Key Technique Research Of Indoor High-precision Fusion Positioning

In recent years,satellite positioning and navigation technologies represented by GPS and Beidou have been rapidly developed,which play an important role in industry,commerce and daily life.However,due to signal blockage and attenuation,satellite positioning technology cannot work effectively in buildings,underground and other indoor environments,and the positioning results are not accurate and reliable enough.At the same time,the fields,such as the Internet of Things,the smart city,and mobile internet,have various requirements for high-precision indoor positioning services.The high-precision location information in an indoor environment becomes the key to connect the actual environment with people and objects.Indoor visible light positioning technology,as a new type of wireless signal positioning technology,has many technical advantages such as high positioning accuracy,strong security,anti-electromagnetic interference,low cost and energy consumption.Indoor visible light positioning technology can be applied to pedestrians in a typical indoor environment through smart phones to determine their own location,and plan the optimal route,which has been widely used in shopping malls,supermarkets,libraries,museums and other common indoor scenes.First of all,this paper studied the related theories and technical characteristics of indoor visible light positioning.From existing theory,specific filtering patterns were designed to modulate the optical positioning signals.Based on the research results of the indoor visible light positioning,an indoor visible light positioning system based on the positioning method of the received signal strength and the single-light source imaging was researched and designed.Secondly,when studying the multi-source data fusion algorithm for indoor visible light positioning,this paper performed denoising and filtering on the received nonvisible light data,such as the inertial navigation data and other multi-source positioning data.The space state vector data provided by the visible light signal and its corresponding distribution weight were input to the Kalman filter for processing,thereby obtaining the expected indoor multi-source sensor fusion positioning data.Finally,according to the fusion positioning information,this paper studied the path planning and navigation algorithm of indoor pedestrians or robots,using a hand-drawn equal-scale experimental scene map model.The A Star heuristic search path planning algorithm was adopted for the global path planning of the known indoor environment,with the actual position of pedestrians or robots being determined through multi-source fusion positioning data.Combining the preset information of the target position and the real-time positioning of the current position,the system can calculate the nearest travel path in real time through path planning,and provide the real-time and accurate navigation for the pedestrian service.In addition,based on the positioning APP platform on the smart phone with the optimization of the background positioning algorithm and the additional functions of the path planning and the real-time navigation,the real-time combination of smart phone positioning and navigation has been successfully achieved.