Research On Walking Navigation Algorithm Based On MEMS Sensors

At present,people pay more attention to location information,some positioning navigation technologies have come into being.Outdoors,GPS has good performance,but indoors,the signal will be blocked by obstacles such as walls,the signal strength will be weakened or lost in a short time which performs poorly.Indoor pedestrian location navigation has become a research hotspot.This paper mainly studies the walking navigation algorithm based on MEMS sensors.In this paper,by designing the hardware circuit,and hardware and software debugging,we could handle the sensor data through the hardware algorithm,achieve the conversion of sensor data in different coordinate systems,and realize the function of filtering,attitude estimation and step counting,and the data could be written as TXT format in TF card for reading data in computer easily.The filtering algorithm can manipulate the sensor data.This paper will introduce Bayesian filter,Complementary filter and Kalman filter.Complementary filter enhances the system accuracy and response speed by using the complementarity of accelerometer and gyroscope in frequency domain.Bayesian filter is the basis of Particle filter.In this paper,the algorithm use the extended Kalman filter to realize attitude estimation by selecting proper parameters and integrating the accelerometer,gyroscope and magnetometer data.Obtaining the sensor data at different locations in the pedestrian through the hardware device.According to the characteristics of accelerometer and gyroscope data,this paper will use different algorithms to step counting and propose the improved algorithm.We use ZUPT to analyze the gyroscope data of foot,and we can achieve pedometer and walking distance by using this algorithm.An improved peak-to-peak detection and crossing the middle threshold algorithm will be proposed to realize step counting.And this paper will propose a solution of the wrong step counting.We can achieve pedometer by using this algorithm with handling device.Compared with the results of other algorithms and wear devices,the algorithm of the step counting we proposed in this paper which based on the algorithm of peak-to-peak detection and crossing the middle threshold has higher accuracy and minimal error.This paper will propose the dead reckoning method based on the particle filter and the map matching.Combining with the indoor map information,limiting the pedestrian track by using the map information to realize the map matching.Using the particle filter can modify the pedestrian track.It can reckon the pedestrian track and avoid the pedestrian path through the wall.The results of multiple experiments show that the walking navigation system can achieve a higher precision step counting,accurate attitude estimation,and the path estimated is close to the actual walking path.This walking navigation system is suitable for the indoor pedestrian navigation.