CN111795694A - Indoor positioning electromagnetic calibration method for fire rescue - Google Patents

Indoor positioning electromagnetic calibration method for fire rescue Download PDF

Info

Publication number
CN111795694A
CN111795694A CN202010269524.6A CN202010269524A CN111795694A CN 111795694 A CN111795694 A CN 111795694A CN 202010269524 A CN202010269524 A CN 202010269524A CN 111795694 A CN111795694 A CN 111795694A
Authority
CN
China
Prior art keywords
error
speed
navigation
zero
equation
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
CN202010269524.6A
Other languages
Chinese (zh)
Other versions
CN111795694B (en
Inventor
谢乐涛
荣建忠
张泽江
赵汉高
李明轩
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.)
Shenzhen Qianshou Qianyan Technology Co ltd
Sichuan Fire Research Institute of Emergency Management Department
Original Assignee
Shenzhen Qianshou Qianyan Technology Co ltd
Sichuan Fire Research Institute of Emergency Management Department
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 Shenzhen Qianshou Qianyan Technology Co ltd, Sichuan Fire Research Institute of Emergency Management Department filed Critical Shenzhen Qianshou Qianyan Technology Co ltd
Priority to CN202010269524.6A priority Critical patent/CN111795694B/en
Publication of CN111795694A publication Critical patent/CN111795694A/en
Application granted granted Critical
Publication of CN111795694B publication Critical patent/CN111795694B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Manufacturing & Machinery (AREA)
  • Navigation (AREA)

Abstract

The invention discloses an indoor positioning electromagnetic calibration method for fire rescue, which mainly comprises the following steps: firstly, an integrated navigation system consisting of an electromagnetic calibration system and an inertial navigation system is adopted to respectively establish an inertial navigation error system equation and an electromagnetic calibration error system equation, combining the two equations, obtaining a system equation through discretization operation, dynamically correcting each parameter, substituting the corrected data into the final system equation, the invention adopts a loose combination mode and utilizes the state design of an indirect polarity combination system, and correcting by feedback correction, taking the error delta X of the navigation parameter X output by the small positioning unit as the main state variable of the filter, wherein the main part of the estimated value of the filter is the estimated value delta X of the navigation parameter, then, the X is corrected by the delta X, so that the indoor accurate positioning of the fire fighter is realized, the real-time accurate monitoring of the fire fighter is realized, the fire rescue efficiency is improved, and the life safety of the fire fighter is ensured.

Description

Indoor positioning electromagnetic calibration method for fire rescue
Technical Field
The invention relates to the technical field of positioning, in particular to an indoor positioning electromagnetic calibration method for fire rescue.
Background
When emergency events such as fire dangerous situations occur, fire fighters are required to perform emergency treatment on the emergency events such as the fire dangerous situations, and when the emergency event treatment is performed, in order to ensure the safety of the fire fighters, the action positions of the fire fighters are required to be positioned and monitored, and in the existing positioning technology, an inertial navigation system cannot be accurately positioned for a long time in an indoor environment, so that a stable real-time navigation positioning system is urgently needed to make up the defects of the inertial navigation system. The problem can be solved just by the combined navigation system formed by combining the electromagnetic calibration system and the inertial navigation system, the inertial navigation system and the electromagnetic calibration system have strong complementarity, and the combination of the two systems not only can give full play to respective advantages, but also can make up respective defects.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the indoor positioning electromagnetic calibration method for fire rescue is provided, so that indoor accurate positioning of fire fighters is realized, real-time accurate monitoring of the fire fighters is realized, efficiency of the fire rescue is improved, and life safety of the fire fighters is guaranteed.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an indoor positioning electromagnetic calibration method for fire rescue comprises the following steps:
s1: an integrated navigation system consisting of an electromagnetic calibration system and an inertial navigation system is adopted;
s2: establishing an inertial navigation system error state equation;
and the northeast fill coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
Figure BDA0002442590040000011
in the formula, the state vector of the position and speed combination system is:
Figure BDA0002442590040000012
wherein the angle includes an attitude error angle phiE、φN、φDVelocity error V of navigation systemE、VN、VDPosition error Xn、Yn、ZnRandom constant drift of gyroX、Y、ZAnd zero offset of accelerometer
Figure BDA0002442590040000021
Figure BDA0002442590040000022
WI(t) is the system process white noise matrix, FI(t) is the system state matrix, GI(t) is a system noise propagation matrix;
s3: establishing an error equation of an electromagnetic calibration system;
Figure BDA0002442590040000023
in the formula, XE(t)＝[lX,lY,lZ,lrE,lrN,lrD]T
Wherein, WE(t) is the system process white noise matrix, FE(t) is the system state matrix, GE(t) is the system noise propagation matrix,/X,lY,lZFor positional error,/rE,lrN,lrDIs the speed error;
s4: merging the error state equation of the navigation system and the error equation of the electromagnetic calibration system to obtain a state equation of a combined system:
Figure BDA0002442590040000024
s5: and taking the difference between the speed and the position as measurement information of the system, and establishing a system measurement equation:
Z(t)＝H(t)X(t)+V(t)
wherein Z is [ V ]E,VN,VU,Xn,Yn]TIs an observation vector, is the difference between the two system velocities and positions, H (t) is the system observation matrix, V (t) is the measurement noise of the velocities and positions;
s6: calculating by adopting a discrete state through a Kalman filter to obtain an integrated navigation system equation:
Figure BDA0002442590040000025
taking the sampling time as T, the discretized system equation is as follows:
Figure BDA0002442590040000026
in the formula phik,k-1For one-step transition matrix of order n x n from K-1 to K, Gk-1Is a system noise matrix of order n x r, HkM x n order system measurement matrix for K time, Wk-1R-dimensional System noise, V, for the time K-1kM-dimensional measurement noise at the time K;
furthermore, the dynamic correction of the position comprises the following correction method: and substituting the measured position information into a combined navigation calculation equation to replace the calculated position information, and correcting the corresponding position error.
Further, the method for dynamically correcting the speed comprises the following steps: a zero-speed correction technology is introduced to correct the error information of the indoor positioning system on line, when the moving speed of the indoor positioning system is zero, a Kalman filter is designed to filter and estimate navigation position, speed and attitude errors in the current time period by utilizing the moment that feet are completely contacted with the ground when a person walks, and the positioning precision of the system is improved through feedback correction; the specific execution algorithm is as follows: detecting the output value of the Y-axis gyroscope to judge a zero-speed interval, and setting a speed error to zero when the zero-speed interval is detected, wherein the speed error is the speed resolved by the MIMU in the zero-speed interval; in order to fully utilize the speed error obtained in the zero-speed detection to estimate more error parameters, the zero-speed correction information is substituted into a Kalman filter, the speed error V solved by the MIMU in the zero-speed interval is used as an observed quantity, the observed quantity is input into the Kalman filter to carry out state error estimation, and the result is fed back to a system to carry out error correction.
Further, the dynamic correction of azimuth angle, its correction azimuth is: and the magnetometer or the satellite navigation can provide azimuth information, and when the accumulated error of the navigation azimuth is overlarge, the measured azimuth information is used for replacing navigation calculation azimuth information, and the corresponding azimuth error is corrected.
Further, the dynamic correction of the horizontal attitude angle comprises the following correction method: when the motion acceleration is zero, utilizing the vector sum information of the three-axis accelerometer and the angular velocity information of the gyroscope, and when the vector sum of the three-axis accelerometer is g and the angular velocity of the gyroscope is zero, utilizing the vector information of the three-axis accelerometer to calculate a horizontal attitude angle, and when the evaluation error of the horizontal attitude angle is smaller than the error of the navigation calculation horizontal attitude angle, replacing the navigation calculation horizontal attitude angle with the horizontal attitude angle.
Compared with the prior art, the invention has the following beneficial effects:
the invention establishes an inertial navigation system error state equation and an electromagnetic calibration system error equation, combines the two equations to obtain a combined system state equation, dynamically corrects each parameter, and brings corrected data information into the combined system state equation to realize the calibration of data errors.
Detailed Description
The present invention is further illustrated by the following examples, which include, but are not limited to, the following examples.
The invention makes up the deficiency of the inertial navigation system through the stable real-time navigation positioning system, realizes the accurate correction of the indoor positioning system and ensures the positioning accuracy. The realization method comprises the following steps: the method adopts a loose combination mode, utilizes the state design of an indirect polar combination system, and corrects by feedback correction, namely, an error delta X of a navigation parameter X output by a small positioning unit is used as a main state variable of a filter, the main part of an estimated value of the filter is the error estimated value delta X of the navigation parameter, and then the error delta X is used for correcting the X.
The specific calibration method comprises the following steps:
s1: an integrated navigation system consisting of an electromagnetic calibration system and an inertial navigation system is adopted;
s2: establishing an inertial navigation system error state equation;
and the northeast fill coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
Figure BDA0002442590040000041
in the formula, the state vector of the position and speed combination system is:
Figure BDA0002442590040000042
wherein the angle includes an attitude error angle phiE、φN、φDVelocity error V of navigation systemE、VN、VDPosition error Xn、Yn、ZnRandom constant drift of gyroX、Y、ZAnd zero offset of accelerometer
Figure BDA0002442590040000043
Figure BDA0002442590040000044
WI(t) is the system process white noise matrix, FI(t) is a systemState matrix, GI(t) is a system noise propagation matrix;
s3: establishing an error equation of an electromagnetic calibration system;
Figure BDA0002442590040000045
in the formula, XE(t)＝[lX,lY,lZ,lrE,lrN,lrD]T
Wherein, WE(t) is the system process white noise matrix, FE(t) is the system state matrix, GE(t) is the system noise propagation matrix,/X,lY,lZFor positional error,/rE,lrN,lrDIs the speed error;
s4: merging the error state equation of the navigation system and the error equation of the electromagnetic calibration system to obtain a state equation of a combined system:
Figure BDA0002442590040000046
s5: and taking the difference between the speed and the position as measurement information of the system, and establishing a system measurement equation:
Z(t)＝H(t)X(t)+V(t)
wherein Z is [ V ]E,VN,VU,Xn,Yn]TIs an observation vector, is the difference between the two system velocities and positions, H (t) is the system observation matrix, V (t) is the measurement noise of the velocities and positions;
s6: calculating by adopting a discrete state through a Kalman filter to obtain an integrated navigation system equation:
Figure BDA0002442590040000051
taking the sampling time as T, the discretized system equation is as follows:
Figure BDA0002442590040000052
in the formula phik,k-1For one-step transition matrix of order n x n from K-1 to K, Gk-1Is a system noise matrix of order n x r, HkM x n order system measurement matrix for K time, Wk-1R-dimensional System noise, V, for the time K-1kThe noise is measured for m dimensions at time K.
When parameter substitution operation is carried out, dynamic correction is carried out on each parameter, the dynamic correction algorithm mainly corrects four items of data, namely position, speed, azimuth angle and horizontal attitude angle, the correction data are substituted into the combined filtering algorithm, combined navigation precision can be rapidly improved in a short time in the dynamic navigation process, and accumulated errors are reduced. The specific correction algorithm is designed as follows:
and (3) position dynamic correction: the correction condition is that the navigation data and the height data of the barometric altimeter are accurate, the measured position information is substituted into the integrated navigation calculation equation to replace the calculated position information, and the corresponding position error is corrected.
And (3) dynamic speed correction: and introducing a zero-speed correction technology to perform online correction on other error information of the indoor positioning system, wherein the correction condition is that the moving speed of the indoor positioning system is zero. The moment when the foot is completely contacted with the ground when a person walks is utilized, a Kalman filter is designed to filter and estimate the navigation position, the speed and the attitude error in the time period, and the positioning precision of the system is improved through feedback correction. The specific execution algorithm is as follows: and detecting the output value of the Y-axis gyroscope to judge a zero-speed interval, and setting a speed error to be zero when the zero-speed interval is detected, wherein the speed error refers to the speed calculated by the MIMU in the zero-speed interval. In order to fully utilize the speed error obtained in the zero-speed detection to estimate more error parameters, the zero-speed correction information is substituted into a Kalman filter, the speed error V solved by the MIMU in the zero-speed interval is used as an observed quantity, the observed quantity is input into the Kalman filter to carry out state error estimation, and the result is fed back to a system to carry out error correction. The zero-speed correction has the advantages of simple and universal realization conditions and no need of data provided by easily interfered equipment such as satellite positioning, a magnetometer and the like.
And (3) azimuth correction: the correction condition is that the magnetometer is usable or the satellite navigation is usable, the magnetometer or the satellite navigation can provide azimuth information, when the accumulated error of the navigation azimuth is overlarge, the measured azimuth information is used for replacing the navigation calculation azimuth information, and the corresponding azimuth error is corrected.
Correcting a horizontal attitude angle: the correction condition is that the acceleration of motion is zero. And when the sum of the vectors of the three-axis accelerometers is g and the angular velocity of the gyroscope is zero, calculating a horizontal attitude angle by using the vector information of the three-axis accelerometers, and when the evaluation error of the horizontal attitude angle is less than the error of the navigation calculated horizontal attitude angle, replacing the navigation calculated horizontal attitude angle with the horizontal attitude angle. The correction process is similar to static coarse alignment, the implementation condition is simple, and the divergence of the horizontal attitude angle can be effectively inhibited.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the protection scope of the present invention, but all the insubstantial changes or modifications made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the protection scope of the present invention.

Claims (5)

1. An indoor positioning electromagnetic calibration method for fire rescue is characterized in that: the method comprises the following steps:
s1: an integrated navigation system consisting of an electromagnetic calibration system and an inertial navigation system is adopted;
s2: establishing an inertial navigation system error state equation;
and the northeast fill coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
Figure FDA0002442590030000011
in the formula, the state vector of the position and speed combination system is:
Figure FDA0002442590030000012
wherein the angle includes an attitude error angle phiE、φN、φDVelocity error V of navigation systemE、VN、VDPosition error Xn、Yn、ZnRandom constant drift of gyroX、Y、ZAnd zero offset of accelerometer
Figure FDA0002442590030000013
Figure FDA0002442590030000014
WI(t) is the system process white noise matrix, FI(t) is the system state matrix, GI(t) is a system noise propagation matrix;
s3: establishing an error equation of an electromagnetic calibration system;
Figure FDA0002442590030000015
in the formula, XE(t)＝[lX,lY,lZ,lrE,lrN,lrD]T
Wherein, WE(t) is the system process white noise matrix, FE(t) is the system state matrix, GE(t) is the system noise propagation matrix,/X,lY,lZFor positional error,/rE,lrN,lrDIs the speed error;
s4: merging the error state equation of the navigation system and the error equation of the electromagnetic calibration system to obtain the state equation of the combined system:
Figure FDA0002442590030000016
s5: and taking the difference between the speed and the position as measurement information of the system, and establishing a system measurement equation:
Z(t)＝H(t)X(t)+V(t)
wherein Z is [ V ]E,VN,VU,Xn,Yn]TIs an observation vector, is the difference between the two system velocities and positions, H (t) is the system observation matrix, V (t) is the measurement noise of the velocities and positions;
s6: calculating by adopting a discrete state through a Kalman filter to obtain an integrated navigation system equation:
Figure FDA0002442590030000021
taking the sampling time as T, the discretized system equation is as follows:
Figure FDA0002442590030000022
in the formula phik,k-1For one-step transition matrix of order n x n from K-1 to K, Gk-1Is a system noise matrix of order n x r, HkM x n order system measurement matrix for K time, Wk-1R-dimensional System noise, V, for the time K-1kThe noise is measured for m dimensions at time K.
2. An indoor positioning electromagnetic calibration method for fire rescue as defined in claim 1, wherein: the dynamic correction of the position comprises the following correction methods: and substituting the measured position information into a combined navigation calculation equation to replace the calculated position information, and correcting the corresponding position error.
3. An indoor positioning electromagnetic calibration method for fire rescue as defined in claim 1, wherein: the method for dynamically correcting the speed comprises the following steps: a zero-speed correction technology is introduced to correct the error information of the indoor positioning system on line, when the moving speed of the indoor positioning system is zero, a Kalman filter is designed to filter and estimate navigation position, speed and attitude errors in the time interval by utilizing the moment that feet are completely contacted with the ground when a person walks, and the positioning precision of the system is improved through feedback correction; the specific execution algorithm is as follows: detecting the output value of the Y-axis gyroscope to judge a zero-speed interval, and setting a speed error to zero when the zero-speed interval is detected, wherein the speed error is the speed resolved by the MIMU in the zero-speed interval; in order to fully utilize the speed error obtained in the zero-speed detection to estimate more error parameters, the zero-speed correction information is substituted into a Kalman filter, the speed error V solved by the MIMU in the zero-speed interval is used as an observed quantity, the observed quantity is input into the Kalman filter to carry out state error estimation, and the result is fed back to a system to carry out error correction.
4. An indoor positioning electromagnetic calibration method for fire rescue as defined in claim 1, wherein: the azimuth angle is dynamically corrected, and the corrected azimuth is as follows: and the magnetometer or the satellite navigation can provide azimuth information, and when the accumulated error of the navigation azimuth is overlarge, the measured azimuth information is used for replacing navigation calculation azimuth information, and the corresponding azimuth error is corrected.
5. An indoor positioning electromagnetic calibration method for fire rescue as defined in claim 1, wherein: the dynamic correction method for the horizontal attitude angle comprises the following steps: when the motion acceleration is zero, utilizing the vector sum information of the three-axis accelerometer and the angular velocity information of the gyroscope, and when the vector sum of the three-axis accelerometer is g and the angular velocity of the gyroscope is zero, utilizing the vector information of the three-axis accelerometer to calculate a horizontal attitude angle, and when the evaluation error of the horizontal attitude angle is smaller than the error of the navigation calculation horizontal attitude angle, replacing the navigation calculation horizontal attitude angle with the horizontal attitude angle.
CN202010269524.6A 2020-04-08 2020-04-08 Indoor positioning electromagnetic calibration method for fire rescue Active CN111795694B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010269524.6A CN111795694B (en) 2020-04-08 2020-04-08 Indoor positioning electromagnetic calibration method for fire rescue

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010269524.6A CN111795694B (en) 2020-04-08 2020-04-08 Indoor positioning electromagnetic calibration method for fire rescue

Publications (2)

Publication Number Publication Date
CN111795694A true CN111795694A (en) 2020-10-20
CN111795694B CN111795694B (en) 2022-05-10

Family

ID=72806133

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010269524.6A Active CN111795694B (en) 2020-04-08 2020-04-08 Indoor positioning electromagnetic calibration method for fire rescue

Country Status (1)

Country Link
CN (1) CN111795694B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102010031350A1 (en) * 2010-07-14 2012-01-19 Bayerische Motoren Werke Aktiengesellschaft Method for determining position of person in e.g. pedestrian navigation, involves determining value of motion based on determined motion class, and using value of motion parameter for determining estimated actual position of person
CN104596504A (en) * 2015-01-30 2015-05-06 中国科学院上海高等研究院 Method and system for quickly setting up map to assist indoor positioning under emergency rescue scene
KR101576424B1 (en) * 2015-06-16 2015-12-10 코디스페이스 주식회사 Automatic calibration method of magnetometer for indoor positioning
CN107655476A (en) * 2017-08-21 2018-02-02 南京航空航天大学 Pedestrian's high accuracy foot navigation algorithm based on Multi-information acquisition compensation
CN109612464A (en) * 2018-11-23 2019-04-12 南京航空航天大学 Indoor navigation system and method based on more algorithms enhancing under IEZ frame
CN110553646A (en) * 2019-07-30 2019-12-10 南京林业大学 Pedestrian navigation method based on inertia, magnetic heading and zero-speed correction

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102010031350A1 (en) * 2010-07-14 2012-01-19 Bayerische Motoren Werke Aktiengesellschaft Method for determining position of person in e.g. pedestrian navigation, involves determining value of motion based on determined motion class, and using value of motion parameter for determining estimated actual position of person
CN104596504A (en) * 2015-01-30 2015-05-06 中国科学院上海高等研究院 Method and system for quickly setting up map to assist indoor positioning under emergency rescue scene
KR101576424B1 (en) * 2015-06-16 2015-12-10 코디스페이스 주식회사 Automatic calibration method of magnetometer for indoor positioning
CN107655476A (en) * 2017-08-21 2018-02-02 南京航空航天大学 Pedestrian's high accuracy foot navigation algorithm based on Multi-information acquisition compensation
CN109612464A (en) * 2018-11-23 2019-04-12 南京航空航天大学 Indoor navigation system and method based on more algorithms enhancing under IEZ frame
CN110553646A (en) * 2019-07-30 2019-12-10 南京林业大学 Pedestrian navigation method based on inertia, magnetic heading and zero-speed correction

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
朱晨光: "消防救援室内自主三维空间定位系统初探", 《消防技术与产品信息》 *

Also Published As

Publication number Publication date
CN111795694B (en) 2022-05-10

Similar Documents

Publication Publication Date Title
Jiménez et al. Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU
CN105043385B (en) A kind of method for adaptive kalman filtering of pedestrian's Camera calibration
US10132829B2 (en) Heading confidence interval estimation
US8010308B1 (en) Inertial measurement system with self correction
EP1653194B1 (en) Azimuth/attitude detecting sensor
CN108931244B (en) Inertial navigation error suppression method and system based on train motion constraint
CN108106635A (en) Inertia defends the anti-interference posture course calibration method of long endurance for leading integrated navigation system
CN106996780B (en) Course error correction method and device and magnetic field detection method and device
CN111896007B (en) Attitude calculation method for quadruped robot for compensating foot-ground impact
JP2010019703A (en) Positioning device for mobile body
CN104880189B (en) A kind of antenna for satellite communication in motion low cost tracking anti-interference method
CN109579836A (en) A kind of indoor pedestrian's bearing calibration method based on MEMS inertial navigation
JP2008116370A (en) Mobile location positioning device
CN106403952A (en) Method for measuring combined attitudes of Satcom on the move with low cost
Sokolović et al. INS/GPS navigation system based on MEMS technologies
Sokolovic et al. Integration of INS, GPS, magnetometer and barometer for improving accuracy navigation of the vehicle
US20160370188A1 (en) Inertial device, control method and program
JP2021179438A (en) System and method for compensating for absence of sensor measurement in heading measuring system
CN112362057A (en) Inertial pedestrian navigation algorithm based on zero-speed correction and attitude self-observation
Kim et al. Performance improvement and height estimation of pedestrian dead-reckoning system using a low cost MEMS sensor
CN108416387B (en) Height filtering method based on fusion data of GPS and barometer
RU2443978C1 (en) Method of determining spatial coordinates of mobile objects and integrated navigation system for realising said method
CN112033405B (en) Indoor environment magnetic anomaly real-time correction and navigation method and device
Ercan et al. Multi-sensor data fusion of DCM based orientation estimation for land vehicles
CN111795694B (en) Indoor positioning electromagnetic calibration method for fire rescue

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