Abstract
The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Human-Machine Systems |
| DOIs | |
| Publication status | Accepted/In press - 2016 Oct 11 |
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ASJC Scopus subject areas
- Human Factors and Ergonomics
- Control and Systems Engineering
- Signal Processing
- Human-Computer Interaction
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
Cite this
Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering. / Pak, Jung Min; Ahn, Choon Ki; Shmaliy, Yuriy S.; Shi, Peng; Lim, Myo Taeg.
In: IEEE Transactions on Human-Machine Systems, 11.10.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering
AU - Pak, Jung Min
AU - Ahn, Choon Ki
AU - Shmaliy, Yuriy S.
AU - Shi, Peng
AU - Lim, Myo Taeg
PY - 2016/10/11
Y1 - 2016/10/11
N2 - The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.
AB - The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.
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UR - http://www.scopus.com/inward/citedby.url?scp=84991106576&partnerID=8YFLogxK
U2 - 10.1109/THMS.2016.2611826
DO - 10.1109/THMS.2016.2611826
M3 - Article
AN - SCOPUS:84991106576
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
SN - 2168-2291
ER -