Research On Indoor Positioning Algorithm Based On Micro Inertial System

With the development of science and technology in recent years,people in various occasions needs more and more precise LBS(Location Based Services),GPS can accurately provide outdoor location information.However,it is easy to be interfered by the complex environment in the indoor environment,which leads to weak signal,positioning can't be finished.The existing indoor positioning systems such as ultrasound,WLAN,ultra wide band,RFID,although to a certain extent can achieve a higher positioning accuracy.But the disadvantage is the need to install the infrastructure in advance in the environment,can not meet the positioning requirements in the unknown environment.The inertial sensor has the advantages of strong autonomy,no external infrastructure,high output frequency,high accuracy,especially in recent years,the development of MEMS technology,so that it has the advantages of small size,easy to carry,etc.This paper is mainly based on low cost MEMS IMU(Micro Inertial Measurement Unit,micro inertial measurement unit)design of navigation and positioning algorithm.This paper mainly includes five aspects as follows:Firstly,the research status of indoor positioning of MEMS inertial sensors is investigated,the movement state of different parts of human body in the process of movement was analyzed by the experimental data.according to the results of the analysis combined with the error characteristics of MEMS inertial sensors and the movement characteristics of human body,the indoor positioning scheme of MEMS inertial sensor is designed.Secondly,in order to solve the problem that high noise of low cost MEMS inertial sensor,the method of the magnetometer aided MEMS inertial sensor is studied.The pitch angle and roll angle are calculated by the output of accelerometer,according to the characteristics of the magnetometer,the path angle is calculated by the magnetometer,the problem that large alignment error caused by low precision of gyro can be effectively solved.Thirdly,aiming at the shortcomings of the traditional zero velocity interval detection algorithm,the acceleration characteristics of MEMS inertial sensors in different motion states are analyzed by the real prototype experiment.A new algorithm that adapt to various gait is proposed.According to the acceleration information output from the MEMS inertial sensor,the method of judging the motion state and matching the corresponding threshold value adopted.The experiment results show that the proposed method can achieve good results.A step counter algorithm based on the zero velocity interval is studied.Fourthly,according to the zero velocity interval,a zero velocity correction(ZUPT)plus zero angular velocity correction(ZARU)Kalman filtering algorithm is designed.The cumulative error of the sensor is estimated and compensated by the Extended Kanman Filter effectively.At last,in order to validate the positioning algorithm,low cost MEMS IMU is used to conduct experiments include normal walking experiment,upanddown stair experiment,strides forward experiment and long range shift walking.The experimental results show that the positioning accuracy of the proposed method can be kept about 3%,basically meet the requirement of indoor positioning.