CN111795694B - Indoor positioning electromagnetic calibration method for fire rescue - Google Patents
Indoor positioning electromagnetic calibration method for fire rescue Download PDFInfo
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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
- G01C21/165—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
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- 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
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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
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 danger and the like occur, emergency treatment is required to be carried out on the emergency events such as the fire danger and the like by fire fighters, and when the emergency treatment is carried out, the action positions of the fire fighters are required to be positioned and monitored in order to ensure the safety of the fire fighters. 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 coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
in the formula, the state vector of the position and speed combination system is:
XI(t)＝[φe φn φu δVe δVn δVu δXn δYn δZn εx εy εz ▽x ▽y ▽z]T
wherein the angle includes an attitude error angle phie、φn、φuVelocity error δ V of navigation systeme、δVn、δVuPosition error δ Xn、δYn、δZnRandom constant drift epsilon of gyrox、εy、εzAnd accelerometer zero bias +x、▽y、▽z，WI(t) is the system process white noise matrix, FI(t) is in the form of a systemState matrix, GI(t) is a system noise propagation matrix;
s3: establishing an error equation of an electromagnetic calibration system;
in the formula, XE(t)＝[δlX,δlY,δlZ,δlrE,δlrN,δlrD]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, δ lX,δlY,δlZFor position error, δ lrE,δlrN,δlrDIs 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:
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 [ δ V ]e,δVn,δVu,δXn,δYn， δ Zn ]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:
if the sampling time is T, the discretized system equation is as follows:
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 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 delta 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 coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
in the formula, the state vector of the position and speed combination system is:
XI(t)＝[φe φn φu δVe δVn δVu δXn δYn δZn εx εy εz ▽x ▽y ▽z]T
wherein the angle includes an attitude error angle phie、φn、φuVelocity error δ V of navigation systeme、δVn、δVuPosition error δ Xn、δYn、δZnRandom constant drift epsilon of gyrox、εy、εzAnd accelerometer zero bias +x、▽y、▽z，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;
in the formula, XE(t)＝[δlX,δlY,δlZ,δlrE,δlrN,δlrD]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, δ lX,δlY,δlZFor position error, δ lrE,δlrN,δlrDIs 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:
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 [ δ V ]e,δVn,δVu,δXn,δYn， δ Zn ]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:
taking the sampling time as T, the discretized system equation is as follows:
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, the 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 altitude 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) speed dynamic 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 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 delta 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 calculating a horizontal attitude angle by using the vector sum information of the three-axis accelerometer and the gyro angular velocity information, when the vector sum of the three-axis accelerometer is g and the gyro angular velocity is zero, and when the horizontal attitude angle evaluation error is smaller than the navigation calculation horizontal attitude angle error, replacing the navigation calculation 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 scope of the present invention, but all the insubstantial modifications or changes 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 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 coordinate system is adopted as a navigation coordinate system, and the error state equation is expressed as follows:
in the formula, the state vector of the position and speed combination system is:
wherein the angle includes an attitude error angle phie、φn、φuVelocity error δ V of navigation systeme、δVn、δVuPosition error δ Xn、δYn、δZnRandom constant drift epsilon of gyrox、εy、εzAnd zero offset of accelerometer
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;
in the formula, XE(t)＝[δlX,δlY,δlZ,δlrE,δlrN,δlrD]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, δ lX,δlY,δlZFor position error, δ lrE,δlrN,δlrDIs 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:
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 [ δ V ]e,δVn,δVu,δXn,δYn， δ Zn ]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:
taking the sampling time as T, the discretized system equation is as follows:
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 delta 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.
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