CN114279438B - Geomagnetic matching navigation method based on PSO and ICCP - Google Patents
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Abstract
The invention provides a geomagnetic matching navigation method based on PSO and ICCP, which comprises the following steps: 1. acquiring geomagnetic measurement values and Inertial Navigation System (INS) indication coordinates at the same time in a geomagnetic matching time period; 2. generating sub-regions in a global search region by a sliding window by adopting a strategy of selecting an optimal track by multiple times of matching, generating initialization particles by quadtrees in different sub-regions by each time of matching, and then performing coarse and fine matching on the inertial navigation instruction tracks by using PSO and ICCP sequentially, thereby obtaining a series of candidate tracks; 3. and the selection of the optimal track is regarded as a multi-attribute decision problem, and the track correlation and ICCP algorithm convergence of the magnetic measurement sequence of each candidate track and the real track are respectively used for comprehensive evaluation, so that the optimal track is selected for output, the inertial navigation accumulated error can be effectively eliminated, and the positioning precision of the integrated navigation system is improved.
Description
Technical Field
The invention relates to the technical field of geomagnetic matching auxiliary navigation, in particular to a geomagnetic matching navigation method based on PSO and ICCP.
Background
Inertial navigation systems, which are typical dead reckoning systems, have good autonomy and concealment, but their navigation errors accumulate over time and have poor long-term stability. Therefore, to compensate for inertial navigation accumulated errors, real-time or periodic corrections by other means of assisted navigation are required. The geomagnetic navigation has strong independence and passivity, errors are not accumulated with time, the concealment is good, and meanwhile, the geomagnetic field is used as a relatively stable geophysical field, so that excellent characteristics of all-day time, all-weather and all-region geomagnetic navigation are provided. Therefore, the geomagnetic navigation system is used for periodically correcting the accumulated errors of the inertial navigation system, so that the navigation positioning of the aircraft with high precision during long voyage is an important navigation mode.
The geomagnetic field is unique in position and serves as a theoretical basis, and geomagnetic navigation is achieved through a geomagnetic matching algorithm: based on the reference track position provided by the inertial navigation system, searching an optimal track matched with the geomagnetic sequence measured in real time in the geomagnetic reference map. And then fusing the matching result with the inertial navigation system through a combined filter, so as to correct the accumulated error of the inertial navigation system.
As a key technology of geomagnetic navigation, the geomagnetic matching algorithm determines the geomagnetic navigation accuracy to a great extent. Common matching algorithms are geomagnetic contour matching (MAGCOM) and iterative closest contour point algorithm (ICCP). The ICCP performs rigid transformation on the inertial navigation instruction track to enable the inertial navigation instruction track to continuously approach the geomagnetic contour line point sequence closest to the inertial navigation instruction track, and can eliminate position errors and heading errors at the same time. However, since ICCP can only converge to local optimum and is susceptible to initial position errors, there is a disadvantage that it is too dependent on initial position. In addition, inspired by the image matching technology, particle Swarm Optimization (PSO) matching algorithm is also started to be applied to geomagnetic matching navigation, has excellent global searching capability, and can realize effective complementation with ICCP.
Disclosure of Invention
Aiming at the defects in the prior art that an ICCP geomagnetic matching algorithm is greatly influenced by initial positioning errors and the like, the invention provides a geomagnetic matching navigation method based on PSO and ICCP.
The geomagnetic matching navigation method based on PSO and ICCP comprises the following steps of:
(1) The position of the carrier and the geomagnetic field intensity of the corresponding point are collected at fixed frequency, and an inertial navigation system outputs an inertial navigation indication trackGeomagnetic measured value corresponding to each track point position is +.>
(2) Then adopting a strategy of selecting an optimal track through multiple matching, generating a sub-region in a global search region by a sliding window, randomly generating a series of particles obeying two-dimensional normal distribution in an initialization sub-region indicated by the sliding window, managing and optimizing the randomly generated initialization particles through a quadtree method, and removing redundant particles;
coarse matching is carried out on inertial navigation indication tracks by PSO, each particle is composed of a group of track correction parameters, and corresponds to a matching track subjected to affine transformation, and the particle u= [ p ] x ,p y ,a,θ]Parameters respectively represent translation correction, course angle correction and scaling correction of the inertial navigation indication track in the x and y directions, and then the matching track corresponding to the particle is:
wherein the method comprises the steps ofThe ith track point coordinate of the corresponding track of the particle, < > is given>The ith track point indicating the track for inertial navigation is +.>Is a relative position coordinate of (a);
after generating initial particles in the initialization area through a quadtree, the PSO enables the inertial navigation indication track to continuously approach the real track through continuous iterative updating of the particles, and finally outputs an optimal matching track, wherein the iterative updating formula of the particles is as follows:
wherein the method comprises the steps ofAnd->The speed and position of the ith particle in the t-th iteration, c 1 、c 2 Is a learning factor, r 1 、r 2 Is uniformly distributed in [0,1 ]]Random number on, w is inertial weight, p i And p g The optimal position and the global optimal particle position of the current particle are respectively experienced;
the particle quality is evaluated by the particle fitness, and the particle fitnessMagnetic measurement sequence->And the corresponding sequence of magnetic values of the particle trajectories in the geomagnetic reference map +.>Absolute average difference calculation of (2):
(3) Then, fine matching is carried out on the coarse matching track acquired by PSO by using ICCP, the ICCP matching algorithm targets the nearest contour line point sequence S of the track X to be matched, and the track Y is obtained by carrying out rigid transformation T on track iteration, so that each track point corresponds to the corresponding magnetic value sequence in the geomagnetic reference pictureMagnetic data as close as possible to the actual track p>A geomagnetic contour line C corresponding to the geomagnetic reference map;
repeating the steps until the track transformation difference of two continuous iterations is smaller than a set threshold, taking the track after the last iteration as the matching result, and calculating the termination condition by the following formula:
wherein θ now 、θ before And l now 、l before The angle and the length delta of the rigid transformation are respectively the front and back θ And delta l Respectively setting an angle threshold value and a length threshold value;
(4) Repeatedly carrying out matching in the steps (2) and (3) on the inertial navigation instruction tracks, obtaining M candidate matching tracks, evaluating each track through ICCP algorithm convergence and track correlation between the matching tracks and the real track, and selecting an optimal track for outputting based on multi-attribute decision;
ICCP algorithm convergence is described by the average Euclidean distance between the output trajectory and the nearest contour point sequence of the last iteration of ICCP:
wherein D is j Is the algorithm convergence of the j candidate track, N is the track point number, Y i Is a candidate track point, S i Is the nearest contour point of the last iteration, W i Is the point weight;
track correlation C of matching track and real track j From linear correlation R j Correlation with geomagnetic intensity I j Common description:
C j ＝I' j +R' j
wherein R 'is' j And I' j Respectively, the lines after normalization treatmentCorrelation and geomagnetic intensity correlation;
and then, weighting and fusing the algorithm convergence and the track correlation by using an entropy weight method to obtain a track comprehensive evaluation index, selecting an optimal track to output, and realizing effective elimination of inertial navigation accumulated errors and high-precision autonomous navigation positioning.
As a further development of the invention, the stiffness transformation in step (3) is calculated by the following formula:
Y i ＝[R i |T i ]X i ＝R i X i +T i
wherein the rigid transformation matrix is [ R ] i |T i ],R i For rotating matrix, T i Is a translation matrix. Locus point X to be matched i (x i ,y i ) Through rigid transformation to point Y i (x i ,y i ). The beneficial effects are that: the invention discloses a geomagnetic matching navigation method based on PSO and ICCP, which adopts a mode of combining coarse matching and fine matching, utilizes the global searching capability of PSO to make up the defect that ICCP is greatly influenced by initial positioning errors, selects optimal track output from a plurality of matching results based on multi-attribute decision, and adopts a particle initialization strategy of combining sliding window with quadtree aiming at PSO. Compared with the prior art, the method has the advantages of high matching precision and high stability.
Drawings
FIG. 1 is a flow chart of the disclosed method;
fig. 2 is a schematic diagram of an ICCP algorithm in the disclosed method.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
the geomagnetic matching navigation method based on PSO and ICCP, as shown in figure 1, comprises the following steps:
step 1, collecting the position of a carrier and the geomagnetic field intensity of a corresponding point at a fixed frequency, and outputting an inertial navigation instruction track by an inertial navigation systemGeomagnetic measured value corresponding to each track point position is +.>Calculating the prior positioning error standard deviation of inertial navigation in the x and y directions according to the inertial navigation parameters, and setting a global search range according to the 3 sigma principle:
wherein the method comprises the steps ofThe method is an inertial navigation indication track initial track point, and U is a search area of an initial matching track point;
and 2, adopting a strategy of selecting the optimal track by multiple times of matching, generating sub-regions in the global search region U by a sliding window, generating initialization particles in different sub-regions by quadtrees in each time of matching, and performing coarse and fine matching on the inertial navigation instruction track by using PSO and ICCP sequentially, so as to obtain a series of candidate tracks.
Each particle in the PSO is composed of a group of track correction parameters, and corresponds to a matching track subjected to affine transformation. Particle u= [ p ] x ,p y ,a,θ]The parameters respectively represent translation correction, course angle correction and scaling correction of the inertial navigation instruction track in the x and y directions.
Particle initialization is mainly performed on particle translation parameters, and a series of particles obeying two-dimensional normal distribution are randomly generated in an initialization subarea indicated by a sliding window:
(p x ,p y )～N(c x ,c y ,w x /2,w y /2,0) (2)
wherein (c) x ,c y ) Sum (w) x ,w y ) The coordinates and the size of the center point of the sliding window are respectively.
And then, optimizing the randomly generated initialization particles through a quadtree method, removing redundant particles, and improving the optimizing efficiency of PSO. The quadtree continuously divides the initialization area into four areas according to the set number of particles, discards the area containing no particles after each division, and keeps the area containing single particles from being subdivided. The process will continue until the number of regions reaches the desired number of particles. Each region is then provided with one particle constituting an initialisation particle population. The quadtree uniformly distributes particles by eliminating redundant individuals, and simultaneously retains the two-dimensional normal distribution characteristics of the particles to the greatest extent.
The subsequent PSO-ICCP joint matching includes the steps of:
coarse matching is carried out on the inertial navigation instruction track by PSO, and the matching track corresponding to the single particle is as follows:
wherein the method comprises the steps ofThe ith track point coordinate of the corresponding track of the particle, < > is given>The ith track point indicating the track for inertial navigation is +.>Is used for the relative position coordinates of the two parts.
After generating initial particles in the initialization area through a quadtree, the PSO continuously approximates the inertial navigation indication track to the real track through continuous iterative updating of the particles according to the optimal position and the global optimal particle position which are experienced by the current particles, and finally outputs an optimal matching track, wherein the iterative updating formula of the particles is as follows:
wherein p is i And p g The optimal position and the global optimal particle position, respectively, that the current particle has undergone，And->The speed and position of the ith particle in the t-th iteration, c 1 、c 2 Is a learning factor reflecting the dependence of the particles on the optimal particles, r 1 、r 2 Is uniformly distributed in [0,1 ]]The random number, w, is an inertial weight, representing the effect of past speed on the current speed.
The inertia weight of each iteration can be continuously adjusted according to the particle fitness so as to improve the optimization efficiency of PSO:
wherein w is min And w max Is a preset minimum and maximum value of inertia weight,and->Respectively representing the inertia weight and fitness of the ith particle in the t-th iteration, +.>The average fitness of all particles is iterated at this time, the particle fitness is used for evaluating the particle quality, and the geomagnetic measurement sequence is adopted for +.>And the corresponding sequence of magnetic values of the particle trajectories in the geomagnetic reference map +.>Absolute average difference calculation of (2):
and 3, performing fine matching on the coarse matching track acquired by the PSO by using the ICCP, wherein the ICCP matching algorithm targets the nearest contour line point sequence of the track to be matched, and performing rigid transformation on track iteration to enable each track point to be as close as possible to the geomagnetic contour line corresponding to the geomagnetic measured value on the geomagnetic reference map.
The stiffness transformation is calculated by:
Y i ＝[R i |T i ]X i ＝R i X i +T i (7)
wherein the rigid transformation matrix is [ R ] i |T i ],R i For rotating matrix, T i Is a translation matrix. Locus point X to be matched i (x i ,y i ) Through rigid transformation to point Y i (x i ,y i )。
Repeating the steps until the track transformation difference of two continuous iterations is smaller than a set threshold, and taking the track after the last iteration as the matching result. The termination condition is calculated by the following formula:
wherein θ now 、θ before And l now 、l before The angle and the length delta of the rigid transformation are respectively the front and back θ And delta l The set angle and length thresholds, respectively.
And 4, repeatedly carrying out the matching in the steps 2 and 3 on the inertial navigation instruction track, obtaining M candidate matching tracks, evaluating each track through ICCP algorithm convergence and track correlation between the matching track and a real track, and selecting an optimal track for outputting based on multi-attribute decision.
ICCP algorithm convergence is described by the average Euclidean distance between the output trajectory and the nearest contour point sequence of the last iteration of ICCP:
wherein D is j Is the algorithm convergence of the j candidate track, N is the track point number, Y i Is a candidate track point, S i Is the nearest contour point of the last iteration, W i The point weight is calculated by Euclidean distance between the initial track point of the last iteration and the nearest contour line point of the initial track point:
track correlation C of matching track and real track j From linear correlation R j Correlation with geomagnetic intensity I j Common description:
C j ＝I' j +R' j (12)
wherein R 'is' j And I' j The linear correlation and geomagnetic intensity correlation after normalization processing are respectively:
and then weighting and fusing the algorithm convergence degree and the track correlation by using an entropy weight method to obtain a track comprehensive evaluation index, wherein the entropy weight method determines weights according to the information difference of each index, and the different indexes also need to be normalized into a uniform scale in the same way before weighting, and the information entropy and the corresponding weights of the different indexes are as follows:
in e D ,e C ,w D ,w C The information entropy and the weight of the algorithm convergence degree and the track correlation are respectively, and M is the number of candidate tracks, namely the matching times of the inertial navigation indication tracks.
The comprehensive evaluation index of the j candidate track is:
F j ＝w D ·D' j +w C ·C j ' (15)
and then, selecting a matching track with the minimum comprehensive evaluation index for output, and realizing effective elimination of inertial navigation accumulated errors and high-precision autonomous navigation positioning.
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.
Claims (2)
1. The geomagnetic matching navigation method based on PSO and ICCP comprises the following steps of:
(1) The position of the carrier and the geomagnetic field intensity of the corresponding point are collected at fixed frequency, and an inertial navigation system outputs an inertial navigation indication trackGeomagnetic measured value corresponding to each track point position is +.>
(2) Then adopting a strategy of selecting an optimal track through multiple matching, generating a sub-region in a global search region by a sliding window, randomly generating a series of particles obeying two-dimensional normal distribution in an initialization sub-region indicated by the sliding window, managing and optimizing the randomly generated initialization particles through a quadtree method, and removing redundant particles;
coarse matching is carried out on inertial navigation indication tracks by PSO, each particle is composed of a group of track correction parameters, and corresponds to a matching track subjected to affine transformation, and the particle u= [ p ] x ,p y ,a,θ]Parameters respectively represent translation correction, course angle correction and scaling correction of the inertial navigation indication track in the x and y directions, and then the matching track corresponding to the particle is:
wherein the method comprises the steps ofThe ith track point coordinate of the corresponding track of the particle, < > is given>The ith track point indicating the track for inertial navigation is +.>Is a relative position coordinate of (a);
after generating initial particles in the initialization area through a quadtree, the PSO enables the inertial navigation indication track to continuously approach the real track through continuous iterative updating of the particles, and finally outputs an optimal matching track, wherein the iterative updating formula of the particles is as follows:
wherein the method comprises the steps ofAnd->The speed and position of the ith particle in the t-th iteration, c 1 、c 2 Is a learning factor, r 1 、r 2 Is uniformly distributed in [0,1 ]]Random number on, w is inertial weight, p i And p g The optimal position and the global optimal particle position of the current particle are respectively experienced;
the particle quality is evaluated by the particle fitness, the particle fitness f i t Magnetic measurement sequenceAnd the corresponding sequence of magnetic values of the particle trajectories in the geomagnetic reference map +.>Absolute average difference calculation of (2):
(3) Then, fine matching is carried out on the coarse matching track acquired by PSO by using ICCP, the ICCP matching algorithm targets the nearest contour line point sequence S of the track X to be matched, and the track Y is obtained by carrying out rigid transformation T on track iteration, so that each track point corresponds to the corresponding magnetic value sequence in the geomagnetic reference pictureMagnetic data as close as possible to the actual track p>A geomagnetic contour line C corresponding to the geomagnetic reference map;
repeating the steps until the track transformation difference of two continuous iterations is smaller than a set threshold, taking the track after the last iteration as the matching result, and calculating the termination condition by the following formula:
wherein θ now 、θ before And l now 、l before The angle and the length delta of the rigid transformation are respectively the front and back θ And delta l Respectively setting an angle threshold value and a length threshold value;
(4) Repeatedly carrying out matching in the steps (2) and (3) on the inertial navigation instruction tracks, obtaining M candidate matching tracks, evaluating each track through ICCP algorithm convergence and track correlation between the matching tracks and the real track, and selecting an optimal track for outputting based on multi-attribute decision;
ICCP algorithm convergence is described by the average Euclidean distance between the output trajectory and the nearest contour point sequence of the last iteration of ICCP:
wherein D is j Is the algorithm convergence of the j candidate track, N is the track point number, Y i Is a candidate track point, S i Is the nearest contour point of the last iteration, W i Is the point weight;
track correlation C of matching track and real track j From linear correlation R j Correlation with geomagnetic intensity I j Common description:
C j ＝I' j +R' j
wherein R 'is' j And I' j The linear correlation and geomagnetic intensity correlation after normalization processing are respectively;
and then, weighting and fusing the algorithm convergence and the track correlation by using an entropy weight method to obtain a track comprehensive evaluation index, selecting an optimal track to output, and realizing effective elimination of inertial navigation accumulated errors and high-precision autonomous navigation positioning.
2. The geomagnetic matching navigation method based on PSO and ICCP as set forth in claim 1, wherein: the rigidity transformation in step (3) is calculated by the following formula:
Y i ＝[R i |T i ]X i ＝R i X i +T i
wherein the rigid transformation matrix is [ R ] i |T i ],R i For rotating matrix, T i To be a translation matrix, the track points X to be matched i (x i ,y i ) Through rigid transformation to point Y i (x i ,y i )。
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