Research On RSSI Indoor Location Algorithm Based On BLE Technology

With the continuous iterative update of intelligent mapping geographic information technology and Internet of things(IOT)wireless sensor network(WSN)technology,location based services(LBS)based on Internet of things technology has received extensive attention.As one of the landing application technologies of location services,indoor positioning accuracy depends on the complexity of indoor environment.Among many indoor positioning technologies,low-power Bluetooth(BLE)technology has the advantages of large bandwidth and long communication distance,It can be well applied to indoor positioning technology.In the practical application of BLE indoor positioning technology based on received signal strength indication(RSSI),there are some problems,such as inaccurate RSSI ranging caused by environmental noise and insufficient accuracy of indoor positioning algorithm.Aiming at the above problems,this thesis mainly does the following research:(1)Aiming at the problem of insufficient indoor positioning accuracy,a particle swarm optimization positioning algorithm based on equal arc triangle distribution is proposed.Firstly,the beacon node layout principle and several common beacon node layout methods are introduced in detail.By integrating the advantages of these beacon node layouts,an improved equal arc triangle layout method is proposed,and then the unknown nodes are located in the positioning stage combined with particle swarm optimization positioning algorithm.The simulation results show that the proposed particle swarm optimization localization algorithm based on equal arc triangle distribution can effectively improve the localization accuracy and reduce the localization cost and workload compared with other localization algorithms.(2)Aiming at the problem that RSSI ranging is not accurate due to various interference factors in the environment,a parameter estimation algorithm of lognormal shadow model based on least square method is studied.Firstly,the collected RSSI data samples are filtered by Gaussian filtering,and the filtered RSSI is fitted by least square method combined with the distance information during data sampling to obtain the estimation parameters of lognormal shadow model in the current environment.The simulation results show that the parameter estimation algorithm of lognormal shadow model based on least square method improves the ranging accuracy compared with the traditional empirical parameter algorithm,which lays a foundation for the subsequent high-precision BLE positioning.(3)In order to further improve the positioning accuracy,a four sided weighted centroid positioning algorithm based on beacon node grouping is proposed in this thesis.Firstly,the beacon nodes in the indoor environment are grouped,and then the four weighted centroids of each group of beacon nodes are located respectively.The estimated positions of each group of beacon nodes are analyzed,and the mean values of the three nearest estimated positions are calculated.The obtained mean values are the final estimated positions of the unknown nodes.The simulation results show that the qgwcpe location algorithm proposed in this thesis has higher location accuracy than other location algorithms.