CN114279438A - 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 a geomagnetic measurement value and an Inertial Navigation System (INS) indication coordinate at the same time in a geomagnetic matching time period; 2. adopting a strategy of selecting an optimal track by multiple matching, generating sub-areas in a global search area by a sliding window, generating initialization particles in different sub-areas by a quadtree in each matching, and then respectively performing coarse and fine matching on an inertial navigation indication track by sequentially using a PSO (particle swarm optimization) and an ICCP (information carrier control protocol), thereby obtaining a series of candidate tracks; 3. and regarding the selection of the optimal track as a multi-attribute decision problem, performing comprehensive evaluation on each candidate track by respectively using the track correlation of the candidate track and the magnetic measurement sequence of the real track and the convergence of the ICCP algorithm, and further selecting the optimal track for output, so that 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 assisted navigation, in particular to a geomagnetic matching navigation method based on PSO and ICCP.
Background
The inertial navigation system is a typical dead reckoning system, and has good autonomy and concealment, but navigation errors of the inertial navigation system accumulate over time, and the long-term stability is poor. Therefore, in order to compensate the inertial navigation accumulated error, the inertial navigation accumulated error needs to be corrected in real time or periodically by other auxiliary navigation modes. The geomagnetic navigation has strong independence and passivity, errors are not accumulated along with time, the concealment is good, and meanwhile, the geomagnetic field is used as a relatively stable geophysical field, so that the geomagnetic navigation has excellent characteristics of all-time, all-weather and all-region geomagnetic navigation. Therefore, the geomagnetic navigation system is used for regularly correcting the accumulated error of the inertial navigation system, so that high-precision navigation positioning of the aircraft during long-term navigation is obtained, and the geomagnetic navigation system is an important navigation mode.
The method is characterized in that the uniqueness of the geomagnetic field in position is taken as a theoretical basis, and geomagnetic navigation is realized through a geomagnetic matching algorithm: and based on the reference track position provided by the inertial navigation system, searching the optimal track matched with the real-time measurement geomagnetic sequence in the geomagnetic reference map. And then, fusing the matching result with the inertial navigation system through a combined filter, thereby correcting the accumulated error of the inertial navigation system.
As a key technology of geomagnetic navigation, a geomagnetic matching algorithm determines precision of geomagnetic navigation to a great extent. Commonly used matching algorithms are the geomagnetic contour matching (MAGCOM) and the iterative closest contour point algorithm (ICCP). The ICCP rigidly transforms the inertial navigation indication track to enable the inertial navigation indication track to continuously approach a geomagnetic isoline point sequence with the closest distance, and position errors and heading errors can be eliminated simultaneously. However, ICCP can only converge to local optimum and is easily affected by initial position error, and thus has a disadvantage of being too dependent on initial position. Furthermore, inspired by image matching technology, Particle Swarm Optimization (PSO) matching algorithm is also beginning to be applied to geomagnetic matching navigation, and has excellent global search capability, which can achieve effective complementation with ICCP.
Disclosure of Invention
In order to solve the technical problems, aiming at the defects of the prior art that the ICCP geomagnetic matching algorithm is greatly influenced by initial positioning error, the invention provides a geomagnetic matching navigation method based on PSO and ICCP.
The geomagnetic matching navigation method based on the PSO and the ICCP comprises the following steps, and is characterized in that:
(1) collecting the position of the carrier and the intensity of the geomagnetic field of the corresponding point at a fixed frequency, and outputting an inertial navigation indication track by an inertial navigation system
The geomagnetic actual measurement value corresponding to each track point position is
(2) Then adopting a strategy of selecting an optimal track by multiple matching, generating a sub-region in the global search region by a sliding window, randomly generating a series of particles which are subjected to two-dimensional normal distribution in an initialization sub-region indicated by the sliding window, managing and optimizing the randomly generated initialization particles by a quadtree method, and removing redundant particles;
carrying out rough matching on the inertial navigation indication track by PSO, wherein each particle is formed by a group of track correction parameters and corresponds to a matching track subjected to affine transformation, and the particle u is [ p ]x,py,a,θ]And the parameters respectively represent the translation correction, the course angle correction and the scaling correction of the inertial navigation indication track in the x and y directions, and then the matching track corresponding to the particle is as follows:
wherein
The ith track point coordinate of the corresponding track of the particle,
indicating the ith track point of the track relative to the initial track point for inertial navigation
Relative position coordinates of (a);
after the PSO generates initial particles through a quadtree in an initialization area, the inertial navigation indication track is continuously approximated to a real track through continuous iterative updating of the particles, and finally an optimal matching track is output, wherein the iterative updating formula of the particles is as follows:
wherein
And
respectively the velocity and position of the ith particle in the t iteration, c1、c2Is a learning factor, r1、r2Is uniformly distributed in [0,1 ]]A random number of (c), w is an inertial weight, piAnd pgRespectively the optimal position and the global optimal particle position which are experienced by the current particle;
the quality of the particles was evaluated by the particle fitness
Magnetic measurement sequence
Corresponding geomagnetic value sequence of particle track in geomagnetic reference map
Absolute mean difference calculation of:
(3) and then, carrying out fine matching on the coarse matching track obtained by the PSO by using an ICCP (integrated circuit chip), wherein an ICCP matching algorithm takes a closest isoline point sequence S of the track X to be matched as a target, and carries out rigid transformation T on track iteration to obtain a track Y so that each track point corresponds to a geomagnetic value sequence in a geomagnetic reference map
Magnetic survey data as close as possible to the actual track p
A corresponding geomagnetic contour line C on 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 according to the following formula:
wherein, thetanow、θbeforeAnd lnow、lbeforeAngle and length, delta, of two rigid transformations, front and back, respectivelyθAnd deltalRespectively set angle and length threshold values;
(4) repeatedly matching the inertial navigation indication tracks in the steps (2) and (3) to obtain M candidate matching tracks, evaluating each track through the convergence of an ICCP algorithm and the track correlation between the matching track and a real track, and selecting an optimal track to output based on multi-attribute decision;
the convergence of the ICCP algorithm is described by the average Euclidean distance between the output track and the nearest isoline point sequence of the last iteration of the ICCP:
wherein D isjIs the algorithm convergence of the jth candidate track, and N is the track pointNumber, YiIs a candidate track point, SiIs the nearest contour point, W, of the last iterationiIs the point weight;
trajectory correlation C of matching trajectory with real trajectoryjFrom a linear dependence RjCorrelation with geomagnetic intensity IjCollectively described:
Cj＝I'j+R'j
wherein R'jAnd l'jRespectively carrying out normalization processing on the linear correlation and the geomagnetic intensity correlation;
and then, carrying out weighted fusion on the algorithm convergence and the track correlation by using an entropy weight method to obtain a track comprehensive evaluation index, selecting an optimal track output, and realizing effective elimination of inertial navigation accumulated errors and high-precision autonomous navigation positioning.
As a further improvement of the present invention, the stiffness transformation in step (3) is calculated by the following formula:
Yi＝[Ri|Ti]Xi＝RiXi+Ti
wherein the rigid transformation matrix is [ R ]i|Ti],RiFor a rotation matrix, TiIs a translation matrix. Track point X to be matchedi(xi,yi) Through rigid transformation to point Yi(xi,yi). Has the advantages that: the invention discloses a geomagnetic matching navigation method based on PSO and ICCP, which adopts a mode of combining rough matching and fine matching, makes up the defect that the ICCP is greatly influenced by initial positioning error by using the global search capability of the PSO, selects an optimal track from results of multiple matching to output based on multi-attribute decision, and adopts a particle initialization strategy of combining a sliding window with a quadtree aiming at the PSO. Compared with the prior art, the method has the advantages of high matching precision and strong stability.
Drawings
FIG. 1 is a flow chart of the disclosed method;
FIG. 2 is a schematic diagram of an ICCP algorithm in the method disclosed by the invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
as shown in fig. 1, the geomagnetic matching navigation method based on PSO and ICCP includes the following steps:
wherein
The method comprises the steps that an initial track point of an inertial navigation indication track is obtained, and U is a search area of an initial matching track point;
and 2, adopting a strategy of selecting an optimal track by multiple matching, generating sub-areas in the global search area U by a sliding window, respectively generating initialization particles in different sub-areas by a quadtree in each matching, and then performing coarse and fine matching on the inertial navigation indication track by sequentially using the PSO and the ICCP, thereby obtaining a series of candidate tracks.
Each particle in the PSO is formed by a group of track correction parameters and corresponds to a matching track subjected to affine transformation. Particle u ═ px,py,a,θ]The parameters respectively represent translation correction and navigation of the inertial navigation indication track in the x and y directionsAngle correction and zoom correction.
Particle initialization is mainly performed according to particle translation parameters, and a series of particles which are subject to two-dimensional normal distribution are randomly generated in an initialization sub-region indicated by a sliding window:
(px,py)～N(cx,cy,wx/2,wy/2,0) (2)
wherein (c)x,cy) And (w)x,wy) The coordinates and the size of the center point of the sliding window are respectively.
And then managing and optimizing the randomly generated initialization particles by a quadtree method, eliminating redundant particles and improving the PSO optimizing efficiency. The quadtree continuously divides the initialization region into four regions according to the set number of particles, discards regions not containing particles after each division, and keeps regions containing single particles from being divided. This process will continue until the number of regions reaches the desired number of particles. Each region is then provided with a particle constituting the initialising particle population. The quadtree enables the particles to be uniformly distributed by removing redundant individuals, and simultaneously, the two-dimensional normal distribution characteristic of the particles is kept to the maximum extent.
The subsequent PSO-ICCP joint matching comprises the following steps:
carrying out rough matching on the inertial navigation indication track by PSO, wherein the matching track corresponding to a single particle is as follows:
wherein
The ith track point coordinate of the corresponding track of the particle,
indicating the ith track point of the track relative to the initial track point for inertial navigation
Relative position coordinates of.
After the PSO generates initial particles through a quadtree in an initialization area, according to the optimal position and the global optimal particle position which are experienced by the current particle, the inertial navigation indication track is continuously close to the real track through continuous iterative updating of the particle, and finally the optimal matching track is output, wherein the iterative updating formula of the particle is as follows:
wherein p isiAnd pgRespectively the optimal position experienced by the current particle and the globally optimal particle position,
and
respectively the velocity and position of the ith particle in the t iteration, c1、c2Is a learning factor reflecting the degree of dependence of the particle on the optimal particle, r1、r2Is uniformly distributed in [0,1 ]]The random number above, w, is the inertial weight, representing the effect of past velocity on the current velocity.
The inertia weight of each iteration is continuously adjusted according to the particle fitness so as to improve the optimization efficiency of the PSO:
wherein wminAnd wmaxAre preset inertia weight minimum and maximum values,
and
respectively representing the inertia weight and the fitness of the ith particle in the t iteration,
the average fitness of all the particles in the iteration is used for evaluating the quality of the particles, and the geomagnetic measurement sequence is used for
Corresponding geomagnetic value sequence of particle track in geomagnetic reference map
Absolute mean difference calculation of:
and 3, performing fine matching on the coarse matching track acquired by the PSO by using the ICCP, and performing rigid transformation on track iteration by taking the closest contour point sequence of the track to be matched as a target according to an ICCP matching algorithm shown in fig. 2 to enable each track point to be as close to the corresponding geomagnetic contour of the geomagnetic measurement value on the geomagnetic reference map as possible.
The rigid transformation is calculated by:
Yi＝[Ri|Ti]Xi＝RiXi+Ti (7)
wherein the rigid transformation matrix is [ R ]i|Ti],RiFor a rotation matrix, TiIs a translation matrix. Track point X to be matchedi(xi,yi) Through rigid transformation to point Yi(xi,yi)。
And repeating the steps until the track transformation difference of two continuous iterations is smaller than a set threshold value, and taking the track after the last iteration as the matching result. The termination conditions were calculated from the following formula:
wherein, thetanow、θbeforeAnd lnow、lbeforeAngle and length, delta, of two rigid transformations, front and back, respectivelyθAnd deltalRespectively, a set angle and a length threshold.
And 4, repeatedly performing matching in the steps 2 and 3 on the inertial navigation indication track to obtain M candidate matching tracks, evaluating each track through the convergence degree of the ICCP algorithm and the track correlation between the matching track and the real track, and selecting the optimal track to output based on multi-attribute decision.
The convergence of the ICCP algorithm is described by the average Euclidean distance between the output track and the nearest isoline point sequence of the last iteration of the ICCP:
wherein D isjIs the algorithm convergence of the jth candidate trajectory, N is the number of trajectory points, YiIs a candidate track point, SiIs the nearest contour point, W, of the last iterationiThe point weight is calculated by the Euclidean distance between the initial track point of the last iteration and the nearest contour point:
trajectory correlation C of matching trajectory with real trajectoryjFrom a linear dependence RjCorrelation with geomagnetic intensity IjCollectively described:
Cj＝I'j+R'j (12)
wherein R'jAnd l'jRespectively, the linear correlation and the geomagnetic intensity correlation after normalization processing:
then, an entropy weight method is used for weighting and fusing the convergence degree and the track correlation of the algorithm to obtain a track comprehensive evaluation index, the weight is determined by the entropy weight method according to the information difference of each index, different indexes are normalized into a unified scale in the same way before weighting, and the information entropy and the corresponding weight are as follows:
in the formula eD,eC,wD,wCRespectively, the information entropy and the weight of the algorithm convergence and the track correlation, wherein M is the number of candidate tracks, namely the matching times of the inertial navigation indication tracks.
Then the comprehensive evaluation index of the jth candidate track is as follows:
Fj＝wD·D'j+wC·Cj' (15)
and then, selecting the 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 a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (2)
1. The geomagnetic matching navigation method based on the PSO and the ICCP comprises the following steps, and is characterized in that:
(1) collecting the position of the carrier and the intensity of the geomagnetic field of the corresponding point at a fixed frequency, and outputting an inertial navigation indication track by an inertial navigation system
Each track point position correspondingMeasured value of geomagnetism is
(2) Then adopting a strategy of selecting an optimal track by multiple matching, generating a sub-region in the global search region by a sliding window, randomly generating a series of particles which are subjected to two-dimensional normal distribution in an initialization sub-region indicated by the sliding window, managing and optimizing the randomly generated initialization particles by a quadtree method, and removing redundant particles;
carrying out rough matching on the inertial navigation indication track by PSO, wherein each particle is formed by a group of track correction parameters and corresponds to a matching track subjected to affine transformation, and the particle u is [ p ]x,py,a,θ]And the parameters respectively represent the translation correction, the course angle correction and the scaling correction of the inertial navigation indication track in the x and y directions, and then the matching track corresponding to the particle is as follows:
wherein
The ith track point coordinate of the corresponding track of the particle,
indicating the ith track point of the track relative to the initial track point for inertial navigation
Relative position coordinates of (a);
after the PSO generates initial particles through a quadtree in an initialization area, the inertial navigation indication track is continuously approximated to a real track through continuous iterative updating of the particles, and finally an optimal matching track is output, wherein the iterative updating formula of the particles is as follows:
wherein
And
respectively the velocity and position of the ith particle in the t iteration, c1、c2Is a learning factor, r1、r2Is uniformly distributed in [0,1 ]]A random number of (c), w is an inertial weight, piAnd pgRespectively the optimal position and the global optimal particle position which are experienced by the current particle;
the quality of the particles was evaluated by the particle fitness fi tMagnetic measurement sequence
Corresponding geomagnetic value sequence of particle track in geomagnetic reference map
Absolute mean difference calculation of:
(3) and then, carrying out fine matching on the coarse matching track obtained by the PSO by using an ICCP (integrated circuit chip), wherein an ICCP matching algorithm takes a closest isoline point sequence S of the track X to be matched as a target, and carries out rigid transformation T on track iteration to obtain a track Y so that each track point corresponds to a geomagnetic value sequence in a geomagnetic reference map
Magnetic survey data as close as possible to the actual track p
A corresponding geomagnetic contour line C on 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 according to the following formula:
wherein, thetanow、θbeforeAnd lnow、lbeforeAngle and length, delta, of two rigid transformations, front and back, respectivelyθAnd deltalRespectively set angle and length threshold values;
(4) repeatedly matching the inertial navigation indication tracks in the steps (2) and (3) to obtain M candidate matching tracks, evaluating each track through the convergence of an ICCP algorithm and the track correlation between the matching track and a real track, and selecting an optimal track to output based on multi-attribute decision;
the convergence of the ICCP algorithm is described by the average Euclidean distance between the output track and the nearest isoline point sequence of the last iteration of the ICCP:
wherein D isjIs the algorithm convergence of the jth candidate trajectory, N is the number of trajectory points, YiIs a candidate track point, SiIs the nearest contour point, W, of the last iterationiIs the point weight;
trajectory correlation C of matching trajectory with real trajectoryjFrom a linear dependence RjCorrelation with geomagnetic intensity IjCollectively described:
Cj＝I'j+R'j
wherein R'jAnd l'jRespectively carrying out normalization processing on the linear correlation and the geomagnetic intensity correlation;
and then, carrying out weighted fusion on the algorithm convergence and the track correlation by using an entropy weight method to obtain a track comprehensive evaluation index, selecting an optimal track output, and realizing effective elimination of inertial navigation accumulated errors and high-precision autonomous navigation positioning.
2. The PSO and ICCP-based geomagnetic matching navigation method according to claim 1, wherein: the stiffness transformation in step (3) is calculated by the following formula:
Yi＝[Ri|Ti]Xi＝RiXi+Ti
wherein the rigid transformation matrix is [ R ]i|Ti],RiFor a rotation matrix, TiIs a translation matrix. Track point X to be matchedi(xi,yi) Through rigid transformation to point Yi(xi,yi)。
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102014004060A1 (en) * | 2014-03-10 | 2015-09-10 | Northrop Grumman Litef Gmbh | METHOD AND DEVICE FOR DETERMINING NAVIGATION DATA |
| CN106123892A (en) * | 2016-06-22 | 2016-11-16 | 武汉科技大学 | A kind of robot localization method based on wireless sensor network Yu earth magnetism map |
| CN107270891A (en) * | 2017-05-05 | 2017-10-20 | 哈尔滨工业大学 | Inertia earth magnetism matching locating method based on Robust filter |
| CN111397599A (en) * | 2020-02-25 | 2020-07-10 | 河海大学 | Improved ICCP (Integrated Circuit chip) underwater geomagnetic matching method based on triangular matching algorithm |
| CN111895995A (en) * | 2020-06-03 | 2020-11-06 | 东南大学 | PSO-based aircraft formation multidimensional geomagnetic matching navigation method and system |
-
2021
- 2021-12-17 CN CN202111553624.2A patent/CN114279438B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102014004060A1 (en) * | 2014-03-10 | 2015-09-10 | Northrop Grumman Litef Gmbh | METHOD AND DEVICE FOR DETERMINING NAVIGATION DATA |
| CN106123892A (en) * | 2016-06-22 | 2016-11-16 | 武汉科技大学 | A kind of robot localization method based on wireless sensor network Yu earth magnetism map |
| CN107270891A (en) * | 2017-05-05 | 2017-10-20 | 哈尔滨工业大学 | Inertia earth magnetism matching locating method based on Robust filter |
| CN111397599A (en) * | 2020-02-25 | 2020-07-10 | 河海大学 | Improved ICCP (Integrated Circuit chip) underwater geomagnetic matching method based on triangular matching algorithm |
| CN111895995A (en) * | 2020-06-03 | 2020-11-06 | 东南大学 | PSO-based aircraft formation multidimensional geomagnetic matching navigation method and system |
Non-Patent Citations (2)
| Title |
|---|
| 王立辉: "飞行器编队PSO 多维地磁匹配算法", 电光与控制 * |
| 肖晶;齐晓慧;段修生;: "一种改进的地磁平缓区的地磁匹配算法", 火力与指挥控制, no. 09 * |
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