Strodal, A., Karlsen, H., Nævdal, G., Skaug, H. and Vallès, B. (2011) Bridging the Ensemble Kalman Filter and Particle Filters: The Adaptive Gaussian Mixture Filter. Computational Geosciences, 15, 293-305.
https://doi.org/10.1007/s10596-010-9207-1

has been cited by the following article:

• TITLE: State Estimation for Sound Environment System with Nonlinear Observation Characteristics by Introducing Wide-Sense Particle Filter

  AUTHORS: Hisako Orimoto, Akira Ikuta, Kouji Hasegawa

  KEYWORDS: Sound Environment, Nonlinear Observation, Non-Gaussian Distribution, Particle Filter

  JOURNAL NAME: Intelligent Information Management, Vol.11 No.6, November 6, 2019

  ABSTRACT: In this study, a modified particle filter considering non-Gaussian properties of noises is proposed in a form applicable to real situation in sound environment system where the observation data are contaminated by the external noise (i.e., background noise) of arbitrary probability distribution and measured in decibel scale. More specifically, a nonlinear observation model in decibel scale with a quantized level is first paid considered by introducing the additive property of energy variables (i.e., sound intensity) in sound environment system. Next, a wide-sense particle filter of an expansion expression type is derived in a form suitable for the nonlinear observation characteristics and the signal processing considering higher-order correlation information between the specific signal and observation. Furthermore, the effectiveness of the proposed theory is confirmed by applying it to the observed data measured in real sound environment.