Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering

• Published in: IEEE transactions on human-machine systems Vol. 47; no. 3; pp. 332 - 342
• Main Authors: Jung Min Pak, Choon Ki Ahn, Shmaliy, Yuriy S., Peng Shi, Myo Taeg Lim
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric measurements; Composite particle/finite impulse response (FIR) filter (CPFF); Decision making; Economic models; Failure; Filtration; Finite impulse response filters; FIR filters; human localization; Localization; Noise measurement; particle filter (PF); Particle measurements; Receivers; Robustness; State space models; Wireless sensor networks
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2016.2611826

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.