Performance Enhancement of Indoor Mobile Localization System using Unscented Kalman Filter

One of the most common and important goals of several indoor mobile localization systems developed in the world is to enhance their performance of localization which is to get a mobile object's position data. Particularly, it is very hard for the indoor localization system using ultrasound sensors to achieve much highly precise localization for mobile target due to ultrasound's physical characteristics, such as multi-path. Such ultrasound's demerits make the measured distance value incorrect. In order to overcome this problem, a number of techniques and tips have been suggested; generally, an estimation algorithm like extended Kalman filter has been used. In this paper, we present the application of unscented Kamlan filter to improve the localization performance of our indoor mobile localization system, 'indoor GPS', as well as to overcome some limitations of ultrasound as enough as possible. In addition, we present the comparison of the unscented Kalman filter and another estimation algorithm, such as the unscented particle filter through simulation for our system