CN110031876A - A kind of vehicle mounted guidance tracing point offset antidote based on Kalman filtering - Google Patents
A kind of vehicle mounted guidance tracing point offset antidote based on Kalman filtering Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- 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
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Abstract
The invention discloses a kind of, and the vehicle mounted guidance tracing point based on Kalman filtering deviates antidote, method includes the following steps: step 1: carrying out denoising to the tracing point in navigation path；Step 2: completion processing is carried out to the missing point in navigation path；Step 3: using Kalman filtering to the navigation path point after denoising and completion, carrying out offset correction；Use Kalman filtering algorithm, the offset point of Gaussian distributed is remedied to well in reasonable range, by the offset point correction to historical navigation track, so that the navigation path of user's positioning device passback improves the availability of historical navigation track closer to true wheelpath.
Description
Technical field
The invention belongs to path navigation planning field, in particular to a kind of vehicle mounted guidance tracing point based on Kalman filtering
Deviate antidote.
Background technique
Satellite navigation is can to provide longitude, latitude, sea in the world using a kind of very universal technology now
Navigation informations, the guidance drivers that can be convenient by satellite navigation such as height, speed, course, time is dialled to arrive at the destination.It leads
The core technology of boat is location technology, obtains data today mainly by GPS system or dipper system.Guidance path is advised
It draws, needs to study the historical track of user's navigation and suggested with providing the user with preferably trip.In the historical track of user's navigation
In point, because of atmosphere ionosphere delay effect, Satellite clock errors are more caused by satellite orbital error and architecture ensemble
Diameter effect will cause GPS positioning and be not allowed, and the distance that will lead to the tracing point deviation physical location of some users' navigation is distant, right
In the point of these offsets, needs to correct its position, historical navigation track could be made to be convenient for closer to the wheelpath of user
The historical track feature of user is conducted further research.Currently, having both at home and abroad largely about fixed in real time during the navigation process
The research of position offset correction, to correct real-time navigation scheme in time, but only a few concern historical navigation tracing point is inclined
Move correction.There can be some tracing points due to the limitation of user's positioning device for the historical track of user's navigation and deviate from reality
Border position.It is distant if there is the position deviation physical location of some points in the research for user's history track characteristic,
It will be unable to accurately know user's driving trace, to influence whether the research for navigation path feature.
Summary of the invention
The present invention provides a kind of vehicle mounted guidance tracing point offset antidote based on Kalman filtering, by using karr
The offset point of Gaussian distributed is remedied in reasonable range by graceful filtering algorithm well, meanwhile, it will not for normal point
Cause to deviate at a distance very much, reduces user's navigation history track well, provided for the research based on navigation path feature
Very big help.
A kind of vehicle mounted guidance tracing point offset antidote based on Kalman filtering, comprising the following steps:
Step 1: denoising is carried out to the tracing point in navigation path；
Step 2: completion processing is carried out to the missing point in navigation path；
Step 3: using Kalman filtering to the navigation path point after denoising and completion, carrying out offset correction；
Step 3.1: obtaining the optimal estimation position of the tracing point of preceding state, predicted using navigation system process model
The position of the tracing point of current state, and navigation system is obtained in the covariance of the tracing point predicted position of current state；
Step 3.2: using navigation system in the covariance of the tracing point predicted position of current state, calculating navigation system and exist
The kalman gain of the tracing point predicted position of current state；
Step 3.3: based on navigation system in current trace points predicted position and its kalman gain and current state
The position measurements of tracing point obtain the optimal estimation position of the tracing point of current state using Kalman filtering optimal estimation,
Position after using the optimal estimation position of the tracing point of current state as correction；
Wherein, in the initial state, the optimal estimation positional value of first tracing point of navigation path is the to navigation system
The position measurements of one tracing point；In the initial state, the covariance of first tracing point of navigation path takes navigation system
Value is 1.
Further, the position of the tracing point using navigation system process model prediction current state is according to following public affairs
Formula, which calculate, obtains predicted value；
X (k | k-1)=AX (k-1 | k-1)
Wherein, X (k | k-1) is indicated with the optimal estimation position X of the tracing point of preceding state k-1 (k-1 | k-1) prediction
The position prediction value of the tracing point of obtained current state k, A indicate navigation system gain, value 1；
The covariance of tracing point predicted position of the acquisition navigation system in current state, is obtained according to following formula calculating
:
P (k | k-1)=AP (k-1 | k-1) A'+Q
Wherein, P (k | k-1) and P (k-1 | k-1) respectively indicate the corresponding covariance of X (k | k-1) and X (k-1 | k-1), when
When k-1=0, P (k-1 | k-1) indicates initial point covariance, i.e. the initial covariance P (0 | 0)=1 of navigation system；Work as k-1 > 0, P
(k-1 | k-1)=(I-Kg (k-1) H) P (k-1 | k-2)；A'=A=1, Q indicate the process covariance of navigation system, value 1
×10-5, I is unit matrix.
Further, described to use Kalman filtering optimal estimation, obtain the optimal estimation position of the tracing point of current state
It sets, is calculated using the following equation acquisition:
X (k | k)=X (k | k-1)+Kg (k) (Z (k)-HX (k | k-1))
Kg (k)=P (k | k-1) H'/(HP (k | k-1) H'+R)
Wherein, Kg (k) indicates that kalman gain, Z (k) indicate that the measured value of the tracing point in state k, H indicate navigation system
Gain of the system state for navigation system in measured value, value 1.
Further, the process that the tracing point in navigation path carries out denoising is as follows:
Step 1.1: speed sectors division is carried out to the tracing point in navigation path；
To in navigation path, section where belonging to the continuous path point of identical speed sectors carries out speed sectors division,
5 continuous path points are included at least in each speed sectors；
Tracing point speed sectors include low speed section [0~10) m/s, middling speed section [20~30) m/s, high-velocity section [0~
10m/s；
Step 1.2: calculating the distance between adjacent track point in each speed sectors, judge adjacent in each speed sectors
Whether the distance between tracing point is more than preset normal maximum distance, if being more than, then it is assumed that second in adjacent track point
Tracing point is noise spot, by noise point deletion；
There is no tracing point missing:
Low speed section, it is 20m that the sampling time, which is separated by the distance of the normal maximum between two points of 1s,；
Middling speed section, sampling time are separated by the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
3 times；
High-velocity section, sampling time are separated by the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
2 times；
There are tracing point missing:
With the sampling time difference of two neighboring point multiplied by place speed sectors there is no tracing point missing in the case where phase
The product that the maximum distance answered obtains, as the preset normal maximum distance between adjacent two o'clock.
Further, the process that the missing point in navigation path carries out completion processing is as follows:
Step 2.1: tracing point lacks degree detecting；
The timestamp information of each point is successively traversed since navigation path starting point, if when the sampling of the latter tracing point
Carving the sampling instant with previous tracing point is more than sampling time interval, then there is missing point, is counted behind the latter tracing point
Continuous 30 tracing points in missing time, if missing time is more than 10s, continuous 30 started with the latter tracing point
Point deletes the orbit segment, otherwise, continually looks for next missing point to lack serious track；
Step 2.2: the navigation path point based on piecewise linear interpolation lacks completion；
The timestamp information that each point is successively traversed since navigation path starting point finds all positions for having missing,
A certain deletion sites section is denoted as [xi,xi+1]；
In deletion sites section [xi,xi+1] former and later two tracing points sampling time difference be n (s), wherein 0 < n≤10 and n
For integer；
By deletion sites section [xi,xi+1] n equal part is carried out, completion point position abscissa distinguishes the 1/ of deletion sites section
The position of n, 2/n, 3/n ..., (n-1)/n Along ent successively substitute into following once linear equation and calculate ordinate；
Wherein, (x, y) indicates the coordinate of completion point, (xi,yi) and (xi+1,yi+1) respectively indicate before deletion sites take a little
The coordinate of latter two tracing point.
Beneficial effect
The present invention provides a kind of, and the vehicle mounted guidance tracing point based on Kalman filtering deviates antidote, uses Kalman
The offset point of Gaussian distributed is remedied in reasonable range by filtering algorithm well, by historical navigation track
Offset point correction, so that the navigation path of user's positioning device passback improves history and lead closer to true wheelpath
The availability of boat track.
In terms of existing technologies, have the advantages that the following:
1. the present invention uses Kalman filtering, using simple recursive algorithm, required data storage capacity is smaller, convenient for using
Computer is handled in real time；
2. the program can make navigation offset point be corrected, while normal point will not be made to cause to deviate at a distance, very well
Ground reduces user's driving trace；
3. the present invention is corrected by the offset to navigation path point, so that the feature general character of user's history track highlights
Come, so that user's history navigation path has higher availability.
Detailed description of the invention
Fig. 1 is the flow diagram of the vehicle mounted guidance course deviation correction based on Kalman filtering；
Fig. 2 is navigation path by velocity partition section schematic diagram；
Fig. 3 is the noise spot schematic diagram in corresponding speed section；
Fig. 4 is the linear interpolation completion schematic diagram of navigation path point missing；
Fig. 5 is longitude and latitude change curve before and after Kalman filtering；
Fig. 6 is Local Navigation track schematic diagram before Kalman filtering；
Fig. 7 is Local Navigation track schematic diagram after Kalman filtering.
Specific embodiment
A kind of vehicle mounted guidance tracing point offset antidote based on Kalman filtering, detailed process are as shown in Figure 1.Mainly
The following steps are included:
Step 1: denoising is carried out to the tracing point in navigation path；
The process that the tracing point in navigation path carries out denoising is as follows:
Step 1.1: speed sectors division is carried out to the tracing point in navigation path；
To in navigation path, section where belonging to the continuous path point of identical speed sectors carries out speed sectors division,
5 continuous path points are included at least in each speed sectors；
Tracing point speed sectors include low speed section [0~10) m/s, middling speed section [20~30) m/s, high-velocity section [0~
10m/s；
The velocity partition section effect finally obtained is as shown in Figure 2.
Step 1.2: calculating the distance between adjacent track point in each speed sectors, judge adjacent in each speed sectors
Whether the distance between tracing point is more than preset normal maximum distance, if being more than, then it is assumed that second in adjacent track point
Tracing point is noise spot, by noise point deletion；
There is no tracing point missing:
Low speed section, it is 20m that the sampling time, which is separated by the distance of the normal maximum between two points of 1s,；
Middling speed section, sampling time are separated by the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
3 times；
High-velocity section, sampling time are separated by the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
2 times；
There are tracing point missing:
With the sampling time difference of two neighboring point multiplied by place speed sectors there is no tracing point missing in the case where phase
The product that the maximum distance answered obtains, as the preset normal maximum distance between adjacent two o'clock.Noise in navigation path
Point is as shown in Figure 3.
Step 2: completion processing is carried out to the missing point in navigation path；
The process that the missing point in navigation path carries out completion processing is as follows:
Step 2.1: tracing point lacks degree detecting；
The timestamp information of each point is successively traversed since navigation path starting point, if when the sampling of the latter tracing point
Carving the sampling instant with previous tracing point is more than sampling time interval, then there is missing point, is counted behind the latter tracing point
Continuous 30 tracing points in missing time, if missing time is more than 10s, continuous 30 started with the latter tracing point
Point deletes the orbit segment, otherwise, continually looks for next missing point to lack serious track；
The timestamp information that each point is successively traversed since navigation path starting point has missing from finding first
The timestamp of point, such as point P76 is 1512094831s, and the timestamp of next point P77 is 1512094834s in track, and centre lacks
Lose the corresponding point of timestamp 1512094832s and 1512094833s, it is believed that the missing time between the two points is 2s.From this
Point traverses 30 points backward, finds the total time of all missings in this 30 points, if total missing time is more than 10s, then it is assumed that
Track missing is serious, (probability that the situation occurs in practice is no more than 2%) not can be used, if total missing of this 30 points
No more than 10s, then next point for having missing is begun look for, is repeated the above steps, until the 30th point reciprocal, this process
In do not find missing serious conditions, then it is assumed that the track is available.
Step 2.2: the navigation path point based on piecewise linear interpolation lacks completion；
The timestamp information that each point is successively traversed since navigation path starting point finds all positions for having missing,
A certain deletion sites section is denoted as [xi,xi+1]；
In deletion sites section [xi,xi+1] former and later two tracing points sampling time difference be n (s), wherein 0 < n≤10 and n
For integer；
By deletion sites section [xi,xi+1] n equal part is carried out, completion point position abscissa distinguishes the 1/ of deletion sites section
The position of n, 2/n, 3/n ..., (n-1)/n Along ent successively substitute into following once linear equation and calculate ordinate；
Wherein, (x, y) indicates the coordinate of completion point, (xi,yi) and (xi+1,yi+1) respectively indicate before deletion sites take a little
The coordinate of latter two tracing point；
The timestamp of such as point P76 is 1512094831s, and coordinate is (x76,y76), the timestamp of next point P77 in track
It is 1512094834s, coordinate is (x77,y77), the corresponding point of intercalary delection timestamp 1512094832s and 1512094833s,
The abscissa of the two points should take x76+1/3*(x77-x76) and x76+2/3*(x77-x76), the two abscissas are substituted into lower section
Formula:
Calculating separately gained y value is corresponding ordinate.By the position of all missings, completion is carried out in the method,
The point of missing is then no longer had on the track.It is finally as shown in Figure 4 using the effect of interpolation algorithm completion.
Step 3: using Kalman filtering to the navigation path point after denoising and completion, carrying out offset correction；
Step 3.1: obtaining the optimal estimation position of the tracing point of preceding state, predicted using navigation system process model
The position of the tracing point of current state, and navigation system is obtained in the covariance of the tracing point predicted position of current state；
It is counted according to following formula the position of the tracing point using navigation system process model prediction current state
It calculates and obtains predicted value；
X (k | k-1)=AX (k-1 | k-1)
Wherein, X (k | k-1) is indicated with the optimal estimation position X of the tracing point of preceding state k-1 (k-1 | k-1) prediction
The position prediction value of the tracing point of obtained current state k, A indicate navigation system gain, value 1；
The covariance of tracing point predicted position of the acquisition navigation system in current state, is obtained according to following formula calculating
:
P (k | k-1)=AP (k-1 | k-1) A'+Q
Wherein, P (k | k-1) and P (k-1 | k-1) respectively indicate the corresponding covariance of X (k | k-1) and X (k-1 | k-1), when
When k-1=0, P (k-1 | k-1) indicates initial point covariance, i.e. the initial covariance P (0 | 0)=1 of navigation system；Work as k-1 > 0, P
(k-1 | k-1)=(I-Kg (k-1) H) P (k-1 | k-2)；A'=A=1, Q indicate the process covariance of navigation system, value 1
×10-5, I is unit matrix.
Step 3.2: using navigation system in the covariance of the tracing point predicted position of current state, calculating navigation system and exist
The kalman gain of the tracing point predicted position of current state；
Step 3.3: based on navigation system in current trace points predicted position and its kalman gain and current state
The position measurements of tracing point obtain the optimal estimation position of the tracing point of current state using Kalman filtering optimal estimation,
Position after using the optimal estimation position of the tracing point of current state as correction；
It is described use Kalman filtering optimal estimation, obtain the optimal estimation position of the tracing point of current state, use with
Lower formula, which calculates, to be obtained:
X (k | k)=X (k | k-1)+Kg (k) (Z (k)-HX (k | k-1))
Kg (k)=P (k | k-1) H'/(HP (k | k-1) H'+R)
Wherein, Kg (k) indicates that kalman gain, Z (k) indicate that the measured value of the tracing point in state k, H indicate navigation system
Gain of the system state for navigation system in measured value, value 1.
Wherein, in the initial state, the optimal estimation positional value of first tracing point of navigation path is the to navigation system
The position measurements of one tracing point；In the initial state, the covariance of first tracing point of navigation path takes navigation system
Value is 1.
Change curve of the longitude and latitude before and after filtering is as shown in figure 5, it will be seen that the Position Latitude of one of point
In the presence of obvious offset.Local Navigation track before filtering is as shown in fig. 6, can be with there are the points of Position Latitude offset at this in Fig. 5
Belong to the point for carrying out offset correction in Local Navigation track.Filtered Local Navigation track is as shown in fig. 7, can see
It arrives, using Kalman filtering algorithm, the point position that there is offset in Fig. 6 is corrected, and after overcorrection, the Local Navigation track is more
Close to true wheelpath.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (5)
1. a kind of vehicle mounted guidance tracing point based on Kalman filtering deviates antidote, which comprises the following steps:
Step 1: denoising is carried out to the tracing point in navigation path；
Step 2: completion processing is carried out to the missing point in navigation path；
Step 3: using Kalman filtering to the navigation path point after denoising and completion, carrying out offset correction；
Step 3.1: obtaining the optimal estimation position of the tracing point of preceding state, predicted using navigation system process model current
The position of the tracing point of state, and navigation system is obtained in the covariance of the tracing point predicted position of current state；
Step 3.2: using navigation system in the covariance of the tracing point predicted position of current state, calculating navigation system current
The kalman gain of the tracing point predicted position of state；
Step 3.3: based on navigation system in current trace points predicted position and its kalman gain and the track of current state
The position measurements of point obtain the optimal estimation position of the tracing point of current state using Kalman filtering optimal estimation, to work as
The optimal estimation position of the tracing point of preceding state is as the position after correction；
Wherein, in the initial state, the optimal estimation positional value of first tracing point of navigation path is first to navigation system
The position measurements of tracing point；In the initial state, the covariance value of first tracing point of navigation path is navigation system
1。
2. the method according to claim 1, wherein described predict current state using navigation system process model
Tracing point position according to following formula carry out calculate obtain predicted value；
X (k | k-1)=AX (k-1 | k-1)
Wherein, X (k | k-1) indicates to obtain with the optimal estimation position X of the tracing point of preceding state k-1 (k-1 | k-1) prediction
Current state k tracing point position prediction value, A indicate navigation system gain, value 1；
The covariance of tracing point predicted position of the acquisition navigation system in current state, calculates according to following formula and obtains:
P (k | k-1)=AP (k-1 | k-1) A'+Q
Wherein, P (k | k-1) and P (k-1 | k-1) respectively indicate the corresponding covariance of X (k | k-1) and X (k-1 | k-1), work as k-1=
When 0, P (k-1 | k-1) indicates initial point covariance, i.e. the initial covariance P (0 | 0)=1 of navigation system；Work as k-1 > 0, P (k-1 |
K-1)=(I-Kg (k-1) H) P (k-1 | k-2)；A'=A=1, Q indicate that the process covariance of navigation system, value are 1 × 10-5,
I is unit matrix.
3. according to the method described in claim 2, acquisition is current it is characterized in that, described use Kalman filtering optimal estimation
The optimal estimation position of the tracing point of state, is calculated using the following equation acquisition:
X (k | k)=X (k | k-1)+Kg (k) (Z (k)-HX (k | k-1))
Kg (k)=P (k | k-1) H'/(HP (k | k-1) H'+R)
Wherein, Kg (k) indicates that kalman gain, Z (k) indicate that the measured value of the tracing point in state k, H indicate navigation system shape
Gain of the state for navigation system in measured value, value 1.
4. method according to claim 1-3, which is characterized in that the tracing point in navigation path carries out
The process of denoising is as follows:
Step 1.1: speed sectors division is carried out to the tracing point in navigation path；
To in navigation path, section where belonging to the continuous path point of identical speed sectors carries out speed sectors division, each
5 continuous path points are included at least in speed sectors；
Tracing point speed sectors include low speed section [0~10) m/s, middling speed section [20~30) m/s, high-velocity section [0~10m/
s；
Step 1.2: calculating the distance between adjacent track point in each speed sectors, judge the adjacent track in each speed sectors
Whether the distance between point is more than preset normal maximum distance, if being more than, then it is assumed that second track in adjacent track point
Point is noise spot, by noise point deletion；
There is no tracing point missing:
Low speed section, it is 20m that the sampling time, which is separated by the distance of the normal maximum between two points of 1s,；
Middling speed section, sampling time are separated by the 3 of the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
Times；
High-velocity section, sampling time are separated by the 2 of the instantaneous velocity that the distance of the normal maximum between two points of 1s is previous point
Times；
There are tracing point missing:
It is corresponding there is no tracing point missing multiplied by place speed sectors with the sampling time difference of two neighboring point
The product that maximum distance obtains, as the preset normal maximum distance between adjacent two o'clock.
5. according to the method described in claim 4, it is characterized in that, the missing point in navigation path carries out completion processing
Process it is as follows:
Step 2.1: tracing point lacks degree detecting；
The timestamp information of each point is successively traversed since navigation path starting point, if the sampling instant of the latter tracing point with
The sampling instant of previous tracing point is more than sampling time interval, then there is missing point, counts the subsequent company of the latter tracing point
Missing time in continuous 30 tracing points is with continuous 30 points that the latter tracing point starts if missing time is more than 10s
Serious track is lacked, the orbit segment is deleted, otherwise, continually looks for next missing point；
Step 2.2: the navigation path point based on piecewise linear interpolation lacks completion；
The timestamp information that each point is successively traversed since navigation path starting point finds all positions for having missing, by certain
One deletion sites section is denoted as [xi,xi+1]；
In deletion sites section [xi,xi+1] the sampling time differences of former and later two tracing points is n (s), wherein 0 < n≤10 and n are whole
Number；
By deletion sites section [xi,xi+1] n equal part is carried out, completion point position abscissa distinguishes 1/n, the 2/n in deletion sites section,
The position of 3/n ..., (n-1)/n Along ent successively substitute into following once linear equation and calculate ordinate；
Wherein, (x, y) indicates the coordinate of completion point, (xi,yi) and (xi+1,yi+1) respectively indicate deletion sites and take front and back two a little
The coordinate of a tracing point.
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