| With the continuous acceleration of urbanization and the continuous improvement of people’s quality of life,more and more people are working and living in indoor environments.Therefore,the demand for indoor positioning technology is becoming increasingly urgent.GPS positioning technology is currently one of the most widely used positioning technologies,but due to the susceptibility of GPS signals to obstruction and interference in indoor environments,indoor positioning services cannot be provided.In recent years,domestic and foreign researchers have attempted various technical solutions to meet the indoor positioning needs of different occasions and objects.The positioning method based on special equipment can only be carried out in the environment where special equipment is installed,which not only has high positioning costs,but also is easily limited by the positioning environment.In contrast,the indoor positioning method based on inertial sensors is an autonomous positioning method.Indoor navigation systems based on inertial sensors have become a research hotspot in the field of pedestrian indoor positioning due to their advantages of concealment,stability,anti-interference,and freedom from time and space constraints.However,this indoor positioning technology also has some shortcomings.Due to the integration operation characteristics of the navigation system and the error characteristics of the sensors,the long-term navigation accuracy of the navigation system will be insufficient.Therefore,it is necessary to further design effective error correction methods to minimize positioning errors.In this paper,a gait detection method based on hidden Markov model and an optimized zero velocity updates algorithm are designed to assist the inertial navigation system by using the scheme of installing inertial sensors on the foot of pedestrians,combined with the unique periodicity and periodicity of pedestrian movement.The main research contents of this article are as follows:(1)A gait detection method based on inertial sensors was studied.In the process of gait detection,due to insufficient consideration of the impact of local measurement value fluctuations on the detection results,existing gait detection methods based on detection thresholds often result in support phase errors and missed detections.This article provides a detailed analysis of the characteristics of pedestrian gait and proposes a gait stage model based on trough detection.This model is based on dynamic local search methods and does not require the introduction of detection thresholds,improving the adaptability of gait detection methods to local measurement fluctuations.(2)Studied gait detection methods based on hidden Markov models.In the process of gait detection,due to insufficient consideration of the impact of gait changes on detection results,existing gait detection methods have a certain degree of passivity and blindness due to the fact that most of their detection parameters need to be manually modified and adjusted separately,and are not suitable for changing gait.This article proposes a gait detection method based on the Markov chain properties of gait stage models,which can effectively cope with local measurement fluctuations and gait changes,thereby improving the accuracy and reliability of gait detection(3)In this paper,a zero velocity updates algorithm for Kalman filter is studied.The algorithm uses the indirect filtering principle to decompose the original system equation of the strapdown inertial navigation system,and treats the velocity output of the supporting in-phase system as a pseudo observation value of the velocity error to achieve the estimation of the system error.This algorithm makes full use of the coupling relationship between velocity error,attitude error,and position error,estimates and corrects more positioning errors,thereby improving the accuracy and reliability of positioning.The results show that the position error and distance error of the inertial navigation system based solely on inertial sensors are less than 3%,which basically meets the requirements of indoor positioning... |
