Study On Improved Biped IMU Pedestrian Positioning Algorithm

The popularity of location services has brought great convenience to people's lives.In the outdoor environment,relying on complete GNSS can obtain real-time high-precision location services.Indoor positioning technology is generally used in environments where buildings,forests,tunnels,or buildings cannot be used for GNSS positioning.Systems that use active signals for indoor positioning require prior installation of infrastructure and are not available in signal jams and emergency scenarios for some disasters.In view of this situation,this paper chooses to study the improvement of data quality,improve the accuracy of gait detection and navigation information fusion based on the calculation of the shoe-type pedestrian dead reckoning,and improve the positioning accuracy and system of the shoe-footed pedestrian dead reckoning stability.The research results are as follows:(1)IMU's multi-position rotation error calibration is proposed.For the problem of low-quality IMU data quality.Firstly analyze the random noise of IMU by using Allan variance.Then accelerometer error model was constructed by comparing sensed specific force and Earth's gravity,the calibration of gyros was carried out by combing the calibrated accelerations and INS navigation algorithm.An improved Levenberg-Marquardt algorithm was proposed to calibrate the IMU sensor errors.This method can accurately calibrate the constant value offset of the accelerometer and the gyroscope,the three-axis installation error and the scaling factor without any external equipment assistance.After calibration,the accelerometer and gyroscope data quality can be effectively improved,and the navigation accuracy is improved by an order of magnitude.(2)Adaptive Gait Detection in Multiple Motion States is proposed.In order to solve the influence of the downward wave and the swing wave in the support term on the zero-speed detection,a multi-parameter gait detection algorithm combining detection threshold,amplitude threshold and time threshold is proposed.By analyzing the characteristics of various common motion states,the threshold values of each gait detection parameter in the corresponding motion state are determined.The random forest algorithm is used to identify the different motion states of pedestrians,and the gait detection threshold in the corresponding state is adjusted.The algorithm can adapt to the gait detection of indoor multi-motion state,and more effectively play the role of zero-speed correction.The comprehensive positioning accuracy can be improved by 33.1%,and the gait detection accuracy is improved by 89.4%.(3)Improved biped IMU positioning algorithm is proposed.Firstly,according to the pedestrian's maximum step size and height of the foot,the divergence of the two navigation system positions bound on the pedestrian's feet is established,and the ellipsoid-constrained bipedal IEZ model is established.On this basis,the heuristic bias elimination biped IMU positioning algorithm is proposed.According to the characteristics of pedestrians moving linearly on the indoor plane,the elevation and heading of the biped IMU positioning algorithm are corrected by using the direction and structure of the building.The experimental results show that the improved biped IMU positioning algorithm proposed in this paper can achieve a relative closure difference of 0.4%.In the case of known building height differences,the elevation error can be completely eliminated on the plane.In the case of unknown elevation,the elevation direction is relative.The positioning error is 6.3%,which effectively improves the accuracy of indoor three-dimensional positioning.