CN110487276A - A kind of sample vector matching locating method based on correlation analysis - Google Patents
A kind of sample vector matching locating method based on correlation analysis Download PDFInfo
- Publication number
- CN110487276A CN110487276A CN201910770081.6A CN201910770081A CN110487276A CN 110487276 A CN110487276 A CN 110487276A CN 201910770081 A CN201910770081 A CN 201910770081A CN 110487276 A CN110487276 A CN 110487276A
- Authority
- CN
- China
- Prior art keywords
- vector
- particle
- matching
- point
- points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000013598 vector Substances 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000010219 correlation analysis Methods 0.000 title claims abstract description 18
- 239000002245 particle Substances 0.000 claims abstract description 79
- 238000005070 sampling Methods 0.000 claims abstract description 44
- 230000005484 gravity Effects 0.000 claims abstract description 37
- 238000012937 correction Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims abstract description 10
- 238000012952 Resampling Methods 0.000 claims abstract description 4
- 238000010606 normalization Methods 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims description 9
- 230000007704 transition Effects 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000006872 improvement Effects 0.000 description 2
- 238000012876 topography Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005358 geomagnetic field Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
Abstract
The invention discloses a kind of sample vector matching locating method based on correlation analysis, is related to Navigation, Guidance and Control technical field, can be improved the matching precision of gravitational vectors matching algorithm.Specifically: obtain the output point at inertial navigation system each moment and the matching point set based on particle filter.The vector A of current time match point and last moment match point composition, the vector B of current time output point and last moment output point composition.Phasing is carried out if being more than angle threshold if the deflection difference of A and B, if whether difference is more than that distance threshold carries out range correction between the Euclidean distance of A and B.If the gap of phase information, range information and gravity anomaly value information between the sampling particle point and inertial navigation tracing point of particle filter process is more than threshold value, correlation analysis then is carried out to the phase relation between sampling particle point and recalculates particle weight, then the normalization of particle weight and the residual error resampling of particle collection are carried out, obtains final matching results after weighted sum.
Description
Technical Field
The invention relates to the technical field of navigation, guidance and control, in particular to a sampling vector matching positioning method based on correlation analysis.
Background
The inertial navigation system adopts a completely autonomous working mode and is highly concealed, but because the error of the inertial navigation system is accumulated along with time, the navigation requirement of the underwater vehicle during long-term navigation cannot be met, and therefore the error needs to be corrected. In the passive navigation auxiliary means, the information of the marine geophysical field is rich and the characteristics are obvious, so that the auxiliary correction of inertial navigation by utilizing a gravity field, a geomagnetic field, terrain and the like becomes a classical means. The seafloor topography is obtained by multi-beam measurements, and the implementation of topography assistance methods becomes difficult in some complex terrain areas. The gravity field generally does not change along with time, has good space-time distribution characteristics, navigation positioning information obtained by matching the characteristics and the information has autonomy, and the precision does not diverge along with time, so that the requirement of underwater navigation positioning is met.
The gravity-assisted inertial navigation system comprises four parts: the system comprises an inertial navigation system, an ocean gravity field background image, an ocean gravity sensor and a gravity matching algorithm. Wherein the gravity matching algorithm is a key technology of gravity-assisted navigation. Conventional gravity matching algorithms fall into two categories: sequence matching and single point matching. Representative algorithms of the sequence matching algorithm are the ICCP algorithm and the related extremum algorithm. The sequence matching algorithm needs to collect a series of matching points as a whole for matching, so that the real-time performance is poor. The representative algorithm of the single-point matching mainly comprises a Sangyo terrain aided navigation method based on the extended Kalman filtering, the SITAN algorithm can carry out real-time measurement and estimation, but an accurate initial position is required, the anti-jamming capability is poor, and Kalman filtering divergence is possibly caused by linearization errors.
The sampling points of the matching algorithms are independent from each other, and the gravity vector matching algorithm takes the position correlation between the sampling points into consideration, so that the matching precision is improved compared with the traditional method. However, the navigation trajectory of the aircraft varies from navigation mission to mission, so the accuracy of the method can be further improved.
Therefore, how to further improve the matching precision of the gravity vector matching algorithm and make the matching track closer to the real track is a problem to be solved urgently at present
Disclosure of Invention
In view of this, the invention provides a sampling vector matching positioning method based on correlation analysis, which can further improve the matching precision of the gravity vector matching algorithm, so that the matching track is closer to the real track.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
in the navigation process of the underwater carrier, output points of the inertial navigation system at all times are obtained to form an inertial navigation track point set, and a particle filter-based matching point set is obtained according to a gravity-assisted inertial navigation matching model.
And obtaining a vector A formed by the current time matching point and the last time matching point in the matching point set and a vector B formed by the current time output point and the last time output point in the inertial navigation track point set.
And taking initial matching points in the matching point set to form a matched point set, and carrying out rigid transformation on the matched point set to obtain an error-free inertial navigation track.
Judging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, if so, correcting the azimuth angle of the vector A formed by the current moment matching point and the last moment matching point according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filter; otherwise no correction is required.
Judging whether the difference value between the Euclidean distance of the vector A and the Euclidean distance of the vector B exceeds a set distance threshold value, if so, correcting the Euclidean distance of the vector A formed by the matching point at the current moment and the matching point at the previous moment according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filtering; otherwise no correction is required.
And judging whether the difference between the phase information, the distance information and the gravity abnormal value information between the sampling particle points and the inertial navigation track points in the particle filtering process exceeds a threshold value, and if so, performing correlation analysis on the phase relationship between the sampling particle points to recalculate the particle weight.
And carrying out normalization processing on the particle weight, then carrying out residual resampling on the particle set, and carrying out weighted summation to obtain a final matching result.
Furthermore, during the navigation process of the underwater vehicle, a gravimeter is adopted to obtain a gravity anomaly measurement value.
The sampling point set is { P'k,P′k+1,P′k+2,P′k+3,…,P′k+nN +1 sampling points, k-k + n being time, wherein the vector information between the sampling points satisfiesi is a positive integer, 1-n is taken, epsilon is an error value set according to the precision of the gravimeter, dist (. +) is an Euclidean distance function.
Further, when the current time is the (k + 1) th time and the previous time is the (k) th time, the vector a isThe vector B is
Judging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, namely judging
Whether or not this is true.
Whereinζ is an angle threshold set according to an empirical value for a unit vector in the horizontal direction.
Further, determining whether a difference between the euclidean distance of the vector a and the euclidean distance of the vector B exceeds a set distance threshold specifically includes:
epsilon is the set distance threshold.
And further, judging whether the difference between the phase information, the distance information and the gravity abnormal value information between the sampling particle point and the inertial navigation track point exceeds a threshold value.
Namely, whether the following three discriminants are satisfied simultaneously is judged:
the angle information and length information of the vector formed by the ith particle at the k +1 th time and the ith particle at the k +1 th time are respectivelyThe angle information of the vector formed by the k-th time matching point and the k + 1-th time matching point isThe length information is G (y) is the gravity anomaly value of the ith particle at the time of the (k + 1) < th > timek+1) And sampling the gravity anomaly measured value of the particle point at the k +1 th moment.
Performing correlation analysis on the phase relationship among the sampling particle points to recalculate the particle weight, specifically:
the weight of the ith particle at the (k + 1) th moment is recalculated into
Where σ is the set variance.
Has the advantages that:
according to the method, the vector matching algorithm is improved, the characteristic of high short-time precision of inertial navigation is utilized, the phase correction and the distance correction are carried out on the inertial navigation track in sequence, and the longitude and latitude errors of the gravity matching track are effectively reduced. Meanwhile, relevance analysis is carried out on the position vector information between the inertial navigation track points and the matching track by introducing a relevance analysis algorithm, and the particle weight is considered, so that the matching precision is improved. The matching precision of the gravity matching algorithm is further improved through the improvement of the two aspects, and the matching track is closer to the real track.
Drawings
Fig. 1 is a schematic flow chart of a sampling vector matching positioning method based on correlation analysis according to the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a gravity sampling vector matching positioning method, as shown in figure 1, comprising the following steps:
acquiring output points of an inertial navigation system at each moment in the navigation process of an underwater carrier to form an inertial navigation track point set; and obtaining a matching point set based on particle filtering according to the gravity-assisted inertial navigation matching model.
In the navigation process of the underwater carrier, acquiring a gravity anomaly measured value by adopting a gravimeter;
the sampling point set is { P'k,P′k+1,P′k+2,P′k+3,…,P′k+nN +1 sampling points, k-k + n being time, wherein the vector information between the sampling points satisfiesi is a positive integer, 1-n is taken, epsilon is an error value set according to the precision of the gravimeter, dist (. +) is an Euclidean distance function. (wherein sampling point P'kExpressed in longitude and latitude information, i.e. P'k(L′k,λ′k)，L′kIs P'kLongitude of λ'kIs P'kThe latitude of (a) is determined,
in the embodiment of the invention, the phase and distance information of the vector formed by the matching point of the current sampling moment and the last moment is considered into the discriminant by utilizing the characteristic of high short-time precision of inertial navigation. Mainly comprises the steps of step two, step three and step four.
Step two, the vector A formed by the current matching point and the last matching point of the particle filter isThe vector B formed by the output point of the inertial navigation track point set at the current moment and the output point at the last moment is
And taking initial matching points in the matching point set to form a matched point set, and carrying out rigid transformation on the matched point set to obtain an error-free inertial navigation track.
Judging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, if so, correcting the azimuth angle of the vector A formed by the current moment matching point and the last moment matching point according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filter; otherwise no correction is required.
The step is phase correction, and in order to better represent angle information, a polar coordinate representation method is adopted, and a sampling point P is adoptedkWhen the polar coordinates of (c) are (l, θ), the coordinates of the sampling point in the rectangular coordinate system are (l · cos θ, l · sin θ).
In the embodiment of the invention, the current time is the (k + 1) th time, the last time is the kth time, and the vector A isThe vector B isJudging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, namely judging
Whether or not this is true.
Whereinζ is an angle threshold set according to an empirical value for a unit vector in the horizontal direction.
In the embodiment of the invention, a sine function is taken into consideration as a differential operatorWhen the deviation position is small, the eigen function of (2) can be approximated asThe eigenfunction problem of (2). And considering the taylor expansion:
and
the navigation track changes due to different navigation tasks in the navigation process, and the sine function discrimination precision is high when the small angle range of the heading angle changes, and the method is also suitable for turning tracks.
If the above discriminant expression is not satisfied, the expression is P'kIs an origin, has an angle theta with the horizontal direction and has a distance of an inertial navigation sampling point as a vectorThen P ″)k+1Is the central position of the particles sprinkled in the particle filterAnd recalculating the state transition probability in the particle filter matching, wherein theta is the error-free inertial navigation track angle.
Step three, judging whether the difference value between the Euclidean distance of the vector A and the Euclidean distance of the vector B exceeds a set distance threshold value, if so, correcting the Euclidean distance of the vector A formed by the matching point at the current moment and the matching point at the previous moment according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filter; otherwise no correction is required.
This step is distance correction. In the embodiment of the present invention, determining whether a difference between the euclidean distance of the vector a and the euclidean distance of the vector B exceeds a set distance threshold specifically includes:
epsilon is the set distance threshold. (specific method for recalculating the center position of the particles sprinkled in the particle filter is P'kAs a center of circle, inMake a circle for the radius, and vectorIs C'2. Then is formed byAnd recalculating the center position of the sprinkled particles in the particle filter.
And step four, adding the phase relation among the sampling points into a correlation analysis link according to the characteristic of high short-time precision of inertial navigation. And judging whether the difference between the phase information, the distance information and the gravity abnormal value information between the sampling particle points and the inertial navigation track points in the particle filtering process exceeds a threshold value, and if so, performing correlation analysis on the phase relationship between the sampling particle points to recalculate the particle weight.
In the embodiment of the invention, considering that INS has high short-time precision, the relative position relation between sampling points is added into a correlation analysis link, in each sampling time, a path section formed by the optimal sampling points is parallel to a corresponding inertial navigation path section as much as possible, and the angle and the length of a vector formed by the sampling points are selected to express the parallel relation. Defining the angle:defining the length:wherein,the angle information of the vector is formed for the i-1 th and i-th sample points,length information of the vector formed for the i-1 th and i-th sample points, λiAnd LiRespectively, the longitude and latitude information of the ith particle. In the invention, the particle filtering based on Bayesian estimation is introduced, and the phase information of the sampling point and the inertial navigation track point is subjected to correlation analysis and is considered in the particle weight.
In the embodiment of the invention, whether the phase information, the distance information and the difference between the gravity abnormal value information of the sampling particle point and the inertial navigation track point exceed the threshold value or not is judged, namely whether the following three discriminants are simultaneously satisfied is judged:
the angle information and length information of the vector formed by the ith particle at the k +1 th time and the ith particle at the k +1 th time are respectivelyThe angle information of the vector formed by the k-th time matching point and the k + 1-th time matching point isThe length information is G (y) is the gravity anomaly value of the ith particle at the time of the (k + 1) < th > timek+1) Sampling the gravity anomaly measured value of the particle point at the (k + 1) th moment;
the phase relation between the sampling particle points is subjected to correlation analysis to recalculate the particle weight, the correlation analysis is a performance index, and the most common algorithms comprise three algorithms: cross correlation algorithm (COR), mean absolute difference algorithm (MAD) and mean square error algorithm (MSD), where the mean square error algorithm is chosen to take the vector position information of the particle points into account in the particle weights.
Mean square error algorithm (MSD):
wherein, gr(i) Is the measured gravity anomaly value, gm(i) The number of particle points sprinkled for each match is n for the gravity anomaly value stored in the digital gravity map.
The weight of the ith particle at the (k + 1) th moment is recalculated into
Wherein σ is a set variance, and is specifically set according to an empirical value.
And 5, carrying out normalization processing on the particle weight, then carrying out residual error resampling on the particle set, and obtaining a final matching result after weighting summation.
Compared with the traditional method, the method effectively reduces the longitude and latitude errors of the gravity matching track, and enables the algorithm to have better robustness when the track has a curve.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A sampling vector matching positioning method based on correlation analysis is characterized by comprising the following steps:
in the navigation process of the underwater carrier, acquiring output points of the inertial navigation system at each moment to form an inertial navigation track point set, and acquiring a particle filter-based matching point set according to a gravity-assisted inertial navigation matching model;
obtaining a vector A formed by the matching points at the current moment in the matching point set and the matching points at the previous moment, and a vector B formed by the output points at the current moment in the inertial navigation track point set and the output points at the previous moment;
taking initial matching points in the matching point set to form a matched point set, and carrying out rigid transformation on the matched point set to obtain an error-free inertial navigation track;
judging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, if so, correcting the azimuth angle of the vector A formed by the current moment matching point and the last moment matching point according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filter; otherwise, no correction is needed;
judging whether the difference value between the Euclidean distance of the vector A and the Euclidean distance of the vector B exceeds a set distance threshold value, if so, correcting the Euclidean distance of the vector A formed by the matching point at the current moment and the matching point at the previous moment according to the error-free inertial navigation track, and recalculating the central position and the state transition probability of the particles sprayed in the particle filtering; otherwise, no correction is needed;
judging whether the difference between phase information, distance information and gravity abnormal value information between sampling particle points and inertial navigation track points in the particle filtering process exceeds a threshold value, and if so, performing correlation analysis on the phase relationship between the sampling particle points to recalculate the particle weight;
and carrying out normalization processing on the particle weight, then carrying out residual resampling on the particle set, and carrying out weighted summation to obtain a final matching result.
2. The method of claim 1, further comprising, during the navigation of the underwater vehicle, obtaining a gravity anomaly measurement using a gravimeter;
the sampling point set is { P'k,P′k+1,P′k+2,P′k+3,…,P′k+nN +1 sampling points, k-k + n being time, wherein the vector information between the sampling points satisfiesi is a positive integer, 1-n is taken, epsilon is an error value set according to the precision of the gravimeter, dist (#) is an Euclidean distance function.
3. The method of claim 2, wherein the current time is the (k + 1) th time, and the previous time is the (k) th time, then the vector a isThe vector B is
Judging whether the difference value between the direction angle of the vector A and the direction angle of the vector B exceeds a set angle threshold value, namely judging
Whether the result is true or not;
whereinζ is an angle threshold set according to an empirical value for a unit vector in the horizontal direction.
4. The method according to claim 3, wherein said determining whether the difference between the euclidean distance of the vector a and the euclidean distance of the vector B exceeds a predetermined distance threshold is:
epsilon is the set distance threshold.
5. The method of claim 4, wherein the method determines whether differences between phase information, distance information, and gravity outlier information between the sampled particle points and the inertial navigation trajectory points exceed a threshold;
namely, whether the following three discriminants are satisfied simultaneously is judged:
the angle information and length information of the vector formed by the ith particle at the k +1 th time and the ith particle at the k +1 th time are respectivelyThe angle information of the vector formed by the k-th time matching point and the k + 1-th time matching point isThe length information is Is the ith at the k +1 th timeGravity anomaly value of particle, g (y)k+1) Sampling the gravity anomaly measured value of the particle point at the (k + 1) th moment;
the correlation analysis of the phase relationship between the sampling particle points is performed to recalculate the particle weight, which specifically comprises the following steps:
the weight of the ith particle at the (k + 1) th moment is recalculated into
Where σ is the set variance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910770081.6A CN110487276B (en) | 2019-08-20 | 2019-08-20 | Sampling vector matching positioning method based on correlation analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910770081.6A CN110487276B (en) | 2019-08-20 | 2019-08-20 | Sampling vector matching positioning method based on correlation analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110487276A true CN110487276A (en) | 2019-11-22 |
| CN110487276B CN110487276B (en) | 2021-01-12 |
Family
ID=68552343
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910770081.6A Active CN110487276B (en) | 2019-08-20 | 2019-08-20 | Sampling vector matching positioning method based on correlation analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110487276B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112097772A (en) * | 2020-08-20 | 2020-12-18 | 深圳市优必选科技股份有限公司 | Robot and map construction method and device thereof |
| CN113503871A (en) * | 2021-05-19 | 2021-10-15 | 北京理工大学 | Gravity matching method based on correlation filtering |
| CN113587923A (en) * | 2021-05-31 | 2021-11-02 | 中国人民解放军61540部队 | Submersible vehicle positioning method and system for screening multi-dimensional gravity gradient lighthouse matching area |
| CN117747116A (en) * | 2024-02-19 | 2024-03-22 | 天津市第五中心医院 | Intelligent early warning method for physiological index of obstetrical department midwifery |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120022784A1 (en) * | 2010-06-25 | 2012-01-26 | Thales | Navigation filter for a navigation system using terrain correlation |
| CN102445201A (en) * | 2011-09-28 | 2012-05-09 | 东北林业大学 | Underwater carrier geomagnetic anomaly feature points matching navigation method |
| CN103148848A (en) * | 2011-12-07 | 2013-06-12 | 三星电子株式会社 | Mobile terminal device for positioning system based on magnetic field map and method thereof |
| CN103438879A (en) * | 2013-09-02 | 2013-12-11 | 北京航空航天大学 | Atomic spin gyroscope and magnetometer tightly-integrated attitude determination method based on ant colony PF (Particle Filter) algorithm |
| CN104215238A (en) * | 2014-08-21 | 2014-12-17 | 北京空间飞行器总体设计部 | Indoor positioning method of intelligent mobile phone |
| US20150018018A1 (en) * | 2013-07-12 | 2015-01-15 | Microsoft Corporation | Indoor Location-Finding using Magnetic Field Anomalies |
| CN105157704A (en) * | 2015-06-03 | 2015-12-16 | 北京理工大学 | Bayesian estimation-based particle filter gravity-assisted inertial navigation matching method |
| CN105737831A (en) * | 2016-01-29 | 2016-07-06 | 北京理工大学 | Variable-scale and variable-direction gravity sampling vector matching and positioning method based on particle filtering |
| CN105928541A (en) * | 2016-04-12 | 2016-09-07 | 北京理工大学 | Gravity matching method of modified correlation sequence algorithm |
| CN106017460A (en) * | 2016-05-20 | 2016-10-12 | 东南大学 | Terrain-assisted inertial-navigation tight-combination underwater vehicle navigation and positioning method |
| CN107389061A (en) * | 2017-06-22 | 2017-11-24 | 北京理工大学 | Error hiding detection method based on spatial order in a kind of Gravity Matching navigation |
| CN110031001A (en) * | 2019-05-21 | 2019-07-19 | 北京理工大学 | A kind of adaptation area choosing method of Method in Gravity Aided INS |
-
2019
- 2019-08-20 CN CN201910770081.6A patent/CN110487276B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120022784A1 (en) * | 2010-06-25 | 2012-01-26 | Thales | Navigation filter for a navigation system using terrain correlation |
| CN102445201A (en) * | 2011-09-28 | 2012-05-09 | 东北林业大学 | Underwater carrier geomagnetic anomaly feature points matching navigation method |
| CN103148848A (en) * | 2011-12-07 | 2013-06-12 | 三星电子株式会社 | Mobile terminal device for positioning system based on magnetic field map and method thereof |
| US20150018018A1 (en) * | 2013-07-12 | 2015-01-15 | Microsoft Corporation | Indoor Location-Finding using Magnetic Field Anomalies |
| CN103438879A (en) * | 2013-09-02 | 2013-12-11 | 北京航空航天大学 | Atomic spin gyroscope and magnetometer tightly-integrated attitude determination method based on ant colony PF (Particle Filter) algorithm |
| CN104215238A (en) * | 2014-08-21 | 2014-12-17 | 北京空间飞行器总体设计部 | Indoor positioning method of intelligent mobile phone |
| CN105157704A (en) * | 2015-06-03 | 2015-12-16 | 北京理工大学 | Bayesian estimation-based particle filter gravity-assisted inertial navigation matching method |
| CN105737831A (en) * | 2016-01-29 | 2016-07-06 | 北京理工大学 | Variable-scale and variable-direction gravity sampling vector matching and positioning method based on particle filtering |
| CN105928541A (en) * | 2016-04-12 | 2016-09-07 | 北京理工大学 | Gravity matching method of modified correlation sequence algorithm |
| CN106017460A (en) * | 2016-05-20 | 2016-10-12 | 东南大学 | Terrain-assisted inertial-navigation tight-combination underwater vehicle navigation and positioning method |
| CN107389061A (en) * | 2017-06-22 | 2017-11-24 | 北京理工大学 | Error hiding detection method based on spatial order in a kind of Gravity Matching navigation |
| CN110031001A (en) * | 2019-05-21 | 2019-07-19 | 北京理工大学 | A kind of adaptation area choosing method of Method in Gravity Aided INS |
Non-Patent Citations (3)
| Title |
|---|
| BO WANG 等: "A Characteristic Parameter Matching Algorithm for Gravity-Aided Navigation of Underwater Vehicles", 《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 * |
| 付梦印 等: "水下惯性/重力匹配自主导航综述", 《水下无人系统学报》 * |
| 黄玉龙 等: "自主水下航行器导航方法综述", 《水下无人系统学报》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112097772A (en) * | 2020-08-20 | 2020-12-18 | 深圳市优必选科技股份有限公司 | Robot and map construction method and device thereof |
| WO2022036981A1 (en) * | 2020-08-20 | 2022-02-24 | 深圳市优必选科技股份有限公司 | Robot, and map construction method and device thereof |
| CN112097772B (en) * | 2020-08-20 | 2022-06-28 | 深圳市优必选科技股份有限公司 | Robot and map construction method and device thereof |
| CN113503871A (en) * | 2021-05-19 | 2021-10-15 | 北京理工大学 | Gravity matching method based on correlation filtering |
| CN113503871B (en) * | 2021-05-19 | 2024-04-16 | 北京理工大学 | Gravity matching method based on correlation filtering |
| CN113587923A (en) * | 2021-05-31 | 2021-11-02 | 中国人民解放军61540部队 | Submersible vehicle positioning method and system for screening multi-dimensional gravity gradient lighthouse matching area |
| CN113587923B (en) * | 2021-05-31 | 2024-04-26 | 中国人民解放军61540部队 | Submersible positioning method and system for screening matching areas of multidimensional gravity gradient lighthouse |
| CN117747116A (en) * | 2024-02-19 | 2024-03-22 | 天津市第五中心医院 | Intelligent early warning method for physiological index of obstetrical department midwifery |
| CN117747116B (en) * | 2024-02-19 | 2024-05-17 | 天津市第五中心医院 | Intelligent early warning method for physiological index of obstetrical department midwifery |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110487276B (en) | 2021-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110487276B (en) | Sampling vector matching positioning method based on correlation analysis | |
| Wang et al. | Student’s t-based robust Kalman filter for a SINS/USBL integration navigation strategy | |
| CN101339036B (en) | Terrain auxiliary navigation method and apparatus | |
| CN111486845B (en) | AUV multi-strategy navigation method based on submarine topography matching | |
| Han et al. | An improved TERCOM-based algorithm for gravity-aided navigation | |
| Han et al. | A combined matching algorithm for underwater gravity-aided navigation | |
| CN104390646B (en) | The location matching method of underwater hiding-machine terrain aided inertial navigation system | |
| Chen et al. | A new geomagnetic matching navigation method based on multidimensional vector elements of earth’s magnetic field | |
| CN113359170A (en) | Inertial navigation-assisted Beidou single-frequency-motion opposite-motion high-precision relative positioning method | |
| CN110726415B (en) | Self-adaptive underwater multi-beam synchronous positioning and mapping method | |
| CN114562992B (en) | Multi-path environment combined navigation method based on factor graph and scene constraint | |
| Han et al. | A matching algorithm based on the nonlinear filter and similarity transformation for gravity-aided underwater navigation | |
| CN113739795A (en) | Underwater synchronous positioning and mapping method based on polarized light/inertia/vision combined navigation | |
| Wang et al. | A filtered-marine map-based matching method for gravity-aided navigation of underwater vehicles | |
| Wang et al. | Land vehicle navigation using odometry/INS/vision integrated system | |
| CN116449405A (en) | Combined navigation method based on density clustering self-adaptive filtering | |
| CN110207721B (en) | Invalid terrain matching result identification method considering residual distribution | |
| Bian et al. | Study on GPS attitude determination system aided INS using adaptive Kalman filter | |
| CN110763234A (en) | Submarine topography matching navigation path planning method for underwater robot | |
| CN113624227A (en) | Contour line iteration gravity matching algorithm based on Mahalanobis distance | |
| Yu | In-situ calibration of transceiver alignment for a high-precision USBL system | |
| Rupeng et al. | A new model and method of terrain-aided positioning confidence interval estimation | |
| Lager et al. | Underwater terrain navigation using standard sea charts and magnetic field maps | |
| CN114355409A (en) | Water surface target motion estimation method | |
| Zhang et al. | Improved salp swarm algorithm for the calibration of the underwater transponder |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |