Research On Indoor Positioning System Based On Adaptive ZUPT And Contextual Fusion

In the traditional Global Positioning System(GPS),because the transmission of satellite signals is easily blocked by the building,it may not be able to locate well in the indoor environment.The strapdown inertial navigation system does not depend on the characteristics of the external signal source,so that its working environment is no longer limited.And today's popular smart phones are generally equipped with inertial sensors,making positioning does not need to buy additional devices,the cost is low.However,low-cost inertial components themselves in use will produce greater error,and because inertial navigation based on the basic characteristics of integration,this error will continue to accumulate in the navigation process,which seriously affect the positioning results,making it impossible to long-term stability locate.This paper intends to design an inertial navigation indoor positioning system based on adaptive zero speed correction and map matching,mainly for the inertial sensor in the hands of the scene,trying to solve the above shortcomings.ZUPT used in traditional inertial navigation systems does not apply because there is no zero speed phase in the horizontal direction at the handheld scene.The vertical direction ZUPT is introduced in this paper,and the vertical displacement and direction information are obtained by the inertial navigation system model based on the Kalman filter.Then,the vertical displacement is combined with the inverted pendulum model to obtain step length information.In this paper,we introduce the dynamic threshold ZUPT,and find the zero speed point by calculating the threshold through the value of the acceleration in the dynamic time window,obtaining the zero speed detection with high robustness,and finally get the accurate step length.In this paper,a Contextual Fusion Algorithm Based on Probabilistic Graph is introduced.Azimuth error is corrected by the limitation of the current context around the user,and the correction amount is fed back to the Kalman filter to correct the error of gyroscope.In this paper,the contextual fusion algorithm constructs the probability model under the context constraint by dividing the positioning area into sub-region set,and use the particle filter to update the probability model.Each sub-region in model stores the posterior value and probability value in the sub-region and is independent of each other.The sub-region set form a single step posterior probability distribution,so as to achieve the independent probability transfer of the sub-region,without relying on the posterior state of the whole system,is conducive to improving the robustness of the system.The system no longer completely lost positioning because of sudden increment of system noise or measurement noise.Under certain conditions the positioning will be promptly corrected even if the positioning error occurs.In order to analyze the overall performance of the system,this paper has carried on the whole experiment to the system.The experimental results show that the inertial navigation indoor positioning system based on adaptive zero speed correction and contextual fusion algorithm can calculate the pedestrian walking distance more accurately and eliminate the azimuth accumulation error effectively,keeping high precision in the long time test experiment.The indoor positioning system designed in this paper has great practical significance,and has good application prospect and value in navigation,fire,health and safety.