CN110986941A - Method for estimating installation angle of mobile phone - Google Patents

Method for estimating installation angle of mobile phone Download PDF

Info

Publication number
CN110986941A
CN110986941A CN201911198827.7A CN201911198827A CN110986941A CN 110986941 A CN110986941 A CN 110986941A CN 201911198827 A CN201911198827 A CN 201911198827A CN 110986941 A CN110986941 A CN 110986941A
Authority
CN
China
Prior art keywords
mobile phone
angle
information
estimating
speed
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
Application number
CN201911198827.7A
Other languages
Chinese (zh)
Other versions
CN110986941B (en
Inventor
旷俭
牛小骥
闫伟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan University WHU
Original Assignee
Wuhan University WHU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Wuhan University WHU filed Critical Wuhan University WHU
Priority to CN201911198827.7A priority Critical patent/CN110986941B/en
Publication of CN110986941A publication Critical patent/CN110986941A/en
Application granted granted Critical
Publication of CN110986941B publication Critical patent/CN110986941B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • G01C21/18—Stabilised platforms, e.g. by gyroscope
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

Abstract

The invention provides a method for estimating a mobile phone mounting angle, which comprises the steps of collecting data of a built-in sensor of a mobile phone, and determining initial navigation information of the mobile phone by using an accelerometer and a magnetometer observed value in an initial period of time; carrying out inertial navigation calculation on continuously acquired sensor data, and carrying out calculation on the sensor data at the current moment to obtain navigation information and storing the navigation information; judging the availability of various constraint information and sensor observation information, and maintaining the navigation performance of the mobile phone through multi-source data fusion; identifying and judging the use mode of the mobile phone by using the original observed value of the sensor, the solved intermediate value and the mobile phone attitude information, and starting mobile phone installation angle estimation when the use mode of the mobile phone changes; and obtaining the moving direction of the pedestrian by using the linear motion constraint, the estimated speed and the historical track, and calculating the installation angle of the mobile phone by combining the estimated course angle of the mobile phone. The invention can improve the positioning performance of pedestrian dead reckoning.

Description

Method for estimating installation angle of mobile phone
Technical Field
The invention belongs to the technical field of pedestrian positioning, and relates to a method for estimating a mounting angle of a mobile phone.
Background
In recent years, a pedestrian navigation technology based on a smart phone is rapidly developed, and a Pedestrian Dead Reckoning (PDR) technology is taken as an important component, wherein the estimation of the motion direction of a pedestrian is a difficult problem to be solved. The pedestrian movement direction mainly comprises two parts: a mobile phone course angle and a mobile phone installation angle. The mobile phone course angle refers to the course of the smart phone and can be obtained by fusing the observation data of the sensor arranged in the smart phone. The mobile phone installation angle refers to a difference angle between a mobile phone heading angle and a pedestrian movement direction, and changes with different mobile phone use modes, such as situations of transversely holding a mobile phone, making a call, putting the mobile phone into a pocket, swinging the mobile phone with a hand arm and the like, wherein the wide application of the PDR technology is seriously influenced by the estimation problem of the mobile phone installation angle.
Aiming at the problem of mobile phone installation angle estimation, at present, there are three main solutions, including a principal component analysis method, a forward lateral velocity model method and an inertial data frequency domain analysis method, and three existing methods are explained as follows:
(1) principal component analysis method
The basic assumption of this method is: the pedestrian moves in the direction, the acceleration change is maximum, namely the variance is maximum; and the acceleration variance is minimum in the lateral direction of the vertical motion direction. The specific idea is as follows: and projecting the observed acceleration to a horizontal plane, extracting the direction with the largest variance and the direction with the smallest variance as the forward direction and the lateral direction of the pedestrian, and further calculating the deviation angle. However, this method has a 180 ° ambiguity problem, i.e. the direction of the largest variance may be opposite to the pedestrian movement direction; meanwhile, the method strictly requires that the movement direction of the smart phone is consistent with the movement direction of the pedestrian, and the method is invalid when the pedestrian inclines to the swing arm.
(2) Forward lateral velocity model method
The method is basically consistent with the principal component analysis method, so the problems are consistent. In contrast, the method requires training forward acceleration and lateral acceleration models in advance, and requires separate training for different use scenarios and users to achieve the desired expected results. The defects are that the workload of model training is large and the applicability is low.
(3) Inertial data frequency domain analysis method
The method comprises the steps of constructing an evaluation function related to the pedestrian course angle by using the frequency domain characteristics of inertial sensor data, solving the pedestrian course angle by using an optimal estimation method, and further calculating an installation angle. The method has the disadvantages of large calculation amount, frequency domain analysis by using long-time historical information, and difficulty in realizing real-time navigation application.
Disclosure of Invention
The invention aims to more accurately estimate the pedestrian course and improve the availability of PDR (portable data radio), and provides a method for accurately estimating the installation angle of a mobile phone in real time. The method is characterized in that: 1) the user does not need to intervene in advance or afterwards; 2) no additional equipment and tools are required; 3) work in a variety of use scenarios (e.g., handheld, phone, swing with hand, pocket); 4) has strong real-time performance.
The invention realizes a method for estimating the installation angle of a mobile phone by the following technical scheme, which comprises the following steps:
step a, collecting data of a built-in sensor of the mobile phone, and determining initial navigation information of the mobile phone by using an accelerometer and a magnetometer observed value in an initial period of time;
b, carrying out inertial navigation calculation on continuously acquired sensor data, and calculating the sensor data at the current moment to obtain navigation information and storing the navigation information;
step c, judging the availability of various constraint information and sensor observation information, and maintaining the navigation performance of the mobile phone through multi-source data fusion;
d, using the original observed value of the sensor, the solved intermediate value and the mobile phone posture information to identify and judge the mobile phone use mode, and entering the step e to start the mobile phone installation angle estimation when the mobile phone use mode changes;
and e, acquiring the moving direction of the pedestrian by using the linear motion constraint, the estimated speed and the historical track, and calculating the installation angle of the mobile phone by combining the estimated course angle of the mobile phone.
And, the built-in sensors of the mobile phone include a gyroscope, an accelerometer, a magnetometer and a barometer.
And the navigation information comprises a three-dimensional position, a three-dimensional speed and a three-dimensional attitude, wherein the three-dimensional position comprises a north position, an east position and a vertical position, the three-dimensional speed comprises a north speed, an east speed and a vertical speed, and the three-dimensional attitude comprises a roll angle, a pitch angle and a course angle.
In addition, in order to maintain the accuracy of the mobile phone navigation information, the constraint information includes quasi-static information, pseudo-velocity information, an acceleration observation value, quasi-static magnetic field information, and a position observation value.
In step d, the intermediate calculation value includes an acceleration module value, an acceleration component of the horizontal plane, a horizontal acceleration module value, a vertical acceleration, an angular velocity module value, a horizontal angular velocity component, a horizontal angular velocity module value, and a vertical angular velocity.
And in the step d, the using modes of the mobile phone comprise end flattening, call making, arm swinging of the mobile phone handle and placing of the mobile phone into the trouser pocket.
And in the step e, the linear motion constraint means that the motion track of the user in a period of time is a straight line, the current motion direction is obtained by using the historical course angle of the mobile phone and the installation angle of the mobile phone, and the installation angle of the mobile phone at the current moment is obtained by combining the historical course angle of the mobile phone and the installation angle of the mobile phone.
And when the mobile phone is relatively static with the human body, the speed estimated by the mobile phone is the pedestrian movement speed, the current pedestrian movement direction is obtained by using the mobile phone speed arctangent, and the mobile phone installation angle is obtained by combining the mobile phone course angle.
And when the mobile phone and the human body are relatively static, the relative displacement estimated by the mobile phone is equal to the relative displacement of the pedestrian, the current moving direction of the pedestrian is obtained by using the arctangent of the relative displacement, and the mobile phone installation angle can be obtained by combining the course angle of the mobile phone.
Moreover, the combined algorithm adopted by the multi-source data fusion implementation mode comprises but is not limited to a Kalman filtering algorithm and a modified algorithm thereof.
The invention has the following beneficial effects:
the method can solve the problem that the course angle of the mobile phone is inconsistent with the moving direction of the pedestrian in the pedestrian positioning application based on the smart phone end, fully utilizes the autonomous reckoning capability of the strapdown inertial navigation method, and estimates the installation angle of the mobile phone by using the relative displacement and the instantaneous speed in a short time period, thereby greatly improving the positioning performance of the pedestrian track reckoning. The method has no 180-degree ambiguity problem, does not need large-scale model training, has small calculated amount, is completely suitable for real-time positioning requirements, and has important popularization and application values.
Drawings
FIG. 1 is a schematic diagram of the relationship among the pedestrian movement direction, the mobile phone heading angle and the mobile phone installation angle in the embodiment of the invention.
Fig. 2 is a block diagram of a method for estimating a mobile phone installation angle according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is specifically described below with reference to the accompanying drawings and examples.
FIG. 1 shows the relationship between the estimated handset mounting angle and the handset heading angle and the direction of pedestrian movement according to the present invention. The invention provides a method for estimating the difference between the pedestrian movement direction and the mobile phone course angle (namely the mobile phone installation angle). Firstly, collecting data of a built-in sensor of the mobile phone, carrying out inertial navigation calculation, and maintaining a navigation state (such as a three-dimensional position, a speed and a posture) of the mobile phone by using a data fusion method and using various constraint information and sensor observation information; then, identifying the use mode of the mobile phone by using the original sensor observation value, the solved intermediate value (such as the projection of the sensor observation value on the horizontal plane) and the mobile phone attitude information; and finally, obtaining the moving direction of the pedestrian by using the estimated historical track of the user or the speed of the mobile phone, and calculating the installation angle of the mobile phone by combining the calculated course angle of the mobile phone.
Fig. 2 shows a method for estimating a mobile phone installation angle according to an embodiment of the present invention, where the method includes the following steps:
step 101, collecting data of a built-in sensor of the mobile phone, and determining initial navigation information of the mobile phone by using the accelerometer and magnetometer observed values collected within an initial period of time (for example, 1-2 seconds).
The built-in sensor of the mobile phone comprises a gyroscope, an accelerometer, a magnetometer and a barometer.
The navigation information includes a three-dimensional position, a three-dimensional velocity, and a three-dimensional attitude. The three-dimensional positions are a north position, an east position and a vertical position, the three-dimensional speeds are a north speed, an east speed and a vertical speed, and the three-dimensional postures are a roll angle, a pitch angle and a course angle.
In the embodiment of the invention, the initial position of the mobile phone is set as the origin of coordinates (0,0,0), the initial speed is set as (0,0,0), the initial horizontal attitude angle comprises a roll angle and a pitch angle, wherein the roll angle and the pitch angle of the smart phone are calculated by using accelerometer data, and the calculation formula is as follows
Figure BDA0002295349980000041
Wherein phi and theta are respectively roll angle and pitch angle,
Figure BDA0002295349980000042
the raw observations of the triaxial accelerometer, atan () represents the inverse tangent function and atan2() represents the inverse tangent function for quadrant 4, respectively.
Computing handset heading angle psi using magnetometer datatrueThe calculation formula is as follows
ψtrue＝ψmag+Δψdeclination＝arctan(My/Mx)+Δψdeclination(2)
In the formula,. DELTA.psideclinationIs magnetic declination and is obtained by the inquiry of an earth magnetic field reference modelmagIs the magnetic heading angle, Mx、MyIs the X, Y axis component of the magnetic field in the navigational coordinate system (n system). MxAnd MyCalculated by the following formula
Figure BDA0002295349980000043
In the formula (I), the compound is shown in the specification,
Figure BDA0002295349980000044
respectively, the raw observed values of the three-axis magnetometer.
And 102, continuously acquiring sensor data, performing inertial navigation calculation, and storing the sensor data and navigation information at the current moment in a memory.
The Inertial Navigation solution refers to Navigation information of a known object at a previous moment, and integrates Inertial sensor data by using an Inertial Navigation System (INS) mechanical arrangement, so that Navigation information of the current moment can be obtained. The discretization form of the INS mechanical layout equation under the navigation coordinate system is as follows
Figure BDA0002295349980000045
In the formula, pnAnd vnRespectively a position vector and a velocity vector under n series;
Figure BDA0002295349980000046
is a directional cosine transform matrix from b to n, gn＝[0 0 -g]TIs a gravity vector, where g is the earth's gravity,
Figure BDA0002295349980000047
and baRespectively a triaxial accelerometer observed value vector and an accelerometer null offset vector,
Figure BDA0002295349980000048
and bgRespectively, a triaxial gyroscope observed value vector and a gyroscope zero offset, subscripts k-1 and k are used for identifying the k-1 th observation epoch and the k-th observation epoch variable, namely
Figure BDA0002295349980000049
And
Figure BDA00022953499800000410
respectively a position vector and a velocity vector under the n system of the k observation epoch,
Figure BDA00022953499800000411
b is a direction cosine transform matrix to n is a direction cosine transform matrix of the k-th observation epoch,
Figure BDA00022953499800000412
the tri-axial accelerometer observation vector for the kth observation epoch,
Figure BDA00022953499800000413
a three-axis gyroscope observation value vector for the kth observation epoch; Δ t ═ tk-tk-1For the time interval of the k-1 observation epoch from the k observation epoch, () × represents the inverse symmetric matrix of vector cross-product.
After the inertial navigation method is solved, the navigation state at the current moment is stored in the memory so as to provide initial conditions for re-performing inertial navigation calculation when the stable moment is searched forward when the installation angle of the mobile phone is calculated.
And 103, fully utilizing various constraint information, performing information fusion by using extended Kalman filtering, and maintaining the navigation precision of the mobile phone. During specific implementation, the usability of various constraint information and sensor observation information can be judged, and then the navigation performance of the mobile phone is maintained through data fusion.
The discretization form of the measurement equation of the extended Kalman filter and the state vector delta x are as follows
δz＝Hδx+n (5)
δx＝[(δrn)1×3(δvn)1×3(φ)1×3(bg)1×3(ba)1×3]T(6)
In the formula, delta z is observation information, H is an observation matrix, and n is observation noise; delta rn、δvnAnd phi is the error of three-dimensional position, three-dimensional speed and three-dimensional attitude under n series respectively.
The constraint information includes: quasi-static information, pseudo-velocity information, acceleration observations, quasi-static magnetic field information, and position observations. The acceleration observed value can be an acceleration vector projected to the navigation system by the accelerometer observed value in a use mode, and can also be a roll angle and a pitch angle calculated by the acceleration vector under the navigation system. The quasi-static magnetic field information can be the magnetic vector change of the magnetometer observed value projected to the navigation system in a use mode, and can also be the course angle change calculated by using the magnetic vector.
In specific implementation, the combination algorithm used in the implementation of the data fusion solution includes, but is not limited to: kalman Filter algorithms and their modifications, such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and other data fusion algorithms, such as bayesian estimation; sequential least squares; artificial neural networks, and the like. The embodiment adopts an extended Kalman filtering method.
The quasi-static information is the objective fact that when the person in a trip is in a standing static state, the speed of the pedestrian is zero and the course angle change is zero, and the aim of controlling the speed error and the course error accumulation of the pedestrian is achieved by reasonably constructing an observation equation. The zero velocity observation equation of the specific structure is as follows
Figure BDA0002295349980000051
In the formula, δ zvThe information is observed for a zero velocity,
Figure BDA0002295349980000052
velocity vector, n, for autonomous calculation by strapdown inertial navigation methodvNoise is measured for velocity. Meanwhile, based on the assumption that the heading is not changed in the static state (namely, heading locking, ZARU), a heading angle measurement equation is constructed as follows
Figure BDA0002295349980000053
In the formula, δ zψIs the observation information of the heading angle in the static state,
Figure BDA0002295349980000054
heading angle, psi, derived for strapdown inertial navigation methodstoreFor the stored heading angle corresponding to the first epoch in the static state,
Figure BDA0002295349980000055
Figure BDA0002295349980000061
respectively are the partial derivatives of the course angle to the roll angle, the pitch angle and the course angle,
Figure BDA0002295349980000062
is the 1 st row and 1 st column element of the directional cosine matrix,
Figure BDA0002295349980000063
is the 2 nd row and 1 st column element of the directional cosine matrix,
Figure BDA0002295349980000064
is the 3 rd row and 1 st column element, n of the directional cosine matrixψIs the measurement noise of the course angle.
The pseudo speed information refers to that only the forward speed, the lateral speed and the vertical speed are zero in the moving process of the pedestrian, wherein the forward speed can be obtained by dividing the moving distance estimated by the step size model by the step period. The pseudo-velocity observation equation of a specific construction is as follows
Figure BDA0002295349980000065
In the formula (I), the compound is shown in the specification,
Figure BDA0002295349980000066
in order to make the information of the observation of the pseudo velocity,
Figure BDA0002295349980000067
is a pedestrian velocity vector autonomously calculated by a strapdown inertial navigation method,
Figure BDA0002295349980000068
in order to be the pseudo-velocity information,
Figure BDA0002295349980000069
is a direction cosine matrix from a b system to an h system, is mainly determined by the difference of roll angle, pitch angle and course angle of two coordinate systems,
Figure BDA00022953499800000610
is a direction cosine matrix from n to b, (-) x represents an antisymmetric matrix of vector cross-multiplication,
Figure BDA00022953499800000611
is the measurement noise of the speed of the pedestrian.
The acceleration observed value means that under the condition that no external acceleration acts, the projection of the acceleration observed value in a local horizontal coordinate system is equal to a gravity vector. And judging whether the external acceleration exists or not by using the absolute value of the difference between the acceleration output module value and the gravity acceleration to be smaller than a certain threshold value. The acceleration observation equation of a specific structure is as follows
Figure BDA00022953499800000612
In the formula, δ zaFor acceleration observation information, fn＝[0 0 -g]TIs a gravity vector, g is a local earth gravity value,
Figure BDA00022953499800000613
respectively n is the observation vector of the accelerometer under the system of n and the system of b, n isaTo measure noise.
The quasi-static magnetic field information means that an indoor environment magnetic field is divided into a plurality of regional magnetic fields, and the relative change of the magnetic heading angle in the region is consistent with the relative change of the real magnetic heading angle. Measuring a corresponding measurement model by a magnetometer:
Figure BDA00022953499800000614
in the formula, δ zmFor the observation of information in a quasi-static magnetic field,
Figure BDA00022953499800000615
is a calibrated amount of the magnetic field in the region,
Figure BDA00022953499800000616
respectively n is the magnetometer observation vector under the n system and the b system, n ismNoise is measured for the magnetic field vector.It is emphasized that zero-bias compensation is required before using magnetometer observations.
The position correction information refers to that other positioning means such as GNSS, WiFi, barometer, map matching and the like can be used, and a measurement equation is constructed by using the positioning result, so that the precision of speed estimation is improved. For WiFi positioning as an example, the corresponding measurement equation is as follows
Figure BDA00022953499800000617
In the formula, δ zrIn order to observe the information for the position,
Figure BDA00022953499800000618
is a position vector autonomously calculated by a strapdown inertial navigation method,
Figure BDA00022953499800000619
location results provided for WiFi, nrTo measure noise.
And 104, identifying and judging the mobile phone use modes by using the original sensor observation values, the solved intermediate values and the mobile phone posture information, and entering 105 to start mobile phone installation angle estimation under the condition that the two adjacent mobile phone use modes are different.
The mobile phone use mode comprises the following steps: the trousers are flat, the telephone is called, the swing arm and the trousers pocket are arranged.
The resolving intermediate value comprises an acceleration module value, an acceleration component of a horizontal plane, a horizontal acceleration module value, a vertical acceleration, an angular velocity module value, a horizontal plane angular velocity component, a horizontal plane angular velocity module value and a vertical angular velocity.
During specific implementation, the current mobile phone use mode can be comprehensively judged by using the acceleration module value, the horizontal acceleration component, the horizontal acceleration module value, the vertical acceleration, the angular velocity module value, the horizontal plane angular velocity component, the horizontal plane angular velocity module value, the vertical angular velocity, the roll angle standard difference and the pitch angle standard difference. In the embodiment, when the pitch angle is larger than zero, the current mobile phone use mode is judged to be end-to-end or call, and then the current mobile phone use mode is further distinguished according to the roll angle; and when the absolute value of the roll angle is larger than 60 degrees, judging that the current mobile phone use mode is a calling mode, otherwise, judging that the current mobile phone use mode is an end-to-end mode. When the pitch angle is smaller than zero, the current mobile phone use mode is judged to be a swing arm mode or a trousers pocket, then the mobile phone use mode is further distinguished according to the standard deviation of the roll angle, when the standard deviation of the roll angle is larger than 10 degrees, the mobile phone use mode is judged to be the trousers pocket mode, and otherwise, the mobile phone use mode is the swing arm mode.
The present invention considers that the mounting angle under the same mode is unchanged or has very small change, and the mode change is used as a condition for entering the mounting angle estimation.
And 105, obtaining the motion direction of the user by using the linear motion constraint, the speed estimated by the mobile phone and the relative displacement estimated by the mobile phone, and calculating to obtain the installation angle of the mobile phone by combining the course angle estimated by the mobile phone.
The linear motion constraint means that the motion track of a user in a period of time is a straight line, the current motion direction is obtained by calculation by using the historical course angle of the mobile phone and the installation angle of the mobile phone, and the installation angle of the mobile phone at the current moment is obtained by combining the course angle of the mobile phone.
When the mobile phone is relatively static with the human body, the speed estimated by the mobile phone is the pedestrian movement speed, the current pedestrian movement direction is obtained by using the mobile phone speed arctangent, and the mobile phone installation angle is obtained by combining the mobile phone course angle; the relative displacement estimated by the mobile phone is equal to the relative displacement of the pedestrian, the current moving direction of the pedestrian is obtained by using the arctangent of the relative displacement, and the mobile phone installation angle can be obtained by combining the course angle of the mobile phone.
The motion direction of the user refers to the direction of the real motion track of the user, and the motion direction is irrelevant to the use mode of the mobile phone. The motion direction of the pedestrian user cannot be accurately estimated for a long time, so the motion direction of the user is estimated by combining the estimated mobile phone installation angle and the mobile phone course angle. Estimating the direction of user movement, which is also required for the mounting angle of the handset, is a paradoxical problem. But the invention can estimate the changed mobile phone installation angle by using the condition that the user motion direction is reliable in a short time.
In the embodiment, based on the objective fact that the usage habits of the mobile phone of the user do not change in a short time, the mobile phone installation angle of the single user in the single mode is considered to be unchanged or slightly changed, so that the mobile phone installation angle estimation is started only when the usage mode of the mobile phone of the user is judged to be changed. After the mobile phone installation angle estimation is started, the time corresponding to different mobile phone use modes is searched forward from the current time, and then inertial navigation integral calculation is carried out again to obtain the user track of the time period.
Then, straight line fitting is carried out on the section of track, if the fitted residual error is less than 0.03 m, a straight line which is walked by the user in the process of switching the mobile phone mode is considered, and the moving direction of the user is not changed at the moment
Figure BDA0002295349980000081
From the relationship between the angles shown in fig. 1, the following relationship can be obtained
Figure BDA0002295349980000082
Can be obtained by finishing
Figure BDA0002295349980000083
Where the superscript "pre" is used to identify the previous steady state, "cur" is used to identify the current state, ψTravel ofFor true heading, psi, of pedestriansMobile phoneHeading angle, psi, estimated for cell phoneMounting angleIs a mobile phone installation angle.
If the trajectory is determined to be a non-linear trajectory, calculating the traveling direction of the user in real time by using the speed of re-calculating the inertial navigation mechanical programming algorithm, wherein the calculation formula is as follows
ψTravel of＝arctan2(vN,vE) (15)
In the formula, vNAnd vENorth and east speeds, respectively. Or obtaining the traveling direction of the user by using the position increment corresponding to the two adjacent step points, wherein the calculation formula is as follows
ψTravel of＝arctan2(dN,dE) (16)
Where dN and dE are the north position increment and east position increment, respectively. The mounting angle of the mobile phone can be expressed as
ψMounting angle＝ψMobile phone-ψTravel of(17)
The information (such as the heading angle of the mobile phone, whether the mobile phone is a straight track, or a pedestrian advancing direction) related to the step 105 can be obtained by calculating the calculation result or the intermediate calculation result in the step 102 and the step 104.
In specific implementation, the automatic operation of the process can be realized by adopting a software mode. The apparatus for operating the process should also be within the scope of the present invention.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives in a similar manner to those skilled in the art to which the present invention pertains.

Claims (10)

1. A method for estimating a mobile phone mounting angle is characterized by comprising the following steps:
step a, collecting data of a built-in sensor of the mobile phone, and determining initial navigation information of the mobile phone by using an accelerometer and a magnetometer observed value in an initial period of time;
b, carrying out inertial navigation calculation on continuously acquired sensor data, and calculating the sensor data at the current moment to obtain navigation information and storing the navigation information;
step c, judging the availability of various constraint information and sensor observation information, and maintaining the navigation performance of the mobile phone through multi-source data fusion;
d, using the original observed value of the sensor, the solved intermediate value and the mobile phone posture information to identify and judge the mobile phone use mode, and entering the step e to start the mobile phone installation angle estimation when the mobile phone use mode changes;
and e, acquiring the moving direction of the pedestrian by using the linear motion constraint, the estimated speed and the historical track, and calculating the installation angle of the mobile phone by combining the estimated course angle of the mobile phone.
2. The method of estimating the mounting angle of a cellular phone according to claim 1, wherein: the built-in sensor of the mobile phone comprises a gyroscope, an accelerometer, a magnetometer and a barometer.
3. The method of estimating the mounting angle of a cellular phone according to claim 1, wherein: the navigation information comprises a three-dimensional position, a three-dimensional speed and a three-dimensional attitude, wherein the three-dimensional position comprises a north position, an east position and a vertical position, the three-dimensional speed comprises a north speed, an east speed and a vertical speed, and the three-dimensional attitude comprises a roll angle, a pitch angle and a course angle.
4. The method of estimating a mounting angle of a cellular phone according to claim 1, wherein: in order to maintain the accuracy of the mobile phone navigation information, the constraint information comprises quasi-static information, pseudo-velocity information, an acceleration observation value, quasi-static magnetic field information and a position observation value.
5. The method of estimating a mounting angle of a cellular phone according to claim 1, wherein: in the step d, the intermediate value includes an acceleration module value, an acceleration component of the horizontal plane, a horizontal acceleration module value, a vertical acceleration, an angular velocity module value, a horizontal plane angular velocity component, a horizontal plane angular velocity module value, and a vertical angular velocity.
6. The method of estimating a mounting angle of a cellular phone according to claim 1, wherein: in the step d, the using modes of the mobile phone comprise end leveling, call making, arm swinging of the mobile phone handle and placing of the mobile phone into a trouser pocket.
7. The method of estimating a mounting angle of a cellular phone according to claim 1, wherein: in the step e, the linear motion constraint means that the motion track of the user in a period of time is a straight line, the current motion direction is obtained by calculation by using the historical course angle of the mobile phone and the installation angle of the mobile phone, and the installation angle of the mobile phone at the current moment is obtained by combining the course angle of the mobile phone.
8. The method of estimating a mounting angle of a cellular phone according to claim 7, wherein: when the mobile phone is relatively static with the human body, the speed estimated by the mobile phone is the pedestrian movement speed, the current pedestrian movement direction is obtained by using the mobile phone speed arctangent, and the mobile phone installation angle is obtained by combining the mobile phone course angle.
9. The method of estimating a mounting angle of a cellular phone according to claim 7, wherein: when the mobile phone and the human body are relatively static, the relative displacement estimated by the mobile phone is equal to the relative displacement of the pedestrian, the current moving direction of the pedestrian is obtained by using the arctangent of the relative displacement, and the mobile phone installation angle can be obtained by combining the course angle of the mobile phone.
10. The method for estimating the installation angle of a cellular phone according to claim 1, 2, 3, 4, 5, 6, 7, 8 or 9, wherein: the combined algorithm adopted by the multi-source data fusion implementation mode comprises but is not limited to a Kalman filtering algorithm and a modified algorithm thereof.
CN201911198827.7A 2019-11-29 2019-11-29 Method for estimating installation angle of mobile phone Active CN110986941B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911198827.7A CN110986941B (en) 2019-11-29 2019-11-29 Method for estimating installation angle of mobile phone

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911198827.7A CN110986941B (en) 2019-11-29 2019-11-29 Method for estimating installation angle of mobile phone

Publications (2)

Publication Number Publication Date
CN110986941A true CN110986941A (en) 2020-04-10
CN110986941B CN110986941B (en) 2021-09-24

Family

ID=70088307

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911198827.7A Active CN110986941B (en) 2019-11-29 2019-11-29 Method for estimating installation angle of mobile phone

Country Status (1)

Country Link
CN (1) CN110986941B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337017A (en) * 2020-04-20 2020-06-26 杭州微萤科技有限公司 Automatic measurement method and device for AOA base station installation attitude
CN113074757A (en) * 2021-04-08 2021-07-06 北京李龚导航科技有限公司 Calibration method for vehicle-mounted inertial navigation installation error angle
CN113390381A (en) * 2021-06-08 2021-09-14 武汉大学 Sensor installation angle estimation method based on personal motion reference template

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150142312A1 (en) * 2013-11-20 2015-05-21 Samsung Electronics Co., Ltd. Method and apparatus for estimating moving direction of user and computer-readable storage medium having recorded thereon the method
CN104776846A (en) * 2014-01-13 2015-07-15 塔塔咨询服务有限公司 Mobile device and method for estimation of direction of motion of users on mobile devices
US20160138926A1 (en) * 2013-10-25 2016-05-19 Qualcomm Incorporated Automatic handover of positioning parameters from a navigation device to a mobile device
CN106017454A (en) * 2016-06-16 2016-10-12 东南大学 Pedestrian navigation device and method based on novel multi-sensor fusion technology
CN106767770A (en) * 2016-11-29 2017-05-31 西安交通大学 A kind of detection of user's direction of travel and method for tracing based on portable intelligent equipment
US10302434B2 (en) * 2012-11-20 2019-05-28 CSR Technology, Inc. Method and apparatus for determining walking direction for a pedestrian dead reckoning process

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10302434B2 (en) * 2012-11-20 2019-05-28 CSR Technology, Inc. Method and apparatus for determining walking direction for a pedestrian dead reckoning process
US20160138926A1 (en) * 2013-10-25 2016-05-19 Qualcomm Incorporated Automatic handover of positioning parameters from a navigation device to a mobile device
US20150142312A1 (en) * 2013-11-20 2015-05-21 Samsung Electronics Co., Ltd. Method and apparatus for estimating moving direction of user and computer-readable storage medium having recorded thereon the method
CN104776846A (en) * 2014-01-13 2015-07-15 塔塔咨询服务有限公司 Mobile device and method for estimation of direction of motion of users on mobile devices
CN106017454A (en) * 2016-06-16 2016-10-12 东南大学 Pedestrian navigation device and method based on novel multi-sensor fusion technology
CN106767770A (en) * 2016-11-29 2017-05-31 西安交通大学 A kind of detection of user's direction of travel and method for tracing based on portable intelligent equipment

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
牛小骥等: "采用MEMS惯导的小口管道内检测定位方案可行性研究", 《传感技术学报》 *
闫伟等: "光源编码+PDR组合的室内行人定位方法", 《测绘通报》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111337017A (en) * 2020-04-20 2020-06-26 杭州微萤科技有限公司 Automatic measurement method and device for AOA base station installation attitude
CN113074757A (en) * 2021-04-08 2021-07-06 北京李龚导航科技有限公司 Calibration method for vehicle-mounted inertial navigation installation error angle
CN113074757B (en) * 2021-04-08 2023-08-22 北京李龚导航科技有限公司 Calibration method for vehicle-mounted inertial navigation installation error angle
CN113390381A (en) * 2021-06-08 2021-09-14 武汉大学 Sensor installation angle estimation method based on personal motion reference template
CN113390381B (en) * 2021-06-08 2022-03-15 武汉大学 Sensor installation angle estimation method based on personal motion reference template

Also Published As

Publication number Publication date
CN110986941B (en) 2021-09-24

Similar Documents

Publication Publication Date Title
Qian et al. An improved indoor localization method using smartphone inertial sensors
CN110986941B (en) Method for estimating installation angle of mobile phone
US11598886B2 (en) GNSS/IMU surveying and mapping system and method
CN106225784B (en) Based on inexpensive Multi-sensor Fusion pedestrian dead reckoning method
CN110487267B (en) Unmanned aerial vehicle navigation system and method based on VIO & UWB loose combination
CN109991636A (en) Map constructing method and system based on GPS, IMU and binocular vision
CN102538781B (en) Machine vision and inertial navigation fusion-based mobile robot motion attitude estimation method
CN105674984B (en) A method of acquisition for mobile terminal pedestrian advancing direction is used without constraint
CN105043387A (en) Personal indoor positioning system based on inertial navigation aiding geomagnetism
JP6054535B2 (en) Pedestrian motion recognition based pedestrian position estimation apparatus and method
CN110764506B (en) Course angle fusion method and device of mobile robot and mobile robot
CN107830858B (en) Gravity-assisted mobile phone heading estimation method
CN109459028A (en) A kind of adaptive step estimation method based on gradient decline
CN108871325B (en) A kind of WiFi/MEMS combination indoor orientation method based on two layers of Extended Kalman filter
CN107677267A (en) Indoor pedestrian navigation course feedback modifiers method based on MEMS IMU
CN110412596A (en) A kind of robot localization method based on image information and laser point cloud
CN109959374A (en) A kind of full-time reverse smooth filtering method of whole process of pedestrian's inertial navigation
CN111522034A (en) Positioning method, equipment and device based on inertial navigation
CN104316058A (en) Interacting multiple model adopted WSN-INS combined navigation method for mobile robot
CN110487273B (en) Indoor pedestrian trajectory calculation method assisted by level gauge
Kuusniemi et al. Multi-sensor multi-network seamless positioning with visual aiding
Qian et al. Optical flow based step length estimation for indoor pedestrian navigation on a smartphone
CN112729283A (en) Navigation method based on depth camera/MEMS inertial navigation/odometer combination
CN106767770A (en) A kind of detection of user's direction of travel and method for tracing based on portable intelligent equipment
Lategahn et al. Robust pedestrian localization in indoor environments with an IMU aided TDoA system

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