CN106908060A - A kind of high accuracy indoor orientation method based on MEMS inertial sensor - Google Patents
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Abstract
The invention discloses a kind of high accuracy indoor orientation method based on MEMS inertial sensor, comprise the following steps：(1) MEMS inertial sensor is connected firmly on pedestrian's pin, MEMS inertial sensor is sensed the motion state of foot, foot navigation information is obtained in real time, and realize transmitting by bluetooth；(2) the hand-held Android mobile phone of pedestrian, Android client real-time reception and preserve MEMS inertial sensor offer data；(3) denoising is carried out to data；(4) zero velocity interval is obtained using zero-speed detection algorithm first, error correction is secondly carried out using zero-velocity curve algorithm bonding state algorithm for estimating；(5) Android client is shown by the data after error correction and compensation in real time by user interface.The present invention does not need extra auxiliary foundation to set；Good positioning precision can be kept to various complicated motion states in indoor positioning；Corrected in real time using mobile terminal and compensate error, run trace is shown by User Interface in real time.
Description
Technical Field
The invention relates to an indoor positioning method, in particular to a high-precision indoor positioning method based on an MEMS inertial sensor without installing additional infrastructure.
Background
Lbs (location based services) is becoming more and more important due to the development of the internet, the popularity of mobile devices and personal devices, and users obtain location information and use it for services such as navigation, tracking, monitoring, information push, and the like. The GPS can conveniently provide outdoor personal positioning information, but because the GPS needs to receive at least 4 satellites to realize positioning, the positioning effect is greatly affected by the received satellite signals, and the positioning effect is seriously affected due to the fact that the GPS cannot receive the satellite signals under an indoor environment.
The inertial navigation system has the advantages of strong autonomy, high output frequency, high short-time precision and the like, and particularly, the MEMSIMU is rapidly developed in recent years, so that the inertial navigation system becomes small in size and low in cost. The advantage of autonomous navigation can be well played by using a mode of Pedestrian Dead Reckoning (PDR) for indoor positioning of the MEMS IMU. From the current indoor positioning research condition, the MEMS indoor positioning is a big research hotspot already abroad, the condition of error accumulation of the MEMS inertial sensor can be well inhibited by judging the motion state of a carrier firstly and then detecting a zero-speed interval through information output by the MEMS inertial sensor, and further performing zero-speed correction (zero velocity Update), meanwhile, the initial alignment can not be performed according to the high-precision inertial navigation system initial alignment method due to the low precision of the low-cost MEMS IMU gyro, and the magnetometer is stable compared with the gyro, so that the magnetometer is introduced to assist course alignment and provide course compensation.
In the process of indoor positioning of the MEMS inertial sensor, zero-speed correction is adopted to correct errors accumulated in a walking period, so that the process has important significance in the process of improving the positioning accuracy of the MEMS inertial sensor, the zero-speed correction accuracy is directly determined by detecting a zero-speed interval in a walking process, a threshold value is set for detection methods of data searched at home and abroad about the zero-speed interval, corresponding detection quantity is compared with the threshold value, the selection of the threshold value is frequently one-sided and can only aim at a certain specific motion state, indoor footsteps can have various complex motion states such as walking, running, going upstairs, going downstairs and the like, and therefore the detection accuracy needs to be improved urgently.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention aims to provide a high-precision indoor positioning method based on a MEMS inertial sensor, which is suitable for autonomous positioning in an indoor environment and does not need additional installation of infrastructure.
The technical scheme is as follows: in order to realize the purpose of the invention, the following technical scheme is adopted: based on MEMS inertial sensor
The high-precision indoor positioning method comprises the following steps:
(1) the MEMS inertial sensor is fixedly connected to the foot of the pedestrian, so that the MEMS inertial sensor senses the motion state of the foot and acquires navigation information of the foot in real time, and the navigation information is transmitted through the Bluetooth transmission module;
(2) the pedestrian holds the android mobile phone, receives data provided by the MEMS inertial sensor for navigating the foot motion state in real time at an android client, and stores the data;
(3) denoising the data;
(4) aiming at the problem of low positioning accuracy of the MEMS inertial sensor, firstly, a zero-speed detection algorithm is adopted to obtain a zero-speed interval, and secondly, a zero-speed correction algorithm is adopted to be combined with a state estimation algorithm to perform error correction so as to improve the positioning accuracy; the zero-speed interval detection method comprises the following steps of acquiring data transmitted by the MEMS inertial sensor through an android client, and detecting the zero-speed interval by adopting a sliding window method:
(a) according to the noise characteristics of an accelerometer and a gyroscope of the MEMS inertial sensor, repeated experiments are carried out in different motion states to obtain zero-speed interval thresholds in the corresponding motion states, wherein the different motion states comprise normal walking, running, climbing stairs or descending stairs;
(b) according to the acceleration information of the accelerometer acquired by the android client, firstly, the motion state of the footstep at the moment is determined by analyzing the characteristics of the acceleration in the sliding window and comparing the change of the acceleration at the adjacent moments and the conditions of the maximum value and the minimum value of the acceleration in the window, and the threshold value of the zero-speed detection corresponding to the motion state is automatically selected;
(c) respectively judging whether the specific force module value, the specific force variance and the angular velocity module value are within a threshold range in the sliding window interval, if the specific force module value, the specific force variance and the angular velocity module value simultaneously meet a threshold condition, judging that the interval is a zero-velocity interval, and after obtaining the zero-velocity interval, determining that the interval is an interval in which the foot is at zero velocity;
at the moment, zero-speed correction is adopted in combination with an adaptive Kalman filtering algorithm, and errors accumulated in a walking interval are eliminated.
(5) And the android client displays the data subjected to error correction and compensation in real time through the user interface display module, and presents the motion state to the user in real time.
The data provided by the MEMS inertial sensor comprises data measured by a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer. The android client has the function of calibrating the data, and can record the data and process the data in real time.
The android client has the function of displaying the walking track of the user in real time.
In the step (3), aiming at the noise problem generated when the feet of the MEMS inertial sensor are severely shaken, the denoising processing method is wavelet denoising, and can remove noise and interference signals contained in high-frequency signals.
In the step (4), aiming at the problem that the error of the low-cost inertial sensor is easy to disperse along with the time, the state estimation algorithm is an adaptive Kalman filtering algorithm, and the step of correcting the error by adopting a zero-speed correction algorithm and combining the state estimation algorithm is as follows:
the accurate zero-speed interval can be obtained through the zero-speed detection algorithm, the speed is zero in the zero-speed interval process, and therefore the speed output by navigation calculation is the speed error. The relationship between the speed error and the gyro drift, the acceleration zero offset and the attitude angle error can be described by an error differential equation. A Kalman filtering equation is established on the basis of an error equation, the speed error is used as an observed quantity, and an attitude angle error, a position error and other error quantities are estimated and fed back to a navigation resolving module, so that the purpose of correcting the error of the system is achieved.
Error equation of attitude angle
Equation of speed error
Equation of position error
A Kalman filtering equation is established on the basis of an error equation, the speed error is used as an observed quantity, an attitude angle error, a position error and other differential quantities are estimated and fed back to a navigation resolving module,
selecting the state quantity of the Kalman filter as follows:
whereinIndicating attitude angle error, vnIndicating a speed error; p is a radical ofnIndicating a position error; the gyro is represented by a constant drift of the gyro,the system equation representing the accelerometer constant zero offset, Kalman is expressed as
X(t)＝FX(t)+W(t)
Measuring velocity errors of equations, i.e.
Zk＝[vn]＝HkXk
The measurement can be obtained only in the zero-speed interval, the measurement is related to the specific motion and interference conditions, and after the zero-speed interval is detected by the zero-speed detection algorithm, the Kalman filtering updates the time and the measurement, and feeds the estimated error back to the system for error compensation.
The MEMS inertial sensor and the Bluetooth module adopt a patch type hardware design method, and have the advantage of small volume.
Has the advantages that: according to the human body walking dynamics model, the MEMS inertial sensor with low cost is adopted, the system has the advantages of small volume and light weight, and compared with a wireless positioning mode, the system structure can realize autonomous positioning without additional auxiliary foundation arrangement; the invention adopts a self-adaptive threshold matching method, can keep good positioning precision for various complicated motion states of footsteps in the indoor positioning process, and has stronger popularity; aiming at the current popular mobile terminal, the mobile terminal is designed to receive the measurement data of the inertial sensor transmitted by the Bluetooth in real time, has the functions of storing and processing the data, corrects and compensates errors of the data in real time, and simultaneously designs a user interaction interface to display the walking track of a user in real time.
Drawings
FIG. 1 is a diagram of a MEMS inertial sensor indoor positioning system;
FIG. 2 is a graph of a MEMS inertial sensor before denoising and after denoising smoothing;
FIG. 3 is a flow chart of a zero-velocity interval detection method;
FIG. 4 is a diagram of the detection effect of the zero-speed interval;
FIG. 5 is an android client interface;
FIG. 6 is a two-dimensional planar walking trajectory diagram of the present invention;
fig. 7 is a three-dimensional up and down stair walking track diagram of the invention.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the following examples and accompanying drawings.
As shown in fig. 1, the high-precision indoor positioning apparatus employed in the present invention includes: low-cost MEMS inertial sensors such as MPU9260, MPU6050 and the like, a Bluetooth transmission module and an android client; the android client comprises a data calibration module, a data storage module, a data processing module and a user interface display module. The MEMS inertial sensor is fixedly connected to the foot, the change of the step state is sensed, the measured parameters are transmitted through the Bluetooth transmission module, and the data transmitted by the Bluetooth transmission module are received through the android client. The android client firstly calibrates the three-axis accelerometer, the three-axis gyroscope and the three-axis magnetometer respectively through the data calibration module, stores and processes data in real time, and simultaneously displays the data on a user interface in real time. The MEMS inertial sensor and the Bluetooth module both adopt a surface mount hardware design method, and have the advantages of compact structure and small volume.
The method comprises the following concrete steps:
(1) the low-cost inertial sensor is fixedly connected to the foot, so that the inertial sensor can sense the motion state of the foot, the MEMS inertial sensor can normally output information of the three-axis accelerometer, the three-axis gyroscope and the three-axis magnetometer, navigation information of the foot can be acquired in real time, and the navigation information is transmitted through the Bluetooth transmission module.
(2) The android mobile phone is held by a pedestrian, data transmitted by a foot inertial sensor is received at an android client in real time, a data calibration module of the android client is firstly opened to respectively calibrate a three-axis accelerometer, a three-axis gyroscope and measurement data of the three-axis magnetometer are calibrated, the accelerometer is removed for the main purpose of calibration, the gyroscope and the zero offset of the magnetometer, the calibration method of the accelerometer and the gyroscope is to level the MEMS inertial sensor, when the zero offset displayed by the calibration module does not change any more, the zero offset at the moment is stored, namely the calibration of the accelerometer and the gyroscope is completed, the MEMS inertial sensor is repeatedly rotated around different directions for the calibration of a magnetic field, and the calibration of the magnetic field is completed after the zero offset of the magnetic field is displayed to change no more by multiple movements. And after the calibration of the data is finished, whether the accelerometer and the gyroscope are zero or not is judged when the MEMS inertial sensor is in a static state, and the data is stored.
(3) The wavelet denoising processing is performed on the obtained data, as shown in fig. 2, which is a graph comparing the data measured by the MEMS inertial sensor and the curve of the data after wavelet denoising.
(4) The data of the MEMS inertial sensor is detected in a zero-speed interval by adopting a sliding window method, and the detection method of the zero-speed interval is shown in figure 3.
The zero-speed detection algorithm plays an important role in the whole MEMS positioning process and directly determines the positioning precision. The identification method comprises the following steps:
calculating the triaxial acceleration module value at each sampling moment i:
selecting a sliding window with the size of 2N +1, wherein N represents the window width, and the acceleration variance in the window is as follows:
wherein,
scanning acceleration data at regular intervals, identifying the state that the pace in the period of time is normal walking, slow speed, fast walking or going upstairs and downstairs and the like through the difference between the maximum value and the minimum value of the scanned acceleration, and determining the acceleration threshold values of the normal walking, the slow speed, the fast walking or the going upstairs and downstairs by multiple tests to respectively take tha1、tha2、tha3、tha4(ii) a Threshold angular velocity is taken thw1、thw2、thw3、thw4(ii) a Covariance th of accelerationσ1、thσ2、thσ3、thσ4。
Above mainly because the motion state identification method of acceleration modulus value also can discern based on angular velocity or acceleration covariance method, after discerning the motion state, carries out zero velocity interval and detects, in order to improve the precision that detects, according to the motion state of discerning above, relies on single element to detect must have the error, and this embodiment adopts the method that the three combines to detect:
the first condition is as follows: modulus of proportionalityWhen the feet are at the zero-speed moment, the output result of the acceleration measured by the MEMS inertial sensor fixed on the feet theoretically has only the local gravity acceleration value, the acceleration accords with chi-square distribution, the judgment can be carried out only by setting a proper confidence interval, and the assumption is that the judgment can be carried out on the condition that the acceleration accords with chi-square distributionThe minimum value and the maximum value of the confidence interval are respectively;
where 1 indicates a stationary state is detected and 0 indicates a moving state.
And a second condition: variance of specific force. The sudden change stage of the signal in the gait cycle can be effectively detected by observing the variance of the specific force through the sliding window. The variance variation of the proportion in the zero-velocity interval is weak and hardly fluctuates, and the step variation in the non-zero-velocity interval has strong fluctuation, so that the zero-velocity interval can be judged by the specific force variance being smaller than a given threshold value. The calculation is as follows:
wherein,is a specific force average value; m is the size of the sliding window and is related to the output frequency;
where 1 indicates a stationary state is detected and 0 indicates a moving state.
And (3) carrying out a third condition: the angular velocity modulus. Because the sole part keeps a complete contact state with the ground in the zero-speed interval, the angular speed and the change value thereof tend to zero in the zero-speed interval, and the angular speed has stronger fluctuation at the time outside the zero-speed interval, so that the corresponding angular speed threshold th can be determined through the identified walking statewTo determine a zero-speed interval in which the angular velocity module value is
Where 1 indicates a stationary state is detected and 0 indicates a moving state.
And when the condition I, the condition II and the condition III are simultaneously satisfied, recording that the state is 1 at the moment, the zero speed interval is required to be detected, and if the state is 0, the motion state is indicated.
Fig. 4 shows the zero velocity interval detected by the above-mentioned zero velocity interval detection method.
When the zero-speed interval is detected by the method, the error accumulated by the MEMS inertial sensor in a walking period is corrected in the zero-speed interval so as to compensate the whole positioning system.
The accurate zero-speed interval can be obtained through the zero-speed detection algorithm, the speed is zero in the zero-speed interval process, and therefore the speed output by navigation calculation is the speed error. The relationship between the speed error and the gyro drift, the acceleration zero offset and the attitude angle error can be described by an error differential equation. A Kalman filtering equation can be established on the basis of an error equation, the speed error is used as an observed quantity, an attitude angle error is estimated, and a position error and other error quantities are fed back to a navigation resolving module, so that the purpose of correcting the error of the system is achieved.
(a) Equation of attitude error
(b) Error in velocity
(c) Equation of position error
Selecting the state quantity of the Kalman filter as follows:
whereinIndicating the attitude error angle, vnIndicating a speed error; p is a radical ofnIndicating a position error; the gyro is represented by a constant drift of the gyro,representing that the accelerometer has a constant value of zero offset, theoretically, the influence factors of the states should be considered as many as possible, the more the factors are considered, the higher the accuracy of the navigation system estimation is, but the order of the system model is increased[11]The system of Kalman equations may be expressed as
X(t)＝FX(t)+W(t)
Measuring velocity errors of equations, i.e.
Zk＝[vn]＝HkXk
Measurement of v quantitykThe Kalman filtering method is characterized in that the Kalman filtering method only can be obtained in a zero-speed interval, the measurement is related to specific motion and interference conditions, after the zero-speed interval is detected through the zero-speed detection algorithm, time updating and measurement updating are carried out through Kalman filtering, estimated errors are fed back to a system, and error compensation is carried out
(5) And displaying the corrected data in real time on a user interface display module, so that a user can clearly see the motion state of the user.
The invention relates to an independent autonomous high-precision indoor positioning method, and the beneficial effects of the invention are verified through partial experiments.
In order to verify the effectiveness of the method, a normal two-dimensional plane walking experiment and a three-dimensional stair-climbing walking experiment are respectively carried out by using a low-cost MEMS MPU9250, data transmitted by an MEMS sensor through Bluetooth are received in real time by a handheld android mobile phone, and an android client interface of the mobile phone is shown in fig. 5.
Experiment one: two-dimensional plane normal walking experiment
In the experimental environment, a corridor of a student building is selected to be reserved in the south-east university, wherein the corridor is in a rectangular shape with the length of 5m and the width of 1 m.
Fig. 6 is a walking trajectory diagram processed by the algorithm of the present invention for data received by an android client, where the walking trajectory is a closed rectangular curve under an ideal situation without error, and a start point and an end point are coincident, but due to the error, the start point and the end point are often misaligned. The positioning accuracy is judged according to the misalignment of the two, and the positioning accuracy of the two-dimensional walking experiment is within 0.5 m.
Experiment two: three-dimensional stair-climbing walking experiment
The experimental environment is selected in the central east-south university building, the starting point is the 2 nd th floor of the central building, the walking route is the position from the right side stair on the 2 nd floor of the central building to the 4 th floor of the central building through the 3 rd floor, and then the walking route goes from the left side of the corridor on the 4 th floor to the left side stair on the central building to the 2 nd floor through the 3 rd floor and returns to the starting point of the 2 nd floor, and the walking route also runs in a closed route. The walking process goes through going upstairs, normal walking, going downstairs and normal walking.
FIG. 7 is a walking trajectory graph of data obtained by an android client processed by the algorithm of the present invention, and the positioning error is calculated to be within 2 m.
Through experimental analysis, the invention can keep higher positioning precision for normal two-dimensional plane walking and still has higher positioning precision for complex upstairs and downstairs movement states. The corrected data are displayed to the user through a user interface display module of the android client, and autonomous navigation of the user can be achieved in various unknown environments.
Claims (6)
1. A high-precision indoor positioning method based on an MEMS inertial sensor is characterized by comprising the following steps:
(1) the MEMS inertial sensor is fixedly connected to the foot of the pedestrian, so that the MEMS inertial sensor senses the motion state of the foot and acquires navigation information of the foot in real time, and the navigation information is transmitted through the Bluetooth transmission module;
(2) the pedestrian holds the android mobile phone, receives data provided by the MEMS inertial sensor for navigating the foot motion state in real time at an android client, and stores the data;
(3) denoising the data;
(4) firstly, obtaining a zero-speed interval by adopting a zero-speed detection algorithm, and secondly, carrying out error correction by adopting a zero-speed correction algorithm in combination with a state estimation algorithm; the zero-speed interval detection method comprises the following steps of acquiring data transmitted by the MEMS inertial sensor through an android client, and detecting the zero-speed interval by adopting a sliding window method:
(a) according to the noise characteristics of an accelerometer and a gyroscope of the MEMS inertial sensor, repeated experiments are carried out in different motion states to obtain zero-speed interval thresholds in the corresponding motion states, wherein the different motion states comprise normal walking, running, climbing stairs or descending stairs;
(b) according to the acceleration information of the accelerometer acquired by the android client, firstly, the motion state of the footstep at the moment is determined by analyzing the characteristics of the acceleration in the sliding window and comparing the change of the acceleration at the adjacent moments and the conditions of the maximum value and the minimum value of the acceleration in the window, and the threshold value of the zero-speed detection corresponding to the motion state is automatically selected;
(c) respectively judging whether the specific force module value, the specific force variance and the angular velocity module value are within a threshold range in the sliding window interval, if the specific force module value, the specific force variance and the angular velocity module value simultaneously meet a threshold condition, judging that the interval is a zero-velocity interval, and after obtaining the zero-velocity interval, determining that the interval is an interval in which the foot is at zero velocity;
(5) and the android client displays the data subjected to error correction and compensation in real time through the user interface display module.
2. The high-precision indoor positioning method based on the MEMS inertial sensor of claim 1, wherein: the data provided by the MEMS inertial sensor comprises data measured by a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer.
3. The high-precision indoor positioning method based on the MEMS inertial sensor of claim 1, wherein: the android client has the function of displaying the walking track of the user in real time.
4. The high-precision indoor positioning method based on the MEMS inertial sensor of claim 1, wherein: in the step (3), the denoising processing method is wavelet denoising.
5. The high-precision indoor positioning method based on the MEMS inertial sensor of claim 1, wherein: in the step (4), the state estimation algorithm is an adaptive kalman filtering algorithm, and the step of performing error correction by adopting a zero-speed correction algorithm and combining the adaptive kalman filtering algorithm is as follows:
and (3) describing the relationship between the speed error and the gyro drift, the acceleration zero offset and the attitude angle error by using an error differential equation:
error equation of attitude angle
Equation of speed error
Equation of position error
Establishing a Kalman filtering equation on the basis of an error equation, taking a speed error as an observed quantity, estimating an attitude angle error, a position error, a gyro drift and an acceleration zero offset, feeding back to a navigation resolving module,
selecting the state quantity of the Kalman filter as follows:
wherein the attitude angle error, v, is expressednIndicating a speed error; p is a radical ofnIndicating a position error; the gyro is represented by a constant drift of the gyro,the system equation representing the accelerometer constant zero offset, Kalman is expressed as
X(t)＝FX(t)+W(t)
Measuring velocity errors of equations, i.e.
Zk＝[vn]＝HkXk
And after the zero-speed interval is detected, the Kalman filtering updates time and measurement, and feeds the estimated error back to the system for error compensation.
6. The high-precision indoor positioning method based on the MEMS inertial sensor of claim 1, wherein: the MEMS inertial sensor and the Bluetooth module adopt a patch type hardware design method.
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