CN109374001A - A kind of azimuth calibration algorithm of combination pedestrian movement context restrictions - Google Patents
A kind of azimuth calibration algorithm of combination pedestrian movement context restrictions Download PDFInfo
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- CN109374001A CN109374001A CN201811380241.8A CN201811380241A CN109374001A CN 109374001 A CN109374001 A CN 109374001A CN 201811380241 A CN201811380241 A CN 201811380241A CN 109374001 A CN109374001 A CN 109374001A
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C1/00—Measuring angles
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- 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
- G01C21/18—Stabilised platforms, e.g. by gyroscope
Abstract
The invention discloses a kind of azimuth calibration algorithms of combination pedestrian movement context restrictions, in Methods of Strapdown Inertial Navigation System, utilize the instantaneous angular increment (angular speed) of gyroscope measurement each axis rotary motion of carrier, the computer azimuth information by the way of integral, with the growth of time, the state of diverging can be presented in azimuth angle error, so that the long-term accuracy of system is poor, electronic compass based on earth magnetism and gravity property, the problem of there is no error accumulations, but when there is interference magnetic field, the data of magnetometer can make azimuth generate relatively large deviation instead, then introduce the azimuth calibration algorithm constrained based on moving scene.It is using some known regular motion modes, such as combine pedestrian on the ground straight line walking mode, stair activity mode as constraint condition, to correct azimuth angle error, azimuth calibration algorithm proposed by the present invention avoids the problem of due to gyroscopic drift and external magnetic field interference bring azimuth angle deviation, further improves navigation and positioning accuracy.
Description
Technical field
What the present invention designed is a kind of azimuth calibration algorithm, more particularly, to a kind of combination pedestrian movement context restrictions
Autonomous azimuth calibration algorithm.
Background technique
Entirely autonomous navigation may be implemented in strapdown inertial navigation system, does not need outside plant.In recent years, based on low cost
Strapdown Inertial Navigation System, small-sized MEMS inertial sensor have received more and more in some military and civilian navigator fixs
Concern, especially in the not available situation of satellite navigation, such as indoor, underwater or underground.Strapdown inertial navigation is positioned and is
For system, the azimuthal resolving of carrier relies primarily on gyroscope completion, and gyroscope has integrator drift characteristic, when azimuth is inclined
When difference is larger, which will become the main source of location error.Therefore, according to the status of MEMS gyroscope precision, deviation with
Accumulated time rises quickly, if not using necessary azimuth angle error regular calibration measure, it is prolonged accurate to be difficult to support
Navigator fix.
In order to improve the azimuth estimated accuracy of MEMS Strapdown Inertial Navigation System, magnetometer is often introduced as auxiliary progress side
The calibration of parallactic angle, magnetometer is most common auxiliary azimuth calibrating installation, since magnetometer is also belonged to from principal mode element,
It is unaffected that the inertial navigation positioning system of introducing magnetometer is able to maintain its independence.But when there is interference magnetic field, magnetic strength
The data of meter can make azimuth generate relatively large deviation instead, so that the long-term accuracy of system is poor.Therefore existing azimuth calibration is calculated
Method is in the environment of there are magnetic interference it cannot be guaranteed that azimuth high-precision accurate output for a long time.
Summary of the invention
It is an object of the invention to overcome the shortcomings of existing azimuth calibration algorithm, a kind of combination pedestrian movement scene is provided
The azimuth calibration algorithm of constraint.
The technical solution adopted by the present invention are as follows:
A kind of azimuth calibration algorithm of combination pedestrian movement context restrictions, comprising the following steps:
(1) the measurement number according to the pedestrian of sensor output in straight line walking mode or pedestrian in stair walking mode
According to, by inertial navigation algorithm obtain pedestrian in straight line walking mode or pedestrian each moment pedestrian in stair walking mode
Speed, position calculation result and orientation calculation result；And fourth order Runge-Kutta algorithm is carried out according to the data of gyroscope measurement and is obtained
The quaternary number arrived；The measurement data of the sensor output includes the data of accelerometer measures and the data of gyroscope measurement；
(2) it according to position calculation result, zero-speed detection and zero-velocity curve technology, obtains perpendicular on two neighboring zero-speed section
Histogram is to displacement difference；According to azimuth calculation result, zero-speed detection and zero-velocity curve technology, obtain on two neighboring zero-speed section
Heading angle deviation；
(3) according to the vertical direction displacement difference and heading angle deviation and quaternary number foundation card on two neighboring zero-speed section
Thalmann filter carries out pedestrian in the update and calibration of straight line walking mode or the pedestrian course angle in stair walking mode.
Wherein, the Kalman filter established in step (3) specifically:
In formula, X (K) and X (k-1) are respectively the quantity of state at k moment and k-1 moment in Kalman filtering algorithm, and Z (K) is
The observed quantity at k moment in Kalman filtering algorithm；Φ (k, k-1) is the process transfer matrix in Kalman filtering algorithm, Γ (k-
It 1) is the additional matrix of Kalman filter, W (k-1) is gyro noise matrix, and H (k) is the adjustment system in state change process
Number, V (k) are observation noise；
Wherein, the quantity of state at k-1 moment and observed quantity respectively indicate are as follows:
X (k-1)=[q0,q1,q2,q3]T
Z (k-1)=[Δ Sz；Δyaw]
In formula, q0,q1,q2,q3For the quaternary number that the data progress fourth order Runge-Kutta algorithm of gyroscope measurement obtains, Δ
Sz is the vertical direction displacement difference on two neighboring zero-speed section, and Δ yaw is the heading angle deviation on two neighboring zero-speed section.
Wherein, gyro noise matrix is expressed as:
W (k-1)=[Wx_offest；Wy_offest；Wz_offest]
In formula, Wx_offest, Wy_offest, Wz_offestWhite noise respectively on three axis of gyroscope；
The additional matrix Γ (k-1) of Thalmann filter indicates are as follows:
In formula, Δ T is the sampling period；
Wherein, the process transfer matrix in Kalman filtering algorithm indicates are as follows:
In formula, Δ θx=wxΔ T, Δ θy=wyΔ T, Δ θz=wzΔ T, Δ θ0=Δ θx 2+Δθy 2+Δθz 2, wx、wyAnd wz
The respectively data of gyroscope measurement.
The present invention compare the prior art have it is following the utility model has the advantages that
The present invention overcomes the occasion in earth magnetism by long-time severe jamming, electronic compass will be unable to accurately determine orientation
The problem of information, improves positioning accuracy.The method of the present invention is simple, and stability and high reliablity effectively raise pedestrian and lead
The positioning accuracy of boat.
Detailed description of the invention:
Fig. 1 is the principle of the present invention block diagram.
Fig. 2 is the linear aspect solution nomogram that the present invention is implemented.
Fig. 3 is the azimuth calibration arithmetic result of going upstairs that the present invention is implemented.
Fig. 4 is the trajectory diagram of going upstairs that the present invention is implemented.
Specific embodiment:
The present invention is described further with reference to the accompanying drawings and detailed description:
Fig. 1 is the principle of the present invention block diagram, and in walking, the stage that foot touches ground is known as zero-speed section, walking
In the vertical direction displacement difference in two neighboring zero-speed section and the difference of course angle should be zero, devise azimuth shown in FIG. 1
Computation.
The present invention realizes that steps are as follows:
(1) pedestrian exported according to gyroscope and accelerometer is in straight line walking mode or pedestrian in stair walking mode
Measurement data, by inertial navigation algorithm obtain pedestrian in straight line walking mode or pedestrian per a period of time in stair walking mode
Carve speed, position calculation result and the orientation calculation result of pedestrian；And quadravalence Long Geku is carried out according to the data of gyroscope measurement
The quaternary number that tower algorithm obtains；The measurement data of the sensor output includes the data and gyroscope measurement of accelerometer measures
Data；
(2) it according to position calculation result, zero-speed detection and zero-velocity curve technology, obtains perpendicular on two neighboring zero-speed section
Histogram is to displacement difference；According to azimuth calculation result, zero-speed detection and zero-velocity curve technology, obtain on two neighboring zero-speed section
Heading angle deviation；Vertical direction displacement difference on two neighboring zero-speed section is indicated with Δ Sz；
Δ Sz (i)=SL (i)-SL (i-1) i=2,3 ... ..nn
Wherein, if zero-speed section number is nn, the vertical direction displacement in zero-speed section is SL.
Heading angle deviation on two neighboring zero-speed section；
Δ yaw (i)=yaw (i)-yaw (i-1) i=2,3 ... ..nn
Wherein which zero-speed section i indicates, if zero-speed section number is nn.
(3) according to the vertical direction displacement difference and heading angle deviation and quaternary number foundation card on two neighboring zero-speed section
Thalmann filter carries out pedestrian in the update and calibration of straight line walking mode or the pedestrian course angle in stair walking mode.
The Kalman filter of foundation specifically:
In formula, X (K) and X (k-1) are respectively the quantity of state at k moment and k-1 moment in Kalman filtering algorithm, and Z (K) is
The observed quantity at k moment in Kalman filtering algorithm；Φ (k, k-1) is the process transfer matrix in Kalman filtering algorithm, Γ (k-
It 1) is the additional matrix of Kalman filter, W (k-1) is gyro noise matrix, and H (k) is the adjustment system in state change process
Number, V (k) are observation noise；
Wherein, the quantity of state at k-1 moment and observed quantity respectively indicate are as follows:
X (k-1)=[q0,q1,q2,q3]T
Z (k-1)=[Δ Sz；Δyaw]
In formula, q0,q1,q2,q3For the quaternary number that the data progress fourth order Runge-Kutta algorithm of gyroscope measurement obtains, Δ
Sz is the vertical direction displacement difference on two neighboring zero-speed section, and Δ yaw is the heading angle deviation on two neighboring zero-speed section.
Wherein, gyro noise matrix is expressed as:
W (k-1)=[Wx_offest；Wy_offest；Wz_offest]
In formula, Wx_offest, Wy_offest, Wz_offestWhite noise respectively on three axis of gyroscope；
The additional matrix Γ (k-1) of Thalmann filter indicates are as follows:
In formula, Δ T is the sampling period；
Process transfer matrix in Kalman filtering algorithm indicates are as follows:
In formula, Δ θx=wxΔ T, Δ θy=wyΔ T, Δ θz=wzΔ T, Δ θ0=Δ θx 2+Δθy 2+Δθz 2, wx、wyAnd wz
The respectively data of gyroscope measurement.
Two groups of experiments have been carried out respectively in order to verify the feasibility of Line Algorithm, and experiment model is to tie up sensor to be expert at
On people's foot, sending instruction makes it, and a certain position is set out indoors, is walked along straight line, two groups of experiment straight line travel distances are respectively 8
Rice.Fig. 2 indicates that pedestrian walks 8 meters of orientation solution nomogram and trajectory diagram along straight line.It is not easy intuitively to see from orientation solution nomogram
The azimuth deviation of the pedestrian in straight line walking mode is surveyed, in order to further determine the practicability of this algorithm, is walked using straight line
Whole story position deviation determined, by MATLAB software can find in stair walking mode pedestrian initial orientation and
The actual value of last location fix, as shown in straight line walking mode table.It can be seen that passing through from straight line walking mode table
In conjunction with the method for linear motion restriction mode, 0.5 ° may remain in pedestrian with 8 meters of inner orientation angular accuracy of straight line pattern walking
Within, should the experimental results showed that, in conjunction with linear motion restriction mode method can be calibrated azimuthal well, utilize
The algorithm can guarantee that high-precision exports for a long time at azimuth.
When in conjunction with stair movement mode, the height of each step is certain in stair walking process, and every
The height of a step is not much different, and according to this feature, can calculate the vertical direction of adjacent step in stair walking
Then displacement carries out the amendment in course, the section of foot and step contact according to the difference of two neighboring step vertical direction displacement
Precisely zero-speed section, i.e., it is believed that it is a platform that two adjacent zero-speed section vertical position differences, which are approximately steady state value,
The height of rank.Firstly, carrying out attitude algorithm according to gyroscope and accelerometer obtains speed, position and orientation, while to movement
Mode is detected, when determining pedestrian when stair are walked, to the vertical side in the two neighboring zero-speed section after zero-velocity curve
To measuring value of the displacement as next step Kalman filtering algorithm, using the quaternary number of resolving during gyroscope measurement data as
Quantity of state is constantly updated by Kalman filtering, finally obtains top optimization direction, the speed, position of stair walking.It is filtered in Kalman
Using Δ Sz as observed quantity in wave, which has been discussed in detail above-mentioned, and the quaternary number obtained using navigation calculation is as shape
State amount to carry out the azimuth in stair activity motor pattern continuous update calibration.
In order to verify the validity of the azimuth calibration algorithm based on stair movement mode, reality of going upstairs has been carried out respectively
It tests, experiment model is that sensor module is tied up to the walking that carries out going upstairs on pedestrian's foot (to provide a certain position of its stairway step
For starting point, for the ease of seeking whole story position course deviation, the position and start position that the last one step stops must be one
On straight line, from starting point to being not limited to course during the stop position of a last step).Fig. 3 is indicated upstairs
The azimuth calibration algorithm calculation result proposed during ladder.Fig. 4 indicates to utilize the azimuth calibration proposed during going upstairs
The trajectory diagram that algorithm obtains.Pedestrian's initial orientation and the last bit side of setting in stair walking mode can be found by MATLAB software
The actual value of position.By the azimuth calibration technology phase for combining kinematic constraint mode of going upstairs according to the table of stair mode
The azimuth accuracy obtained than the compensation calculation method in navigator fix resolves only with zero-velocity curve is significantly improved.
Excellent beneficial effect of the invention is further described in conjunction with following experiment:
The MPU9150 that the present invention is produced using STM32F103ZET6 development board (Buddhist nun not M3S) and InvenSense company
(GY-9150) sensor module acquires data, in elevator motion mode, sensor module is tied up on pedestrian's foot, pedestrian
State is remain stationary in elevator；In linear motion mode, pedestrian carries the sensor module tied up on foot and carries out linear rows
It walks；In stair movement mode, sensor is tied up to the mode walking that carries out going upstairs on pedestrian's foot.Testing scene is Hebei work
In industry University College building.
To meet the Larger Dynamic requirement of pedestrian movement.The module encapsulate 3 axis gyroscopes, 3 axis accelerometers, measured value with
Digital quantity is exported by IIC interface, and range, sensitivity, sample rate, bandwidth PLC technology, main technical performance index is such as
Under:
(1) sensor range of dynamic measurement (maximum): gyroscope: ± 2000 °/s；Accelerometer: ± 16g.
(2) transducer sensitivity (when maximum range): gyroscope: 1/16.4 °/s；Accelerometer: 1/2048g.
Magnetometer: ± 0.3uT；Thermometer: 1/340 DEG C.
(3) sensor noise: gyroscope isAccelerometer is
(4) highest sample rate: gyroscope: 8000SPS；Accelerometer: 1000SPS.
(5) sensor bandwidth (programmable): gyroscope: 2-256Hz；Accelerometer: 5-260Hz.
The sample frequency of pedestrian navigation positioning system is 100HZ in experiment, in order to it is vivider illustrate it is of the invention effective
Property.Combination proposed by the present invention is given shown in the table table 1 of straight line walking mode and shown in the table table 2 of stair walking mode
Linear motion mode, the bearing calibration deviation in conjunction with stair movement mode.Movement of going upstairs wherein is combined in the mode of going upstairs
The azimuth calibration algorithm of restriction mode relatively before azimuth accuracy improve 81.36%.It can be well using the present invention
Azimuthal is calibrated, and can guarantee that high-precision exports for a long time at azimuth using the invention.
Table 1
Table 2
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in original of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within then.
Claims (4)
1. a kind of azimuth calibration algorithm of combination pedestrian movement context restrictions, which comprises the following steps:
(1) measurement data according to the pedestrian of sensor output in straight line walking mode or pedestrian in stair walking mode, leads to
Cross inertial navigation algorithm obtain pedestrian straight line walking mode or pedestrian in stair walking mode the speed of each moment pedestrian,
Position calculation result and orientation calculation result；And four that fourth order Runge-Kutta algorithm obtains are carried out according to the data of gyroscope measurement
First number；The measurement data of the sensor output includes the data of accelerometer measures and the data of gyroscope measurement；
(2) according to position calculation result, zero-speed detection and zero-velocity curve technology, the vertical side on two neighboring zero-speed section is obtained
To displacement difference；According to azimuth calculation result, zero-speed detection and zero-velocity curve technology, the boat on two neighboring zero-speed section is obtained
To angular displacement；
(3) according on two neighboring zero-speed section vertical direction displacement difference and heading angle deviation and quaternary number establish Kalman
Filter carries out pedestrian in the update and calibration of straight line walking mode or the pedestrian course angle in stair walking mode.
2. a kind of azimuth calibration algorithm of combination pedestrian movement context restrictions according to claim 1, which is characterized in that
The Kalman filter established in step (3) specifically:
In formula, X (K) and X (k-1) are respectively the quantity of state at k moment and k-1 moment in Kalman filtering algorithm, and Z (K) is karr
The observed quantity at k moment in graceful filtering algorithm；Φ (k, k-1) is the process transfer matrix in Kalman filtering algorithm, and Γ (k-1) is
The additional matrix of Kalman filter, W (k-1) are gyro noise matrix, and H (k) is the regulation coefficient in state change process, V
It (k) is observation noise；
Wherein, the quantity of state at k-1 moment and observed quantity respectively indicate are as follows:
X (k-1)=[q0,q1,q2,q3]T
Z (k-1)=[Δ Sz；Δyaw]
In formula, q0,q1,q2,q3For the quaternary number that the data progress fourth order Runge-Kutta algorithm of gyroscope measurement obtains, Δ Sz is phase
Vertical direction displacement difference on adjacent two zero-speed sections, Δ yaw are the heading angle deviation on two neighboring zero-speed section.
3. a kind of azimuth calibration algorithm of combination pedestrian movement context restrictions according to claim 2, which is characterized in that
Gyro noise matrix is expressed as:
W (k-1)=[Wx_offest；Wy_offest；Wz_offest]
In formula, Wx_offest, Wy_offest, Wz_offestWhite noise respectively on three axis of gyroscope；
The additional matrix Γ (k-1) of Thalmann filter indicates are as follows:
In formula, Δ T is the sampling period.
4. a kind of azimuth calibration algorithm of combination pedestrian movement context restrictions according to claim 2, which is characterized in that
Process transfer matrix in Kalman filtering algorithm indicates are as follows:
In formula, Δ θx=wxΔ T, Δ θy=wyΔ T, Δ θz=wzΔ T, Δ θ0=Δ θx 2+Δθy 2+Δθz 2, wx、wyAnd wzRespectively
For the data of gyroscope measurement.
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| CN106017461A (en) * | 2016-05-19 | 2016-10-12 | 北京理工大学 | Pedestrian navigation system three-dimensional spatial positioning method based on human/environment constraints |
| CN107478223A (en) * | 2016-06-08 | 2017-12-15 | 南京理工大学 | A kind of human body attitude calculation method based on quaternary number and Kalman filtering |
| CN108362282A (en) * | 2018-01-29 | 2018-08-03 | 哈尔滨工程大学 | A kind of inertia pedestrian's localization method based on the adjustment of adaptive zero-speed section |
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| CN104613963A (en) * | 2015-01-23 | 2015-05-13 | 南京师范大学 | Pedestrian navigation system and navigation positioning method based on kinesiology model |
| CN104931049A (en) * | 2015-06-05 | 2015-09-23 | 北京信息科技大学 | Movement classification-based pedestrian self-positioning method |
| CN106017461A (en) * | 2016-05-19 | 2016-10-12 | 北京理工大学 | Pedestrian navigation system three-dimensional spatial positioning method based on human/environment constraints |
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