CN118347497A - High-precision image navigation positioning method based on local neighborhood map - Google Patents
High-precision image navigation positioning method based on local neighborhood map Download PDFInfo
- Publication number
- CN118347497A CN118347497A CN202410780957.6A CN202410780957A CN118347497A CN 118347497 A CN118347497 A CN 118347497A CN 202410780957 A CN202410780957 A CN 202410780957A CN 118347497 A CN118347497 A CN 118347497A
- Authority
- CN
- China
- Prior art keywords
- positioning
- moment
- local neighborhood
- latitude
- satellite
- 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
- 238000000034 method Methods 0.000 title claims abstract description 63
- 230000004927 fusion Effects 0.000 claims description 67
- 230000008859 change Effects 0.000 claims description 61
- 238000010606 normalization Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 10
- 238000009499 grossing Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 239000006185 dispersion Substances 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000000007 visual effect Effects 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
- G01C21/165—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 combined with non-inertial navigation instruments
-
- 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
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
- Navigation (AREA)
Abstract
The application relates to the technical field of navigation positioning, in particular to a high-precision image navigation positioning method based on a local neighborhood map, which comprises the following steps: acquiring each positioning parameter of a navigation device to be positioned at each moment in a local neighborhood; constructing a distance sequence and a differential sequence of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood; determining a positioning interference factor of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood; determining a positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood; determining a positioning discrete coefficient of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood; and determining the target navigation position of the navigation device to be positioned at each moment in the local neighborhood, and guiding the navigation device to be positioned to move the target navigation position. The application can improve the navigation positioning precision of the navigation device to be positioned.
Description
Technical Field
The application relates to the technical field of navigation positioning, in particular to a high-precision image navigation positioning method based on a local neighborhood map.
Background
Along with the rapid development of satellite navigation positioning technology, the requirements on the precision of navigation positioning are higher and higher, and the method is particularly applicable to the fields of industry, logistics, military and the like. The navigation positioning technology based on the inertial sensor is a relatively mature navigation positioning technology, has an autonomous navigation function independent of an external environment, but has accumulated errors, and the greater the accumulated errors are over time, the greater the accumulated errors are, the influence on the accuracy of navigation positioning of a target object can be generated.
In order to improve the accuracy of navigation and positioning of a target object, the accurate positioning of the target object is realized by adopting a mode of combining GPS navigation and vision in the prior art, because of the complexity of urban environment, a large number of shielded buildings and interference signals exist, so that a visual image does not have certain geographic position information, or a satellite positioning system cannot work normally, and at the moment, the accuracy of navigation and positioning of the target object cannot be effectively improved by adopting the mode of combining GPS navigation and vision, and the accuracy of navigation and positioning of the target object is poor.
Disclosure of Invention
In order to solve the technical problems, a high-precision image navigation positioning method based on a local neighborhood map is provided to solve the existing problems.
The application provides a high-precision image navigation positioning method based on a local neighborhood map, which comprises the following steps:
a positioning data acquisition step of acquiring positioning parameters of a navigation device to be positioned at each moment in a local neighborhood; constructing a distance sequence and a differential sequence of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood;
A positioning interference analysis step, namely determining positioning interference factors of the positioning parameters of the navigation device to be positioned at each moment in the local neighborhood according to the distribution condition of the elements in the distance sequence of the positioning parameters and the discrete degree of the differential sequence;
determining a positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in a local neighborhood according to the trend change condition in the distance sequence of each positioning parameter and the positioning interference factor of each positioning parameter;
determining the positioning discrete coefficient of each positioning parameter of the navigation device to be positioned in the local neighborhood according to the difference condition of the positioning deviation indexes of each positioning parameter of each moment;
And a positioning navigation step, namely determining a target navigation position of the navigation device to be positioned at each moment in a local neighborhood based on positioning discrete coefficients of all positioning parameters, and guiding the navigation device to be positioned to move in the target navigation position.
Preferably, the positioning parameters at each time are a satellite longitude, a satellite latitude, an inertial longitude and an inertial latitude at each time.
Preferably, the method for constructing the distance sequence and the differential sequence of each positioning parameter at each moment comprises the following steps:
Forming a local sequence of the positioning parameters of each moment in the local neighborhood by using the positioning parameters of a plurality of moments closest to the distance between the positioning parameters of each moment;
Calculating the distance between each positioning parameter at each moment in the local neighborhood and each positioning parameter in the local sequence of the positioning parameter, and forming a distance sequence of each positioning parameter at each moment in the local neighborhood according to the arrangement sequence from large to small of all the distances;
And taking the first-order difference result of the distance sequence as a difference sequence of each positioning parameter at each moment in the local neighborhood.
Preferably, the determining the positioning interference factor of each positioning parameter of the navigation device to be positioned at each moment in the local vicinity includes:
Acquiring an upper quartile, a median and a lower quartile in a distance sequence of each positioning parameter at each moment;
Taking the difference between the median and the upper quartile as a first difference; taking the difference between the median and the lower quartile as a second difference;
Determining the average value of the first difference and the second difference as the relative variation difference of each positioning parameter at each moment in the local neighborhood;
Acquiring the discrete degree of elements in the differential sequence of each positioning parameter;
And determining a positioning interference factor of each positioning parameter at each moment in the local neighborhood based on the relative change difference and the discrete degree, wherein the positioning interference factors respectively have positive cooperative relation with the relative change difference and the discrete degree.
Preferably, the determining the positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in the local vicinity includes:
Performing curve fitting on the distance sequence of the satellite latitude at each moment in the local neighborhood, and obtaining trend change coefficients of elements in the distance sequence of each positioning parameter based on the change trend of the fitted curve;
Calculating the positioning change factor of each positioning parameter according to the difference change condition of the trend change coefficient of each element in the distance sequence of each positioning parameter;
determining the degree of confusion of trend change coefficients of all elements in the distance sequence of each positioning parameter as the degree of confusion of each positioning parameter;
The information change characteristic values of the positioning parameters at each moment in the local neighborhood respectively form positive cooperative relationship with the positioning change factors and the confusion;
And determining a positioning deviation index of each positioning parameter at each moment in the local neighborhood based on the information change characteristic value and the positioning interference factor, wherein the positioning deviation index is respectively in positive cooperative relation with the information change characteristic value and the positioning interference factor.
Preferably, the calculation method of the positioning change factor of each positioning parameter is as follows:
calculating the mean value of trend change coefficients of all elements in the distance sequence of each positioning parameter, and recording the mean value as a first mean value;
taking the difference between the trend change coefficient of each element in the distance sequence of each positioning parameter and the first mean value as the relative difference of each element;
the positioning change factor of each positioning parameter is in positive cooperative relation with the relative differences of all elements.
Preferably, the determining the positioning discrete coefficient of each positioning parameter of the navigation device to be positioned at each moment in the local vicinity includes:
Calculating the difference between the positioning deviation indexes of the positioning parameters at each time and the rest time in the local neighborhood as the positioning deviation of the positioning parameters;
And determining the positioning discrete coefficient of each positioning parameter at each moment in the local neighborhood based on the distribution confusion of all the positioning deviations.
Preferably, the determining the target navigation position of the navigation device to be positioned at each moment in the local vicinity includes:
Acquiring fusion weights of satellite latitudes and fusion weights of inertial latitudes at each moment in a local neighborhood based on positioning discrete coefficients of the satellite latitudes and the inertial latitudes in each positioning parameter;
determining a satellite positioning fusion component and an inertial positioning fusion component of each moment in the local neighborhood based on the satellite latitude and the inertial latitude of each moment in the local neighborhood and the corresponding fusion weight;
Taking the sum of the satellite positioning fusion component at each moment and the inertial positioning fusion component at the same moment as the fusion latitude of each moment in the local neighborhood;
aiming at the positioning discrete coefficient of the satellite longitude and the positioning discrete coefficient of the inertia longitude at each moment in the local adjacent domain, obtaining the fusion longitude at each moment in the local adjacent domain by adopting the same method as the fusion latitude;
And taking the fusion latitude and the fusion longitude as target navigation positions of the navigation device to be positioned at each moment in the local neighborhood.
Preferably, the method for acquiring the fusion weight of the satellite latitude and the fusion weight of the inertial latitude at each moment in the local neighborhood comprises the following steps:
Based on the positioning discrete coefficients of the satellite latitude and the inertia latitude in each positioning parameter, obtaining a positioning credibility value of the satellite latitude and the inertia latitude at each moment in a local neighborhood, wherein the positioning credibility value and the positioning discrete coefficients are in negative cooperative relation;
And respectively taking the normalization results of the positioning reliability degree values of the satellite latitude and the inertia latitude at each moment in the local neighborhood as the fusion weight of the satellite latitude and the fusion weight of the inertia latitude at each moment in the local neighborhood.
Preferably, the determining the satellite positioning fusion component and the inertial positioning fusion component at each time in the local neighborhood includes:
Taking the product of the fusion weights of the satellite latitudes at each moment in the local neighborhood and the satellite latitudes at the corresponding moment as a satellite positioning fusion component at each moment in the local neighborhood;
and taking the product of the fusion weights of the inertia latitude of each moment in the local neighborhood and the inertia latitude of the corresponding moment as an inertia positioning fusion component of each moment in the local neighborhood.
The application has at least the following beneficial effects:
The method and the device have the beneficial effects that the deviation degree in the positioning process is analyzed by the distance of the adjacent position is considered, and the accuracy of the positioning parameters is reflected; the method has the beneficial effects that the degree of influence of external interference on the positioning parameters in the positioning process is considered; calculating the positioning discrete coefficient of each positioning parameter at each moment in the local adjacent domain, which has the beneficial effects of reflecting the discrete characteristic of positioning deviation in the navigation positioning process and considering the authenticity of positioning information; the method has the advantages that compared with the prior art, the method which utilizes the fixed weight to fuse the longitude and latitude after the filtering treatment, the navigation positioning accuracy of the navigation device to be positioned is poor due to the influence of external interference in the positioning process, and the method obtains the fusion weight of the longitude and latitude through the positioning reliability degree value of the longitude and latitude data, and improves the navigation positioning accuracy of the navigation device to be positioned.
Drawings
The high-precision image navigation positioning method based on the local neighborhood map is further described in detail below with reference to the accompanying drawings.
FIG. 1 is a flow chart of steps of a high-precision image navigation positioning method based on a local neighborhood map;
FIG. 2 is a flowchart of a method for acquiring positioning interference factors of positioning parameters of a navigation device to be positioned at each moment in a local vicinity;
Fig. 3 is a flowchart of a step of a method for obtaining a positioning deviation index of each positioning parameter of a to-be-positioned navigation device in a local vicinity at each moment.
Detailed Description
In order to make the objects, technical schemes and advantages of the present application more clear, the present application provides a high-precision image navigation positioning method based on a local neighborhood map, which is further described in detail below with reference to the accompanying drawings and the implementation examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Referring to fig. 1, a flowchart of a high-precision image navigation positioning method based on a local neighborhood map according to an embodiment of the present application is shown, and the method includes the following steps:
step 1, obtaining each positioning parameter of a navigation device to be positioned at each moment in a local neighborhood.
Acquiring GPS satellite positioning positions of the navigation devices to be positioned at each moment through a GPS satellite positioning system, and acquiring the GPS satellite positioning positions of the navigation devices to be positioned at all moments in a local neighborhood with a preset radius of R by taking the GPS satellite positioning positions of the navigation devices to be positioned at each moment as the center; correspondingly, acquiring inertial positioning coordinates of the navigation device to be positioned at each moment through an inertial navigation system, and acquiring the inertial positioning positions of the navigation device to be positioned at all moments in a local neighborhood with a preset radius of R by taking the inertial positioning position of the navigation device to be positioned at each moment as a center.
Preferably, as an embodiment of the present application, the navigation device to be positioned is a target vehicle, the preset radius R is 200 meters, and as other embodiments, the embodiment may be set by an operator according to the actual situation, and the embodiment is not limited in particular.
Based on the GPS satellite positioning position and the inertial positioning coordinates, acquiring satellite positioning longitude, satellite positioning latitude, inertial positioning longitude and inertial positioning latitude of a navigation device to be positioned at each moment in the local neighborhood, and acquiring positioning parameters of each moment in the local neighborhood by adopting a noise reduction smoothing processing method, wherein the positioning parameters comprise satellite longitude, satellite latitude, inertial longitude and inertial latitude;
Preferably, as an embodiment of the present application, a kalman filter algorithm is used to perform the noise reduction smoothing processing, where the kalman filter algorithm is a known technology, and the specific process is not repeated.
It should be understood that the present embodiment provides only one data noise reduction smoothing method, i.e. kalman filtering algorithm, and as other implementation manners, the practitioner may use other methods in the prior art to perform the data smoothing, for example, a gaussian filter, a moving average filter, etc., which is not limited in particular in the present embodiment.
So far, each positioning parameter at each moment in the local neighborhood is obtained.
Step 2, constructing a distance sequence and a differential sequence of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood; and determining the positioning interference factors of the positioning parameters of the navigation device to be positioned at each moment in the local neighborhood according to the distribution condition of the elements in the distance sequence of the positioning parameters and the discrete degree of the differential sequence.
Further, in general, the GPS satellite positioning system and the inertial navigation system have advantages and disadvantages, and the satellite navigation positioning system is easy to be affected by signal propagation delay and interference signals, so that satellite signals are not firm, and the navigation positioning accuracy of the navigation device to be positioned is low; the inertial navigation system is easy to have drift errors and accumulated errors, so that the navigation positioning accuracy of the navigation device to be positioned is low. Therefore, a data fusion algorithm is needed to fuse satellite navigation data with inertial navigation data so as to improve the navigation positioning accuracy of the navigation device to be positioned.
In this embodiment, taking the satellite latitude in each positioning parameter as an example, the specific method for obtaining the positioning interference factor of the satellite latitude at each moment in the local neighborhood is as follows:
The satellite latitudes at a plurality of moments, which are closest to each other, in the local neighborhood are formed into a local sequence of the satellite latitudes at each moment in the local neighborhood;
calculating the distance between the satellite latitude at each moment in the local neighborhood and each element in the local sequence of the satellite latitude, and forming a distance sequence of the satellite latitude at each moment in the local neighborhood according to the arrangement sequence from large to small of all the distances;
Preferably, as an embodiment of the present application, the local sequence of satellite latitudes and the distance sequence of satellite latitudes are obtained by calculating euclidean distances between the satellite latitudes at all times and the satellite latitudes at each time.
It should be understood that the present embodiment provides only one method for calculating the distance between two data, i.e. euclidean distance, and as other embodiments, an implementer may use other distance measuring methods in the prior art to obtain a local sequence of satellite latitudes and a distance sequence of satellite latitudes, which is not limited in particular in this embodiment.
Taking a first-order difference result of the distance sequence of the satellite latitudes as a difference sequence of the satellite latitudes at each moment in the local neighborhood;
Acquiring an upper quartile, a median and a lower quartile in a distance sequence of the satellite latitude;
Taking the difference between the median and the upper quartile as a first difference;
Taking the difference between the median and the lower quartile as a second difference;
And determining the average value of the first difference and the second difference as the relative variation difference of each positioning parameter at each moment in the local neighborhood.
Preferably, as an embodiment of the present application, the first difference is an absolute value of a difference between the median and the upper quartile; accordingly, the second difference is the absolute value of the difference between the median and the lower quartile.
Aiming at the rest positioning parameters at each moment in the local neighborhood, the same construction method as the distance sequence and the differential sequence of the satellite latitude is adopted, and the distance sequence and the differential sequence of each positioning parameter at each moment in the local neighborhood are obtained.
Acquiring the discrete degree of elements in the differential sequence of the satellite latitude;
Determining a positioning interference factor of the satellite latitude at each moment in a local neighborhood based on the relative change difference and the discrete degree, wherein the positioning interference factor respectively has positive cooperative relation with the relative change difference and the discrete degree;
It will be appreciated that a positive synergistic relationship indicates that the dependent variable increases with increasing independent variable and decreases with decreasing independent variable, and that the specific relationship may be a multiplication relationship, an addition relationship, an idempotent of an exponential function, as determined by the actual application, and the application is not particularly limited in this regard.
Preferably, as one embodiment of the present application, the degree of dispersion of the satellite latitude at each time in the local neighborhood is the standard deviation of all elements in the differential sequence of the satellite latitude; and secondly, taking the product of the relative change difference and the discrete degree as a positioning interference factor of the satellite latitude at each moment in the local neighborhood.
It should be noted that, the distance sequence of the satellite latitude reflects the distance change characteristics between adjacent positions in the local sequence of the satellite latitude, the distance change between the adjacent positions has higher similarity under normal conditions, the larger the external interference is, the lower the accuracy of positioning information is, and the lower the similarity of the distance change is; the difference sequence of the satellite latitude measures the similarity characteristic of the distance change, if the difference sequence is subjected to larger external interference, the accuracy of positioning information is lower, and the discreteness of the similarity characteristic of the distance change in the first-order difference sequence is stronger, the influence of the external interference is larger in the positioning process, and the measured positioning interference factor is larger; the positioning interference factor reflects the influence of external interference on the positioning result in the positioning process, namely, the larger the positioning interference factor is, the larger the influence of external interference on the positioning result in the positioning process is, and the worse the real result of the corresponding latitude data is.
And aiming at the satellite longitude, the inertial longitude and the inertial latitude of each moment in the local adjacent domain, obtaining the positioning interference factor of the satellite longitude, the positioning interference factor of the inertial longitude and the positioning interference factor of the inertial latitude of each moment in the local adjacent domain by adopting the same method as the positioning interference factor of the satellite latitude.
Further, a flowchart of steps of a method for acquiring a positioning interference factor of each positioning parameter of a navigation device to be positioned in each time in a local vicinity is shown in fig. 2.
So far, the positioning interference factors of the positioning parameters at each moment in the local neighborhood are obtained.
And step 3, determining the positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood according to the trend change condition in the distance sequence of each positioning parameter and the positioning interference factor of each positioning parameter.
Further, the greater the influence of external interference on the longitude and latitude of satellite positioning and inertial positioning, the greater the possibility of deviation of positioning at the moment, and the greater the phenomenon of positioning deviation in the positioning process is illustrated when the larger longitude and latitude information change occurs because the longitude and latitude of the satellite positioning and the inertial positioning are the position information of a local area theoretically.
In this embodiment, taking the satellite latitude in each positioning parameter as an example, the positioning deviation index of the satellite latitude at each moment in the local neighborhood is obtained, and the specific method is as follows:
performing curve fitting on the satellite latitude distance sequence at each moment in the local neighborhood, and obtaining trend change coefficients of elements in the satellite latitude distance sequence based on the change trend of the fitted curve;
preferably, as an embodiment of the present application, a least square method is adopted to perform curve fitting on the distance sequence, and a curve slope of each moment is calculated as a trend change coefficient of each element.
It should be understood that the present embodiment provides only one curve fitting method, i.e. the least square method, and as other implementation manners, the practitioner may use other methods in the prior art to perform curve fitting, for example, maximum likelihood estimation, etc., which is not limited in particular by the present embodiment.
Calculating the mean value of trend change coefficients of all elements in the distance sequence of the satellite latitude, and recording the mean value as a first mean value;
taking the difference between the trend change coefficient of each element in the distance sequence of the satellite latitude and the first mean value as the relative difference of each element;
the positioning change factor of the satellite latitude and the relative difference of all elements are in positive cooperative relation;
Preferably, as an embodiment of the present application, an absolute value of a difference between a trend change coefficient of each element in the range sequence of the satellite latitude and the first mean value is used as a relative difference of each element; second, the positioning change factor of the satellite latitude is the mean of the relative differences of all elements.
Determining the degree of confusion of trend change coefficients of all elements in the distance sequence of the satellite latitude as the degree of confusion of the satellite latitude;
And the information change characteristic value of the satellite latitude at each moment in the local neighborhood is respectively in positive cooperative relation with the positioning change factor and the confusion.
Preferably, as one embodiment of the present application, the information entropy of the trend change coefficient of all elements in the distance sequence of the satellite latitude is used as the confusion of the satellite latitude; and secondly, taking the product of the positioning change factor and the confusion degree as an information change characteristic value of the satellite latitude at each moment in the local neighborhood.
It can be understood that the degree of confusion of the trend change coefficients of all elements in the distance sequence of the satellite latitude can be reflected by information entropy, variance and standard deviation, and as other embodiments, a practitioner can obtain the degree of confusion by adopting other methods in the prior art, and the embodiment is not limited in particular.
Determining a positioning deviation index of the satellite latitude at each moment in a local neighborhood based on the information change characteristic value of the satellite latitude and a positioning interference factor of the satellite latitude, wherein the positioning deviation index is respectively in positive cooperative relation with the information change characteristic value and the positioning interference factor;
Preferably, as an embodiment of the present application, the positioning deviation index of the satellite latitude is a product of an information variation characteristic value of the satellite latitude and a positioning interference factor of the satellite latitude.
It should be noted that, the positioning deviation index of the latitude of the satellite reflects the degree of deviation of positioning caused by the influence of the interference signal during satellite positioning, that is, the greater the positioning deviation index of the latitude of the satellite, the greater the degree of deviation of positioning caused by the influence of external interference during satellite positioning.
And calculating the positioning deviation index of the satellite longitude, the positioning deviation index of the inertia longitude and the positioning deviation index of the inertia latitude at each moment in the local neighborhood by adopting the same method as the positioning deviation index of the satellite latitude.
Further, a flowchart of the steps of the method for obtaining the positioning deviation index of each positioning parameter of the navigation device to be positioned in each time in the local vicinity is shown in fig. 3.
So far, the positioning deviation index of each positioning parameter at each moment in the local neighborhood is obtained.
And 4, determining the positioning discrete coefficient of each positioning parameter of the navigation device to be positioned in the local neighborhood according to the difference condition of the positioning deviation indexes of each positioning parameter at each moment.
Further, the positioning deviation index of the satellite latitude reflects the degree of deviation of positioning caused by the influence of interference signals during satellite positioning, and when the degree of dispersion of the deviation between the positioning deviation indexes of the satellite latitude is larger, the positioning reliability degree of the description data is smaller, and at the moment, smaller fusion weight is given; on the contrary, when the degree of dispersion of deviation between positioning deviation indexes of satellite latitudes is smaller, the degree of positioning reliability of the description data is larger, and at the moment, larger fusion weight is given.
In this embodiment, taking the satellite latitude in each positioning parameter as an example, the specific method for obtaining the positioning discrete coefficient of the satellite latitude at each moment in the local neighborhood includes:
calculating the difference between the positioning deviation indexes of the satellite latitudes at each moment and the rest moments in the local neighborhood, and taking the difference as the positioning deviation of the satellite latitudes;
determining a positioning discrete coefficient of the satellite latitude at each moment in the local neighborhood based on the distribution chaos of the positioning deviation of all the satellite latitudes;
Preferably, as one embodiment of the present application, a difference between the positioning deviation indexes of the satellite latitudes at each time and the rest of time in the local neighborhood is calculated as the positioning deviation of the satellite latitudes; and secondly, the positioning discrete coefficient of the satellite latitude at each moment in the local neighborhood is the standard deviation of the positioning deviation of all the satellite latitudes.
It should be noted that, the positioning discrete coefficient of the satellite latitude reflects the discrete feature of the positioning deviation in the navigation positioning process, the stronger the discrete feature of the positioning deviation is, the worse the authenticity of the positioning information is, the smaller the fusion weight is given, that is, the larger the positioning discrete coefficient of the satellite latitude is, the stronger the discrete feature of the positioning deviation is, and the worse the authenticity of the positioning information is.
And obtaining the positioning discrete coefficient of the satellite longitude, the positioning discrete coefficient of the inertia longitude and the positioning discrete coefficient of the inertia latitude at each moment in the local neighborhood by adopting the same method as the positioning discrete coefficient of the satellite latitude.
So far, the positioning discrete coefficient of each positioning parameter at each moment in the local neighborhood is obtained.
And step 5, determining a target navigation position of the navigation device to be positioned at each moment in the local neighborhood based on the positioning discrete coefficients of the positioning parameters, and guiding the navigation device to be positioned to move at the target navigation position.
Further, the greater the positioning discrete coefficient of the satellite latitude, the worse the authenticity of the positioning information, and the smaller the fusion weight to be given. And calculating confidence fusion weights of each datum in each satellite positioning latitude smoothing sequence by utilizing the positioning discrete coefficients of the satellite latitudes, and carrying out data fusion on the satellite latitudes and the inertia latitudes at each moment by utilizing the fusion weights to obtain fusion latitudes.
The positioning reliability degree value of the satellite latitude at each moment in the local neighborhood and the positioning discrete coefficient of the satellite latitude are in a negative cooperative relationship;
Correspondingly, the positioning reliability degree value of the inertia latitude at each moment in the local neighborhood and the positioning discrete coefficient of the inertia latitude form a negative cooperative relationship;
it will be appreciated that the negative synergistic relationship indicates that the dependent variable decreases with increasing independent variable and increases with decreasing independent variable, and the specific relationship may be a subtractive relationship, a division relationship, a negative exponential idempotent, etc., which is determined by the practical application, and the present application is not limited in this regard.
Preferably, as an embodiment of the present application, the positioning reliability value of the satellite latitude at each time in the local vicinity is the inverse of the positioning discrete coefficient of the satellite latitude, and correspondingly, the positioning reliability value of the inertial latitude at each time in the local vicinity is the inverse of the positioning discrete coefficient of the inertial latitude.
The larger the positioning discrete coefficient of the satellite latitude or the positioning discrete coefficient of the inertial latitude is, the stronger the discrete feature of the positioning deviation is, and the worse the authenticity of the positioning information is, the smaller the positioning reliability value of the satellite latitude or the inertial latitude is.
The method comprises the steps of respectively carrying out normalization processing on the positioning reliability degree value of the satellite latitude and the positioning reliability degree value of the inertia latitude in all moments in the local neighborhood, and respectively taking a normalization result of the positioning reliability degree value of the satellite latitude and a normalization result of the positioning reliability degree value of the inertia latitude in each moment in the local neighborhood as a fusion weight of the satellite latitude and a fusion weight of the inertia latitude in each moment in the local neighborhood;
Taking the product of the fusion weights of the satellite latitudes at each moment in the local neighborhood and the satellite latitudes at the corresponding moment as a satellite positioning fusion component at each moment in the local neighborhood;
Taking the product of the fusion weights of the inertia latitude of each moment in the local neighborhood and the inertia latitude of the corresponding moment as an inertia positioning fusion component of each moment in the local neighborhood;
Taking the sum of the satellite positioning fusion component at each moment and the inertial positioning fusion component at the same moment as the fusion latitude of each moment in the local neighborhood;
aiming at the positioning discrete coefficient of the satellite longitude and the positioning discrete coefficient of the inertia longitude at each moment in the local adjacent domain, obtaining the fusion longitude at each moment in the local adjacent domain by adopting the same method as the fusion latitude;
and taking the fusion latitude and the fusion longitude as target navigation positions of the navigation device to be positioned at each moment in the local neighborhood, and conducting heading guiding on the navigation device to be positioned, wherein the target navigation positions move.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the application. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the spirit of the present application, and therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present application fall within the protection scope of the technical solution of the present application.
Claims (10)
1. The high-precision image navigation positioning method based on the local neighborhood map is characterized by comprising the following steps of:
a positioning data acquisition step of acquiring positioning parameters of a navigation device to be positioned at each moment in a local neighborhood; constructing a distance sequence and a differential sequence of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood;
A positioning interference analysis step, namely determining positioning interference factors of the positioning parameters of the navigation device to be positioned at each moment in the local neighborhood according to the distribution condition of the elements in the distance sequence of the positioning parameters and the discrete degree of the differential sequence;
determining a positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in a local neighborhood according to the trend change condition in the distance sequence of each positioning parameter and the positioning interference factor of each positioning parameter;
determining the positioning discrete coefficient of each positioning parameter of the navigation device to be positioned in the local neighborhood according to the difference condition of the positioning deviation indexes of each positioning parameter of each moment;
And a positioning navigation step, namely determining a target navigation position of the navigation device to be positioned at each moment in a local neighborhood based on positioning discrete coefficients of all positioning parameters, and guiding the navigation device to be positioned to move in the target navigation position.
2. The method for high-precision image navigation and positioning based on local neighborhood map as claimed in claim 1, wherein each positioning parameter of each moment is satellite longitude, satellite latitude, inertial longitude and inertial latitude of each moment.
3. The high-precision image navigation positioning method based on the local neighborhood map as set forth in claim 1, wherein the construction method of the distance sequence and the differential sequence of each positioning parameter at each moment is as follows:
Forming a local sequence of the positioning parameters of each moment in the local neighborhood by using the positioning parameters of a plurality of moments closest to the distance between the positioning parameters of each moment;
Calculating the distance between each positioning parameter at each moment in the local neighborhood and each positioning parameter in the local sequence of the positioning parameter, and forming a distance sequence of each positioning parameter at each moment in the local neighborhood according to the arrangement sequence from large to small of all the distances;
And taking the first-order difference result of the distance sequence as a difference sequence of each positioning parameter at each moment in the local neighborhood.
4. The method for high-precision image navigation positioning based on local neighborhood map according to claim 1, wherein determining the positioning interference factor of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood comprises:
Acquiring an upper quartile, a median and a lower quartile in a distance sequence of each positioning parameter at each moment;
Taking the difference between the median and the upper quartile as a first difference; taking the difference between the median and the lower quartile as a second difference;
Determining the average value of the first difference and the second difference as the relative variation difference of each positioning parameter at each moment in the local neighborhood;
Acquiring the discrete degree of elements in the differential sequence of each positioning parameter;
And determining a positioning interference factor of each positioning parameter at each moment in the local neighborhood based on the relative change difference and the discrete degree, wherein the positioning interference factors respectively have positive cooperative relation with the relative change difference and the discrete degree.
5. The method for high-precision image navigation positioning based on local neighborhood map according to claim 1, wherein determining the positioning deviation index of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood comprises:
Performing curve fitting on the distance sequence of the satellite latitude at each moment in the local neighborhood, and obtaining trend change coefficients of elements in the distance sequence of each positioning parameter based on the change trend of the fitted curve;
Calculating the positioning change factor of each positioning parameter according to the difference change condition of the trend change coefficient of each element in the distance sequence of each positioning parameter;
determining the degree of confusion of trend change coefficients of all elements in the distance sequence of each positioning parameter as the degree of confusion of each positioning parameter;
The information change characteristic values of the positioning parameters at each moment in the local neighborhood respectively form positive cooperative relationship with the positioning change factors and the confusion;
And determining a positioning deviation index of each positioning parameter at each moment in the local neighborhood based on the information change characteristic value and the positioning interference factor, wherein the positioning deviation index is respectively in positive cooperative relation with the information change characteristic value and the positioning interference factor.
6. The high-precision image navigation positioning method based on the local neighborhood map as set forth in claim 5, wherein the calculation method of the positioning change factor of each positioning parameter is as follows:
calculating the mean value of trend change coefficients of all elements in the distance sequence of each positioning parameter, and recording the mean value as a first mean value;
taking the difference between the trend change coefficient of each element in the distance sequence of each positioning parameter and the first mean value as the relative difference of each element;
the positioning change factor of each positioning parameter is in positive cooperative relation with the relative differences of all elements.
7. The high-precision image navigation positioning method based on the local neighborhood map according to claim 1, wherein determining the positioning discrete coefficient of each positioning parameter of the navigation device to be positioned at each moment in the local neighborhood comprises:
Calculating the difference between the positioning deviation indexes of the positioning parameters at each time and the rest time in the local neighborhood as the positioning deviation of the positioning parameters;
And determining the positioning discrete coefficient of each positioning parameter at each moment in the local neighborhood based on the distribution confusion of all the positioning deviations.
8. The method for high-precision image navigation positioning based on local neighborhood map according to claim 2, wherein determining the target navigation position of the navigation device to be positioned at each moment in the local neighborhood comprises:
Acquiring fusion weights of satellite latitudes and fusion weights of inertial latitudes at each moment in a local neighborhood based on positioning discrete coefficients of the satellite latitudes and the inertial latitudes in each positioning parameter;
determining a satellite positioning fusion component and an inertial positioning fusion component of each moment in the local neighborhood based on the satellite latitude and the inertial latitude of each moment in the local neighborhood and the corresponding fusion weight;
Taking the sum of the satellite positioning fusion component at each moment and the inertial positioning fusion component at the same moment as the fusion latitude of each moment in the local neighborhood;
aiming at the positioning discrete coefficient of the satellite longitude and the positioning discrete coefficient of the inertia longitude at each moment in the local adjacent domain, obtaining the fusion longitude at each moment in the local adjacent domain by adopting the same method as the fusion latitude;
And taking the fusion latitude and the fusion longitude as target navigation positions of the navigation device to be positioned at each moment in the local neighborhood.
9. The high-precision image navigation positioning method based on the local neighborhood map as claimed in claim 8, wherein the method for acquiring the fusion weight of the satellite latitude and the fusion weight of the inertial latitude at each moment in the local neighborhood is as follows:
Based on the positioning discrete coefficients of the satellite latitude and the inertia latitude in each positioning parameter, obtaining a positioning credibility value of the satellite latitude and the inertia latitude at each moment in a local neighborhood, wherein the positioning credibility value and the positioning discrete coefficients are in negative cooperative relation;
And respectively taking the normalization results of the positioning reliability degree values of the satellite latitude and the inertia latitude at each moment in the local neighborhood as the fusion weight of the satellite latitude and the fusion weight of the inertia latitude at each moment in the local neighborhood.
10. The method for high-precision image navigation positioning based on local neighborhood map according to claim 8, wherein determining the satellite positioning fusion component and the inertial positioning fusion component at each moment in the local neighborhood comprises:
Taking the product of the fusion weights of the satellite latitudes at each moment in the local neighborhood and the satellite latitudes at the corresponding moment as a satellite positioning fusion component at each moment in the local neighborhood;
and taking the product of the fusion weights of the inertia latitude of each moment in the local neighborhood and the inertia latitude of the corresponding moment as an inertia positioning fusion component of each moment in the local neighborhood.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410780957.6A CN118347497B (en) | 2024-06-18 | 2024-06-18 | High-precision image navigation positioning method based on local neighborhood map |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410780957.6A CN118347497B (en) | 2024-06-18 | 2024-06-18 | High-precision image navigation positioning method based on local neighborhood map |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118347497A true CN118347497A (en) | 2024-07-16 |
| CN118347497B CN118347497B (en) | 2024-09-03 |
Family
ID=91812454
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410780957.6A Active CN118347497B (en) | 2024-06-18 | 2024-06-18 | High-precision image navigation positioning method based on local neighborhood map |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118347497B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2511000A1 (en) * | 2003-01-21 | 2004-07-21 | Novatel Inc. | Inertial gps navigation system with modified kalman filter |
| CA2802445A1 (en) * | 2010-06-25 | 2011-12-29 | Trusted Positioning Inc. | Moving platform ins range corrector (mpirc) |
| CN102410837A (en) * | 2011-07-29 | 2012-04-11 | 江苏大学 | Combined locating navigation system and method for vehicles |
| US20150073707A1 (en) * | 2013-09-09 | 2015-03-12 | Honeywell International Inc. | Systems and methods for comparing range data with evidence grids |
| CN105928515A (en) * | 2016-04-19 | 2016-09-07 | 成都翼比特自动化设备有限公司 | Navigation system for unmanned plane |
| CN109946731A (en) * | 2019-03-06 | 2019-06-28 | 东南大学 | A kind of highly reliable fusion and positioning method of vehicle based on fuzzy self-adaption Unscented kalman filtering |
| CN110645980A (en) * | 2019-09-27 | 2020-01-03 | 成都市灵奇空间软件有限公司 | Indoor and outdoor integrated positioning and navigation method and system thereof |
| CN113029137A (en) * | 2021-04-01 | 2021-06-25 | 清华大学 | Multi-source information self-adaptive fusion positioning method and system |
| CN115096295A (en) * | 2022-05-24 | 2022-09-23 | 中国人民解放军空军工程大学 | Combined navigation method based on single base station space difference |
| US20240061130A1 (en) * | 2022-08-16 | 2024-02-22 | East China Jiaotong University | High-precision positioning method and system for high-speed train |
-
2024
- 2024-06-18 CN CN202410780957.6A patent/CN118347497B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2511000A1 (en) * | 2003-01-21 | 2004-07-21 | Novatel Inc. | Inertial gps navigation system with modified kalman filter |
| CA2802445A1 (en) * | 2010-06-25 | 2011-12-29 | Trusted Positioning Inc. | Moving platform ins range corrector (mpirc) |
| CN102410837A (en) * | 2011-07-29 | 2012-04-11 | 江苏大学 | Combined locating navigation system and method for vehicles |
| US20150073707A1 (en) * | 2013-09-09 | 2015-03-12 | Honeywell International Inc. | Systems and methods for comparing range data with evidence grids |
| CN105928515A (en) * | 2016-04-19 | 2016-09-07 | 成都翼比特自动化设备有限公司 | Navigation system for unmanned plane |
| CN109946731A (en) * | 2019-03-06 | 2019-06-28 | 东南大学 | A kind of highly reliable fusion and positioning method of vehicle based on fuzzy self-adaption Unscented kalman filtering |
| CN110645980A (en) * | 2019-09-27 | 2020-01-03 | 成都市灵奇空间软件有限公司 | Indoor and outdoor integrated positioning and navigation method and system thereof |
| CN113029137A (en) * | 2021-04-01 | 2021-06-25 | 清华大学 | Multi-source information self-adaptive fusion positioning method and system |
| CN115096295A (en) * | 2022-05-24 | 2022-09-23 | 中国人民解放军空军工程大学 | Combined navigation method based on single base station space difference |
| US20240061130A1 (en) * | 2022-08-16 | 2024-02-22 | East China Jiaotong University | High-precision positioning method and system for high-speed train |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118347497B (en) | 2024-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7522090B2 (en) | Systems and methods for a terrain contour matching navigation system | |
| US12005907B2 (en) | Method for determining position data and/or motion data of a vehicle | |
| CN109884586A (en) | Unmanned plane localization method, device, system and storage medium based on ultra-wide band | |
| KR20020050224A (en) | Registration method for multiple sensor radar | |
| RU2630783C2 (en) | Method and system for determining the ionosphere travel time estimation error | |
| CN110906953A (en) | Relative position precision evaluation method and device for automatic driving positioning | |
| CN114488230B (en) | Doppler positioning method and device, electronic equipment and storage medium | |
| US20150219459A1 (en) | Method for determining future position boundary for a moving object from location estimates | |
| CN112526470A (en) | Method and device for calibrating radar parameters, electronic equipment and storage medium | |
| CN112229406A (en) | Redundancy guide full-automatic landing information fusion method and system | |
| CN112070130A (en) | Cross-road vehicle matching method and system based on dynamic time warping | |
| CN104101861B (en) | Distance-measuring and positioning method and system | |
| CN117367419A (en) | Robot positioning method, apparatus and computer readable storage medium | |
| CN118347497B (en) | High-precision image navigation positioning method based on local neighborhood map | |
| CN113935402A (en) | Training method and device for time difference positioning model and electronic equipment | |
| CN111474516B (en) | Multi-level indoor positioning method and system based on crowdsourcing sample surface fitting | |
| CN109085624A (en) | Indoor and outdoor localization method, device and computer equipment based on positioning signal strength | |
| CN104407366A (en) | Pseudo-range smooth processing method | |
| JP2019138862A (en) | Radar signal processor | |
| CN116106869A (en) | Positioning evaluation method and device for automatic driving vehicle and electronic equipment | |
| CN109655057A (en) | A kind of six push away the filtering optimization method and its system of unmanned plane accelerator measured value | |
| CN112147614B (en) | Method and system for mapping physical environments using an occupancy grid | |
| CN113673105A (en) | Design method of true value comparison strategy | |
| CN112781591A (en) | Robot positioning method and device, computer readable storage medium and robot | |
| Kim et al. | Relative azimuth estimation algorithm using rotational displacement |
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 |