Open AccessRobust Special Strategies Re sampling for Mobile Inertial Navigation Systems
Author(s) -
Wan Mohd Yaakob Wan Bejuri,
Mohd Murtadha Mohamad,
Hadri Omar,
Farag Khalifa Omar,
Nurfarah Ain Limin
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b7322.129219
Subject(s) - resampling , particle filter , computer science , sample (material) , global positioning system , particle swarm optimization , real time computing , inertial measurement unit , inertial navigation system , computation , mobile device , inertial frame of reference , artificial intelligence , data mining , algorithm , kalman filter , telecommunications , chemistry , physics , operating system , chromatography , quantum mechanics
The mobile navigation services in an obstructed area can be extremely challenging especially if the Global Positioning System (GPS) is blocked. In such conditions, users will find it difficult to navigate directly on-site. This needs to use inertial sensor in order to determine the location as standalone, low cost and ubiquity. However, the usage of accurate inertial sensor and fast localization module in the system would lead the phenomenon of sample impoverishment, which it is contribute computation burden to the system. There are different situation of the sample impoverishment, and the solution by using special strategies resampling algorithm cannot be used or fitted in different cases in altogether. Adaptations relating to particle filtering attribute need to be made to the algorithm in order to make resampling more intelligent, reliable and robust. In this paper, we are proposes a robust special strategy resampling algorithm by adapting particle filtering attribute such as; noise and particle measurement. This adaptation is used to counteract sample impoverishment in different cases in altogether. Finally, the paper presents the proposed solution can survive in three (3) types of sample impoverishment situation inside mobile computing platform.