An adaptive non-Gaussian filtering using pattern recognition approach

被引：0
|
作者：
Durovic, ZM
Kovacevic, BD
机构：
来源：
ICECS 96 - PROCEEDINGS OF THE THIRD IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS, AND SYSTEMS, VOLS 1 AND 2 | 1996年
关键词：
Kalman filter; adaptive filtering; nonlinear filtering; pattern recognition; expert systems;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
An approach to adaptive non-Gaussian filtering based on the approximate maximum likelihood estimation, the so-called M-estimation, and a pattern recognition methodology has been considered in the paper. The proposed pattern recognition approach is based on the generation of a suitably chosen learning set, the appropriate selection of pattern vectors and the reduction of their dimension, as the k nearest neighbors classification procedure. A possibility of constructing an expert system for adaptive filtering is also discussed. The feasibility of the approach is demonstrated with simulations.
引用
收藏
页码：676 / 679
页数：4
相关论文
共 50 条
  • [21] Adaptive feature-specific imaging for recognition of non-Gaussian classes
    Baheti, Pawan K.
    Ke, Jun
    Neifeld, Mark A.
    APPLIED OPTICS, 2009, 48 (28) : 5225 - 5239
  • [22] Performance issues in non-Gaussian filtering problems
    Hendeby, Gustaf
    Karlsson, Rickard
    Gustafsson, Fredrik
    Gordon, Neil
    NSSPW: NONLINEAR STATISTICAL SIGNAL PROCESSING WORKSHOP: CLASSICAL, UNSCENTED AND PARTICLE FILTERING METHODS, 2006, : 65 - +
  • [23] Gaussian sum filtering methods for nonlinear non-Gaussian models
    Lin Q.
    Yin J.-J.
    Zhang J.-Q.
    Hu B.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2010, 32 (12): : 2493 - 2499
  • [24] Efficient parametric non-Gaussian dynamical filtering
    Loxam, James
    Drummond, Tom
    NSSPW: NONLINEAR STATISTICAL SIGNAL PROCESSING WORKSHOP: CLASSICAL, UNSCENTED AND PARTICLE FILTERING METHODS, 2006, : 121 - 124
  • [25] Non-Gaussian Bayesian filtering by density parametrization using power moments
    Wu, Guangyu
    Lindquist, Anders
    AUTOMATICA, 2023, 153
  • [26] Non-Gaussian Feature Detection and Recognition Based on PCA and ICA Pattern Fusion
    Ge Q.-B.
    Cheng H.-R.
    Zhang M.-C.
    Zheng R.-J.
    Zhu J.-L.
    Wu Q.-T.
    Zidonghua Xuebao/Acta Automatica Sinica, 2024, 50 (01): : 169 - 180
  • [27] A Novel Adaptive Filtering for Cooperative Localization Under Compass Failure and Non-Gaussian Noise
    Xu, Bo
    Wang, Xiaoyu
    Zhang, Jiao
    Guo, Yu
    Razzaqi, Asghar A.
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2022, 71 (04) : 3737 - 3749
  • [28] Non-Gaussian signals separation via higher-order statistics and adaptive filtering
    Pawelec, JJ
    Janulewicz, A
    ELECTROMAGNETIC COMPATIBILITY 1999 SUPPLEMENT - TUTORIAL LECTURES, WORKSHOPS, OPEN MEETINGS AND ADVERTISEMENTS, 1999, : 213 - 216
  • [29] DETECTION OF NON-GAUSSIAN SIGNALS IN NON-GAUSSIAN NOISE USING THE BISPECTRUM
    HINICH, MJ
    WILSON, GR
    IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, 1990, 38 (07): : 1126 - 1131
  • [30] An Investigation into Using Kalman Filtering for Phase Estimation in Bluetooth Receivers For Gaussian and Non-Gaussian Noise
    Nsour, Ahmad
    Abdallah, Alhaj-Saleh
    Zohdy, Mohammed
    2013 IEEE INTERNATIONAL CONFERENCE ON ELECTRO-INFORMATION TECHNOLOGY (EIT 2013), 2013,
12345