angle feature of template vector figure and base vector figure.

Step 201, the compression of vector line segment: adopt Douglas-Peucker method to compress template vector figure and base vector figure, in compression process, use identical distance threshold value D ₁thereby, template vector figure and base vector figure is unified under same vector accuracy rank.

Suppose that certain vector line segment is as shown in the ABCDEF curve in Fig. 3, the basic process of vector compression Douglas-Peucker method is as follows:

(1) junction curve AF end points obtains the string of straight-line segment AF(curve), calculate on this curve and leave bowstring AF apart from maximum some C, calculate it to the distance d of AF.

(2) compare the distance threshold value T of d and appointment.If d<T, is considered as this straight-line segment the approximate of curve; If d>T, is divided into two sections of AC and CF with C by curve, respectively two line segments are processed according to above method.

(3) after all curve processing, connect successively the broken line that each cut-point forms, be the compression result of vector line segment, if the broken line ABCDEF in Fig. 1 is the vector quantization compression result of curve A F.

Step 202, angle feature extraction: for step 201, compression obtains respectively real-time polar plot and base vector figure, all broken lines in sweep vector figure, and calculate corresponding broken line angle sequence.

Suppose that certain vector line segment is as the P in Fig. 4 ₁p ₂p ₃p ₄p ₅shown in broken line, the computation process of angle sequence is as follows: choose successively three some P on broken line ₁p ₂p ₃, angle is defined as vector P ₁p ₂with vector P ₂p ₃between angle, i.e. P ₁p ₂p ₃p ₄p ₅the angle sequence that broken line is corresponding is α ₁～α ₃.

Step 3, constraint at many levels: the template vector figure obtaining according to step 2 and the angle feature of base vector figure, utilize successively distance restraint condition, order constrained condition and three constraint conditions of error constraints condition progressively to limit the size of solution space.

Step 301, distance restraint condition: for the angle sequence of each broken line in template vector figure, calculate the unidirectional Hausdorff distance between all broken line angle sequences on itself and base vector figure, retain unidirectional Hausdorff distance on base vector figure and be less than predetermined threshold value D ₂all broken lines, as ground floor solution space.

On note template vector figure, the angle sequence of certain broken line is A=[α ₁, α ₂..., α _n], on base vector figure, certain broken line angle sequence is k represents the numbering of this broken line on base vector figure, unidirectional Hausdorff distance h (A, B ^k) computing formula as follows:

$h(A,B^k)=\underset{{\mathrm{\&alpha;}}_i\&Element;A}{\max }\underset{{{\mathrm{\&beta;}}_j}^k\&Element;B^k}{\min }|{\mathrm{\&alpha;}}_i-{{\mathrm{\&beta;}}_j}^k|$

All angles in above formula reflection sequence A are to sequence B ^kminimal difference, as A and B ^kunidirectional Hausdorff distance h (A, B ^k) be greater than and preset predetermined threshold value D ₂time, can judge B ^kthe corresponding broken line broken line corresponding with A do not mate.

Step 302, order constrained condition: in the ground floor solution space obtaining in step 301, according to broken line angle, sequentially reject the broken line not satisfying condition, to the broken line satisfying condition, find out point correspondence possible between template vector figure and base vector figure, and then set up second layer solution space.

Utilize the detailed process of broken line angle order constrained condition as follows: in the ground floor solution space that certain broken line is corresponding in template vector figure, each end points on a base vector figure broken line of usining is respectively set up the to be selected corresponding broken line identical with template vector figure broken line number of endpoint as starting point; Then, this broken line of template vector figure is compared with each angle of base vector figure corresponding broken line to be selected, note difference is at threshold value D ₃the interior angle number of take is N _s, total angle number is N, if ratio N _s/ N is greater than predetermined threshold value D ₄, think that the corresponding broken line of this base vector figure meets order constrained condition, and record this group point correspondence in solution space for the second time.

Step 303, error constraints condition: for all possible point correspondence in second layer solution space, utilize the position and orientation estimation method of anti-change of scale to solve rotation angle, side-play amount and scale factor, and utilize the rotation angle and the side-play amount that obtain to carry out after pose conversion template vector figure, with base vector figure compare and solve square mean error amount a little, square mean error amount is greater than to predetermined threshold value D ₅point correspondence from second layer solution space, reject away, and be designated as the 3rd layer of solution space.

Suppose in second layer solution space, a certain group of point correspondence is: the coordinate of template vector figure point set P is p _i=[p _ix, p _iy] ^t(i=1,2 ..., n), the coordinate of corresponding base vector figure point set Q is q _i=[q _ix, q _iy] ^t, it is q=sRp+t that the coordinate transform between note template vector figure and base vector figure is closed, and wherein, t is side-play amount, and s is scale factor, and R is rotation matrix, the detailed process of the position and orientation estimation method of anti-change of scale is as follows:

(1) according to the following formula, the center point coordinate of calculating point set P and point set Q is:

$\stackrel{\&OverBar;}{p}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{p}_i,\stackrel{\&OverBar;}{q}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{q}_i$

(2) according to the following formula, estimate scale factor for:

$\hat{s}=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-\stackrel{\&OverBar;}{q}\left|\right|}^2/\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|p_i-\stackrel{\&OverBar;}{p}\left|\right|}^2}$

(3) according to the following formula, estimate the anglec of rotation for:

$a=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{iy}}-{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{ix}}),b=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{ix}}+{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{iy}})$

(4) according to the following formula, estimate side-play amount for:

$\hat{t}=\stackrel{\&OverBar;}{q}-\hat{s}\hat{R}\stackrel{\&OverBar;}{p}$

Rotation matrix wherein estimated value can obtain according to following formula:

$\hat{R}=\left[\begin{array}{cc}\cos \left(\widehat{\mathrm{\&theta;}}\right)& -\sin \left(\widehat{\mathrm{\&theta;}}\right)\\ \sin \left(\widehat{\mathrm{\&theta;}}\right)& \cos \left(\widehat{\mathrm{\&theta;}}\right)\end{array}\right]$

(5) calculate square error e:

$e=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-(\hat{s}\hat{R}{p}_i+\hat{t})\left|\right|}^2}$

Step 4, Global treatment and decision-making: the pose parametric solution to the 3rd layer of solution space carries out cluster, to comprise class that pose parametric solution is maximum as correct matching relationship, and then utilize the pose algorithm for estimating in step 303 to calculate pose parameter, as coupling positioning result, proofread and correct the error of inertial navigation system.

Step 401, for each pose parametric solution, set up a class, and calculate and other all pose parametric solutions between difference, if angle difference, position difference and yardstick difference are all less than respectively predetermined threshold value D ₆, D ₇, D ₈, this pose parametric solution is deposited in class.

Suppose that the pose parametric solution obtaining according to i group point correspondence is v _i=(θ _i, t _i, s _i) (i=1,2, m), note is j group pose parametric solution when pre-treatment, and sets up j group pose parameter class, other all pose parametric solutions and j group pose parametric solution are compared, take i group as example, if following formula is set up, i group pose parametric solution deposited in j group pose parameter class:

|θ _i-θ _j|<D ₆,||t _i-t _j||<D ₇,|s _i-s _j|<D ₈

Step 402, find out the maximum class of pose parametric solution number, and find out the point correspondence of such all pose parametric solution representatives, according to all corresponding point, again utilize the pose algorithm for estimating of the anti-change of scale in step 303 to calculate pose parameter, and as the output of final coupling positioning result, in order to proofread and correct the error of inertial navigation system.

The present invention compared with prior art, has following features:

(1) for existing aviation, with grating image matching navigation method, there is the problems such as data volume is large, efficiency is low, the present invention adopts map vector as matched data source, avoid the storage of mass data, improved the extraction efficiency of data and the time efficiency of matching algorithm.

(2) there is local feature and the inconsistent problem of global characteristics of wire key element when adopting normalization distance or described function as matching characteristic amount in existing vector graphics matching algorithm, the present invention adopts the angle of broken line as matching characteristic amount, the local angle of wire key element is identical with overall angle, and broken line angle feature has unchangeability for translation, rotation, yardstick, feature is obvious, and matching effect is better.

(3) the present invention is based on the thought of processing by different level, according to distance restraint condition, order constrained condition and error constraints condition, reduce successively the size of solution space, guaranteed rapidity and the reliability of matching algorithm.

In order to evaluate the performance of the map vector matching navigation method of the linear target that the present invention proposes, carried out l-G simulation test.In verification of correctness test, concrete test condition is as follows:

(1) do not consider picture noise and image vector error;

(2) database parameter: 116.05 °～116.60 ° of longitude scopes, 40.25 °～40.80 ° of latitude scope (being the scope of base vector figure);

(3) imageing sensor parameter: in the situation that being highly 0 ° for 4000m, course angle, the ground scope that imageing sensor can collect is: 0.04 ° of longitudinal, 0.04 °, latitude direction (being the size of template vector figure).

The in the situation that of diverse geographic location, different course angle, differing heights, design respectively three groups of tests, corresponding pose parameter is as shown in table 1.

Table 1 verification of correctness is tested default pose parameter

As shown in Figure 5, the large figure in left side represents vector reference map to be searched to test findings, and the little figure A on right side, B, C respectively analog image sensor gather the result of image after vector quantization.According to final matching results, template vector figure is transformed in base vector figure to the part comprising in square frame in figure as large in left side.As can be seen from Figure 5, in the situation that not considering error, three width template vector figure all can correctly mate, and have verified the correctness of algorithm.

On the computing machine of Pentium (R) 4 CPU 3.00GHz processors, 1GB internal memory, based on MATLABR2007b programmed environment, the reference diagram for 4 groups of different sizes carries out respectively emulation in the situation that of diverse location, the anglec of rotation and zoom factor.In the situation that guaranteeing that coupling is correct, the elapsed time of record Statistical Vector map-matching algorithm, as shown in table 2.

The real-time performance testing result of table 2 algorithm

As can be seen from Table 2, this algorithm has good real-time.Along with reference diagram diminishes gradually, the solution space that search for and compare also diminishes with regard to corresponding, and matching speed is further accelerated.That is to say, if the reference position error that other navigational system such as INS provide is less, the real-time performance of map vector Matching Navigation System is also just better.

Consider picture noise and Vectorization Error, further the reliability of analytical algorithm, has designed reliability compliance test.The grating image size of supposing collection is 2000 * 2000pixels, using height 4000m as benchmark, when imageing sensor is horizontal, ingestible ground scene scope is 0.04 ° * 0.04 °, be that ground bin corresponding to camera pixel unit is 0.00002 ° (0.072 rad, about 2.2m).And suppose that image vector error is in unit picture element length range, therefore in real time in figure the error of coordinate (longitudinal is identical with latitude direction) of each end points of vector line segment can to select standard deviation be the white noise of 0.00002 °.In l-G simulation test, adopting size is the reference diagram of 0.45 ° * 0.45 °, selects the pose parameter in table 1, carries out test of many times and can find out, in the situation that polar plot exists error in real time, this algorithm can correctly be located.

By 100 l-G simulation tests, the position and attitude error after coupling as shown in Figure 7 and Figure 8, can find out, the anglec of rotation error of 100 times is all less than 0.2 °, and longitudinal and latitudinal site error are all less than 0.18 rad (about 5.4m).Statistics is as shown in table 3, can find out, the standard deviation of site error is slightly less than ground bin size, and the standard deviation of angular error is all less than 0.06 °, and map vector Matching Navigation System can obtain higher course precision and positioning precision.

The error statistics result of table 3 test of many times

Above embodiment only, for explanation technological thought of the present invention, can not limit protection scope of the present invention with this, every technological thought proposing according to the present invention, and any change of doing on technical scheme basis, within all falling into protection domain of the present invention.

Claims (6)

1. a map vector matching navigation method for linear target for aviation, is characterized in that comprising the steps:

(1) judge whether to start navigation by map-matching system, if started, prepare base vector figure and the template vector figure of map match;

(2) template vector figure and base vector figure unification are arrived to same vector accuracy rank, and extract the angle feature of template vector figure and base vector figure;

(3) the template vector figure obtaining according to step (2) and the angle feature of base vector figure, utilize successively distance restraint condition, order constrained condition and three constraint conditions of error constraints condition progressively to limit the size of solution space, finally obtain the 3rd layer of solution space;

The particular content of described step (3) is:

Step 301, for the angle sequence of each broken line in template vector figure, calculate the unidirectional Hausdorff distance between all broken line angle sequences on itself and base vector figure, retain unidirectional Hausdorff distance on base vector figure and be less than predetermined threshold value D ₂all broken lines, as ground floor solution space;

In step 302, the ground floor solution space that obtains in step 301, according to broken line angle, sequentially reject the broken line not satisfying condition, to the broken line satisfying condition, find out point correspondence possible between template vector figure and base vector figure, and then set up second layer solution space;

Step 303, for all possible point correspondence in second layer solution space, utilize the position and orientation estimation method of anti-change of scale to solve rotation angle, side-play amount and scale factor, and utilize the rotation angle and the side-play amount that obtain to carry out after pose conversion template vector figure, with base vector figure compare and solve square mean error amount a little, square mean error amount is greater than to predetermined threshold value D ₅point correspondence from second layer solution space, reject away, thereby set up the 3rd layer of solution space;

The particular content of described step 303 is: suppose in second layer solution space, a certain group of point correspondence is: the coordinate of template vector figure point set P is p _i=[p _ix, p _iy] ^t, i=1 wherein, 2 ..., n, the coordinate of corresponding base vector figure point set Q is q _i=[q _ix, q _iy] ^t, it is q=sRp+t that the coordinate transform between note template vector figure and base vector figure is closed, and wherein, t is side-play amount, and s is scale factor, and R is rotation matrix, the detailed process of the position and orientation estimation method of anti-change of scale is as follows:

(1a) according to the following formula, the center point coordinate of calculating point set P and point set Q is:

$\stackrel{\&OverBar;}{p}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{p}_i,\stackrel{\&OverBar;}{q}=\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{q}_i$

(2a) according to the following formula, estimate scale factor for:

$\hat{s}=\sqrt{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-\stackrel{\&OverBar;}{q}\left|\right|}^2/\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|p_i-\stackrel{\&OverBar;}{p}\left|\right|}^2}$

(3a) according to the following formula, estimate the anglec of rotation for:

$a=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{iy}}-{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{ix}}),b=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}({\tilde{p}}_{\mathrm{ix}}\hat{s}{\tilde{q}}_{\mathrm{ix}}+{\tilde{p}}_{\mathrm{iy}}\hat{s}{\tilde{q}}_{\mathrm{iy}})$

(4a) according to the following formula, estimate side-play amount for:

$\hat{t}=\stackrel{\&OverBar;}{q}-\hat{s}\hat{R}\stackrel{\&OverBar;}{p}$

Rotation matrix wherein estimated value can obtain according to following formula:

$\hat{R}=\left[\begin{array}{cc}\cos \left(\widehat{\mathrm{\&theta;}}\right)& -\sin \left(\widehat{\mathrm{\&theta;}}\right)\\ \sin \left(\widehat{\mathrm{\&theta;}}\right)& \cos \left(\widehat{\mathrm{\&theta;}}\right)\end{array}\right]$

(5a) calculate square error e:

$e=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|q_i-(\hat{s}\hat{R}{p}_i+\hat{t})\left|\right|}^2};$

(4) the pose parametric solution of the 3rd layer of solution space is carried out to cluster, to comprise class that pose parametric solution is maximum as correct matching relationship, and then utilize pose algorithm for estimating to calculate pose parameter, as coupling positioning result, proofread and correct the error of inertial navigation system.

2. the map vector matching navigation method of linear target for a kind of aviation as claimed in claim 1, is characterized in that: the particular content of described step (1) is:

Step 101, according to inertial navigation system outgoing position, judge whether to enter navigation by map-matching region, if not, do not start navigation by map-matching system; If so, start navigation by map-matching system, allow aircraft keep horizontal attitude, and utilize airborne imageing sensor to gather ground image, enter step 102;

Step 102, be written near the map vector data storehouse based on wire key element inertial navigation system outgoing position, as the base vector figure of map match;

Step 103, for the linear target in Real-time Collection ground image, by vector technology, obtain the real-time polar plot based on wire key element, the coordinate system of this real-time polar plot adopt initial point in the image upper left corner, the pixel of the take image coordinate system that is unit;

Step 104, by the coordinate transform of real-time polar plot under geographic coordinate yardstick, as the template vector figure of map match.

3. the map vector matching navigation method of linear target for a kind of aviation as claimed in claim 1, is characterized in that: the particular content of described step (2) is:

Step 201, employing Douglas-Peucker method are compressed template vector figure and base vector figure, use identical distance threshold value D in compression process ₁thereby, template vector figure and base vector figure is unified under same vector accuracy rank;

Step 202, for step 201, compression obtains respectively real-time polar plot and base vector figure, all broken lines in sweep vector figure, and calculate corresponding broken line angle sequence.

4. the map vector matching navigation method of linear target for a kind of aviation as claimed in claim 1, is characterized in that: the particular content of described step 301 is: on note template vector figure, the angle sequence of certain broken line is A=[α ₁, α ₂..., α _n], on base vector figure, certain broken line angle sequence is k represents the numbering of this broken line on base vector figure, unidirectional Hausdorff distance h (A, B ^k) computing formula as follows:

$h(A,B^k)=\underset{a_i\&Element;A}{\max }\underset{{{\mathrm{\&beta;}}_j}^k\&Element;B^k}{\min }|a_i-{{\mathrm{\&beta;}}_j}^k|$

All angles in above formula reflection sequence A are to sequence B ^kminimal difference, as A and B ^kunidirectional Hausdorff distance h (A, B ^k) be greater than and preset predetermined threshold value D ₂time, judgement B ^kthe corresponding broken line broken line corresponding with A do not mate.

5. the map vector matching navigation method of linear target for a kind of aviation as claimed in claim 1, it is characterized in that: the particular content of described step 302 is: in the ground floor solution space that certain broken line is corresponding in template vector figure, each end points on a base vector figure broken line of usining is respectively set up the to be selected corresponding broken line identical with template vector figure broken line number of endpoint as starting point; Then, this broken line of template vector figure is compared with each angle of base vector figure corresponding broken line to be selected, note difference is at threshold value D ₃the interior angle number of take is N _s, total angle number is N, if ratio N _s/ N is greater than predetermined threshold value D ₄, think that the corresponding broken line of this base vector figure meets order constrained condition, and record this group point correspondence in solution space for the second time.

6. the map vector matching navigation method of linear target for a kind of aviation as claimed in claim 1, is characterized in that: the particular content of described step (4) is:

Step 401, for each pose parametric solution, set up a class, and calculate and other all pose parametric solutions between difference, if angle difference, position difference and yardstick difference are all less than respectively predetermined threshold value D ₆, D ₇, D ₈, this pose parametric solution is deposited in class;

Step 402, find out the maximum class of pose parametric solution number, and find out the point correspondence of such all pose parametric solution representatives, according to all corresponding point, utilize the pose algorithm for estimating of anti-change of scale to calculate pose parameter, and as the output of final coupling positioning result, in order to proofread and correct the error of inertial navigation system.