Application of Nonlinear Observers in Dynamic Indoor Positioning

• Main Authors: Ding-huan Ge, 葛定寰
• Other Authors: Wei-Wen Kao
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/24778714747923475520

碩士 === 國立臺灣科技大學 === 機械工程系 === 98 === The Kalman filter is a recursive optimal filter that estimates the States of a linear dynamics system, efficiently from a series of noisy measurement. An ideal Kalman filter can lead to an optimal filter when dynamic model of system is completely known, linear and noise must be white with zero mean. However in reality, linear systems do no really exist, that means system’s state space equation and measurement equation are all nonlinear. In this case, Kalman filter can not suffice us for precise positioning. If we want to resolve this kind of problem, nonlinear filter estimation must to be used for higher localization accuracy. In this thesis, three methods of nonlinear estimation algorithm including Extended/Unscented Kalman Filter and Particle Filter are applied for simulating WSN/DR integration positioning in dynamic indoor environment . In the framework of the system, two ZigBees and two inertial navigation sensors were used to obtain measurement informations for estimating the position of moving body, parameters of ZigBee’s RSSI and other relating states. Simulation results show that Particle Filter can estimate the states of the system more accurate than EKF and UKF, efficiently reducing the effect of unstable RSSI measurements in the dynamic indoor environment.