Adaptive memory-based single distribution resampling for particle filter

• Main Authors: Bejuri, W. M. Y. W., Mohamad, M. M., Raja Mohd. Radzi, R. Z., Salleh, M., Yusof, A. F.
• Format: Article
• Language: English
• Published: SpringerOpen 2017
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/74837/1/MazleenaSalleh2017_AdaptiveMemorybasedSingleDistributionResampling.pdf; http://eprints.utm.my/id/eprint/74837/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031995615&doi=10.1186%2fs40537-017-0094-3&partnerID=40&md5=0872c6e976c6cb0c266897ede21938d6

The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices’ memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices’ physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle’s particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.