Robust Special Strategies Re sampling for Mobile Inertial Navigation Systems
Wan MohdYaakob Wan Bejuri1, MohdMurtadha Mohamad2, Hadri Omar3, Farhana Syed Omar4, Nurfarah Ain Limin5
1Wan MohdYaakob Wan Bejuri*, Centre of Foundation Studies, City University Malaysia, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia.
2MohdMurtadha Mohamad, School of Computing, Universiti Teknologi Malaysia, Malaysia.
3Hadri Omar, Centre of Foundation Studies, City University Malaysia, Malaysia.
4Farhana Syed Omar, Centre of Foundation Studies, City University Malaysia, Malaysia.
5Nurfarah Ain Limin,Centre of Foundation Studies, City University Malaysia, Malaysia.
Manuscript received on November 16, 2019. | Revised Manuscript received on 27 November, 2019. | Manuscript published on December 10, 2019. | PP: 3196-3204 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7322129219/2019©BEIESP | DOI: 10.35940/ijitee.B7322.129219
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Abstract: 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.
Keywords: Resampling, Sample Impoverishment, Inertial Navigation Systems, Mobile Computing
Scope of the Article: Cloud Computing