TY - GEN
T1 - Improving location estimation with two-tier particle filtering in mobile wireless environment
AU - Sung, Kwangjae
AU - Lee, Suk Kyu
AU - Kim, Hwangnam
PY - 2011
Y1 - 2011
N2 - A particle filter is a sequential Monte Carlo method that is superior in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. Due to its nature, a particle filter has been regarded as an appropriate algorithm for localization. However, conventional problems, such as sample impoverishment and degeneracy problem, have not been perfectly solved yet. To solve these problems in mobile wireless environment, we propose an enhanced localization scheme, called Gaussian kernel density estimation-based particle filtering (GKPF), which calculates the target distribution (for location estimation) based on nonparametric technique. In order to estimate the target distribution, the GKPF algorithm creates both unimodal and multimodal distributions based on particle representations, and it calculates a pdf for each distribution with Gaussian kernel-density estimation. Simulation study indicates that the proposed GKPE scheme can accurately estimate the location in mobile wireless environment.
AB - A particle filter is a sequential Monte Carlo method that is superior in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. Due to its nature, a particle filter has been regarded as an appropriate algorithm for localization. However, conventional problems, such as sample impoverishment and degeneracy problem, have not been perfectly solved yet. To solve these problems in mobile wireless environment, we propose an enhanced localization scheme, called Gaussian kernel density estimation-based particle filtering (GKPF), which calculates the target distribution (for location estimation) based on nonparametric technique. In order to estimate the target distribution, the GKPF algorithm creates both unimodal and multimodal distributions based on particle representations, and it calculates a pdf for each distribution with Gaussian kernel-density estimation. Simulation study indicates that the proposed GKPE scheme can accurately estimate the location in mobile wireless environment.
UR - http://www.scopus.com/inward/record.url?scp=80053016144&partnerID=8YFLogxK
U2 - 10.1109/ICCCN.2011.6006052
DO - 10.1109/ICCCN.2011.6006052
M3 - Conference contribution
AN - SCOPUS:80053016144
SN - 9781457706387
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - 2011 20th International Conference on Computer Communications and Networks, ICCCN 2011 - Proceedings
T2 - 2011 20th International Conference on Computer Communications and Networks, ICCCN 2011
Y2 - 31 July 2011 through 4 August 2011
ER -