Research On Localization Methods In NLOS Environment For Wireless Sensor Network

Wireless sensor network is a novel information acquisition platform.Due to the availability of such low energy cost sensors,micro-processor and radio frequency circuitry for information transmission,there is a wide and rapid diffusion of WSN,and it has attracted considerable research interest.This thesis focuses on the study of indoor localization issues.The major research contents and productions are shown in the following areas:Due to the complicated indoor environment and great uncertainty in the room.The surveillance environment is obstructed by obstacles causes the non-line of sight(NLOS)propagation of signals,which is widely existed in complicated indoor environment,will degrade the performance of the WSN based localization system if it is not taken into consideration in the localization algorithm design.This paper presents an IMM-IKF(Interacting multiple model-Improved Kalman filter,IMM-IKF)based mobile localization algorithm for WSN.We utilize the interacting multiple model frame to estimate the position of unknown node.The improved Kalman filter is employed to filter the estimated location.The transition between the LOS and NLOS is described by two state Markov models.To get the location of the moving target,two filtering results are weighted fusion according to the likelihood probability.Simulation results demonstrate that the IMM-IKF algorithm outperforms the traditional algorithms in NLOS scenarios.In the actual application environment,the presence of NLOS propagation is one of the main challenges for mobile location estimation by using WSN.It results in erroneous measurements containing NLOS error which will seriously decrease the accuracy of localization due to the reflection and diffraction.We propose a Mean Shift-based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network algorithm to mitigate NLOS errors.Firstly,the NLOS detection is employed to identify the propagation condition.For the LOS measurements,The calculation of the final location result by the direct application of the LOS distance measurement value.For the NLOS measurements,a novel algorithm based on mean shift is proposed to filter the distance measurements and reserve the reliable measurements.Use the processed data for the next step.Simulation experiment results show that the proposed method can effectively mitigate the NLOS error and does not assume any statistical knowledge of the NLOS error.Simulation results demonstrate that the MS-IKF algorithm outperforms the traditional algorithms in NLOS scenarios.