Indoor Location Algorithm Based On Bluetooth 4.0

With the gradual improvement of the communication infrastructure and the arrival of the Internet of Things,wireless sensor network technology is widely used in the military,civil,commercial and other indoor environments as the underlying underlying network technology to realize the comprehensive perception function of Internet of Things.Indoor positioning technology is currently on behalf of positioning programs are Wi-Fi,Bluetooth,etc,which are carried to the wireless sensor network to achieve coverage of local area.Bluetooth module based on the latest BLE4.0 technology because of its low power consumption,fast response,good performance and other features that stand out in many design options.Bluetooth technology,this excellent performance,short-distance,low-cost wireless interconnection communication technology,is very qualified for indoor positioning,indoor positioning has become a popular research direction.In this paper,based on the analysis and research of existing indoor location methods,aiming at the shortcomings of the existing indoor positioning methods,combined with the existing development status.In this paper,an RSSI indoor positioning optimization algorithm based on the signal strength of Bluetooth 4.0 is proposed.The traditional RSSI localization algorithm is optimized from two aspects,ranging from location and location.The specific optimization strategies include the clustering algorithm for signal strength optimization,Segmentation fitting wireless signal attenuation model and optimization weighting and distance correction weighted centroid localization algorithm optimization method,in order to eliminate multipath interference,non-line of sight,long-distance transmission and other factors,thereby effectively improve the indoor positioning accuracy and positioning algorithm and applied in practical environment.The results show that this method can improve the localization precision of the system effectively,and the localization precision of the algorithm is 35.6% higher than that of traditional Gaussian filtering based RSSI signal processing optimization method,and the average error of positioning is not more than 0.5m.The results meet the indoor positioning accuracy requirements.