| Nowadays,with the enrichment of life and the diversified development of industries,people's demand for location services,whether in leisure or daily travel,is increasingly strong.In the indoor environment,due to the obstruction and interference of buildings,it is based on GPS,etc.Outdoor positioning methods are difficult to effectively locate indoors,so people urgently need a low-cost,high-precision,and easy-to-use indoor positioning solution.With the development of smart phones,the accuracy of its built-in inertial sensor components such as accelerometers and gyroscopes has gradually improved.In addition,smart phones have abundant computing resources,which can quickly process and calculate large amounts of data,and human-computer interaction is also very friendly and convenient.All of these provide a solid foundation for the smart phone-based pedestrian path estimation algorithm to be used as an indoor positioning solution.To this end,this paper proposes an indoor positioning scheme based on the integrated QR code and pedestrian track estimation based on Android smart phones.Extended Kalman filter are used to optimize the steps of the traditional pedestrian track estimation algorithm.The precise coordinates provided by the code correct the errors of the above algorithm,improve the overall positioning accuracy,and conduct experimental verification.The main research contents of this paper are as follows:(1)Research on the acquisition method of sensor data based on smart phones,aiming at its shortcomings such as low stability and serious error drift,and considering that the sensor data belongs to the nonlinear field when pedestrians are moving,constructing an extended Kalman filter model for the built-in Android smart phone A variety of sensor data are processed for filtering and noise reduction,thereby obtaining more reliable data for positioning.(2)Research on pedestrian track estimation(PDR)positioning technology,and optimize separately from the three aspects of gait detection,step length estimation and heading angle estimation.In gait detection and step length estimation,after experimental analysis and verification,select The resultant acceleration is used in the calculation instead of the Z-axis acceleration to avoid large system errors caused by the shaking of the specific orientation of the mobile phone when walking;at the same time,the peak detection method is optimized in the gait recognition,and the threshold method is combined with the threshold method for each walking cycle.Both peaks and troughs are threshold-filtered in amplitude,and the interval time is also threshold-constrained to improve the accuracy of step recognition;in the calculation of the heading angle,it is proposed to calculate the basis of the heading angle based on the gyroscope,accelerometer and magnetometer.Above,fusion correction method is used to realize the complementary advantages of the two heading angles,which can effectively improve the stability of heading angle estimation.(3)Aiming at the shortcomings of error accumulation during the operation of the PDR algorithm,this paper proposes and implements a positioning technology that uses the precise coordinates provided by the QR code to correct the accumulated error of the PDR.This scheme is simple to use and low in cost.At the same time,it has been verified by experiments.This scheme can effectively reduce the overall positioning accuracy error.(4)Analyze the system requirements,sort out the prototype system architecture,and finally write the mobile phone APP interface in modules to prove the feasibility and universality of the indoor positioning algorithm to run on Android smart phones. |
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