Pedestrian indoor position tracking method based on inertial sensor

An inertial sensor and indoor position technology, applied to instruments, measuring devices, and navigation through speed/acceleration measurement, can solve problems such as time-consuming, inconvenient to carry, and poor real-time performance, and achieve position estimation error reduction and improvement The effect of accuracy

Active Publication Date: 2014-09-24
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AI-Extracted Technical Summary

Problems solved by technology

Wearable devices were used in the early stage. Sensors were installed on shoes, helmets, pockets, waists, etc. The feet can reflect more movement characteristics during walking, so the foot sensors can better detect steps, but additional Purchase special equipment and it is inconvenient to carry, not suitable for ordinary pedestrian indoor navigation system
During the walking process, the acceleration sensor will output certain walking characteristics. Through peak detection, zero-crossing detection, autocorrelation matching...
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The invention discloses a pedestrian indoor position tracking method based on an inertial sensor, relates to a pedestrian indoor position tracking method and particularly relates to a PDR (precision depth recorder) method and a PF fusion map information algorithm based on the inertial sensor. The pedestrian indoor position tracking method disclosed by the invention is used for solving the conditions such as great position estimation errors and even wrong estimation, and the like caused by long-time drift performance of the inertial sensor when only the PDR is used for tracking the pedestrian position. The method comprises the following steps of I. detecting steps and estimating step length according to an acceleration sensor; II. estimating a course angle according to the change of three-axle angular velocity in measured data of a gyroscope, correcting the course angle, carrying out course calculation according to the corrected course angle and the step length of the first step; and finally, estimating the position according to the step length and the course angle through the PDR method; and III. fusing map information with the PDR estimation by particle filtering to complete the pedestrian indoor position tracking method based on the inertial sensor. The pedestrian indoor position tracking method disclosed by the invention is applied to the technical field of indoor positioning.

Application Domain

Navigation by speed/acceleration measurements

Technology Topic

Time driftAngular velocity +8


  • Pedestrian indoor position tracking method based on inertial sensor
  • Pedestrian indoor position tracking method based on inertial sensor
  • Pedestrian indoor position tracking method based on inertial sensor


  • Experimental program(4)

Example Embodiment

[0019] Specific implementation manner 1: The indoor position tracking method for pedestrians based on inertial sensors in this implementation manner is implemented in the following steps:
[0020] 1. Perform step detection and step length estimation according to the acceleration sensor;
[0021] 2. The heading angle is estimated according to the three-axis angular velocity changes in the gyroscope measurement data, the heading angle is corrected, and then the track is calculated based on the corrected heading angle and the first step, and finally passed according to the step length and the heading angle PDR method to estimate location:
[0022] pos k PDR = x k y k = x k - 1 y k - 1 + sLen k · cos ( h k ) sin ( h k ) - - - ( 1 )
[0023] among them, Represents the estimated PDR position of the k-th step h k Represents the heading angle estimated at step k, sLen k Represents the step length of the k-th step;
[0024] Third, the map information and the PDR estimation result are merged through particle filtering, which completes the indoor position tracking method of pedestrians based on inertial sensors.

Example Embodiment

[0025] Specific embodiment two: This embodiment is different from specific embodiment one in that the specific process of step one is as follows:
[0026] The method of peak-zero-valley-time interval is adopted for step detection, that is, each step includes 1 maximum acceleration, 2 zero values, and 1 minimum acceleration, and the time interval is reasonable, and the walking speed is at the normal speed of adults. 2~4 steps per second, set the lower limit of the time interval to 250 milliseconds, S 0 Means start, S i ,i=1...9 represents the i-th step, and the step length estimation is obtained by the following formula
[0027] sLen = 1.07 · acc Ave 3 , acc Ave = X i = 1 N | acc i | N - - - ( 2 )
[0028] Where sLen is the estimated step size, acc i , Acc Ave It represents the acceleration value and average acceleration value in each step, and N represents the number of data collected in each step.
[0029] The other steps and parameters are the same as in the first embodiment.

Example Embodiment

[0030] Specific embodiment three: This embodiment is different from specific embodiment one or two in that the heading angle estimation method in step two is:
[0031] First, perform time integration on the three-axis angular velocity around x, y, and z to obtain the pitch angle, roll angle, and azimuth angle, which are recorded as Pitch, Roll, Azimuth;
[0032] Use (3) to make the first correction to the heading angle:
[0033] heading=c 1 ·Pitch+c 2 ·Roll+c 3 ·Azimuth
[0034] (3) Among them, the pitch angle Pitch represents the amount of rotation around the x-axis, the roll angle Roll represents the amount of rotation around the y-axis, and the azimuth angle Azimuth represents the amount of rotation around the z-axis, where c 1 ,c 2 ,c 3 Is the corresponding weighting factor;
[0035] The heading angle corrected in the first step is corrected in the second step, that is, successively smoothed, which is calculated by the following formula:
[0036] heading i = mean ( X k = 1 i heading k ) - - - ( 4 )
[0037] mean(·) means to take the average value.
[0038] The other steps and parameters are the same as in the first or second embodiment.


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