Adaptive memory-based single distribution resampling for particle filter

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

An improved resampling scheme for particle filtering in inertial navigation system

The particle filter provides numerical approximation to the nonlinear filtering problem in inertial navigation system. In the heterogeneous environment, reliable state estimation is the critical issue. The state estimation will increase the positioning error in the overall system. To address such problem, the sequential implementation resampling (SIR) considers cause and environment for every specific resampling task decision in particle filtering. However, by only considering the cause and environment in a specific situation, SIR cannot generate reliable state estimation during their process. This paper proposes an improved resampling scheme to particle filtering for different sample impoverishment environment. Adaptations relating to noise measurement and number of particles need to be made to the resampling scheme to make the resampling more intelligent, reliable and robust. Simulation results show that proposed resampling scheme achieved improved performance in term of positioning error in inertial navigation system In conclusion, the proposed scheme of sequential implementation resampling proves to be valuable solution for different sample impoverishment environment

Adaptive special strategies resampling for inertial-based mobile indoor positioning systems: an initial proposal

The phenomenon of sample impoverishment during particle filtering always contribute computation burden to the inertial-based mobile IPS systems. This is due to the factor of noise measurement and number of particle. Usually, the special strategies resampling algorithms was used. However, these algorithms just can fit in certain environment. This needs an adaptation of noise measurement and number of particle in a algorithm in order to make resampling with more intelligence, reliability and robust. In this paper, we will propose an adaptive special strategies resampling by adapting noise and particle measurement. These adaptation is used to determine the most suitable algorithm of special strategies resampling, that can be switched for resampling purpose. Finally, we illustrate our proposed solution our for indoor environment setup