Diffusion in Networks by Cooperative Particle Filtering

被引:0
|
作者
Wang, Hechuan [1 ]
Djuric, Peter M. [1 ]
机构
[1] SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11794 USA
来源
2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP) | 2017年
关键词
RECURSIVE LEAST-SQUARES; DISTRIBUTED ESTIMATION; ADAPTIVE NETWORKS; STRATEGIES;
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
In this paper, we propose a diffusion method for estimation of hidden processes by cooperative agents in a network. We adopt particle filtering for estimating the hidden processes. Each filter has it own observation process, unknown to the rest of the agents, but the hidden process is the same for all the agents. The agents track the hidden process, form their posteriors of the process and then approximate it with Gaussians. Then they exchange these Gaussians to form fused posteriors and to allow for diffusing of information across the network. The Gaussian approximation of the posterior distributions simplify the calculations in the diffusion steps. The simulation results show that the proposed method has similar performance as the centralized method.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Particle Diffusion in Complex Nanoscale Pore Networks
    Mueter, D.
    Sorensen, H. O.
    Bock, H.
    Stipp, S. L. S.
    JOURNAL OF PHYSICAL CHEMISTRY C, 2015, 119 (19): : 10329 - 10335
  • [32] Cooperative Heading Estimation with von Mises-Fisher Distribution and Particle Filtering
    Makela, Maija
    Kirkko-Jaakkola, Martti
    Hammarberg, Toni
    Malkamaki, Tuomo
    Rantanen, Jesperi
    Kaasalainen, Sanna
    2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022), 2022,
  • [33] COOPERATIVE KALMAN FILTERING WITH DATA FUSION IN TIME VARYING COMMUNICATION NETWORKS
    Spinello, Davide
    Stilwell, Daniel J.
    PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2010, VOL 1, 2010, : 947 - 954
  • [34] Distributed Box Particle Filtering for Target Tracking in Sensor Networks
    Liu, Ying
    Liu, Hao
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [35] INFERRING PARAMETERS OF GENE REGULATORY NETWORKS VIA PARTICLE FILTERING
    Shen, Xiaohu
    Vikalo, Haris
    2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 546 - 549
  • [36] Inferring Parameters of Gene Regulatory Networks via Particle Filtering
    Xiaohu Shen
    Haris Vikalo
    EURASIP Journal on Advances in Signal Processing, 2010
  • [37] Gaussian Sum Particle Filtering Based on RBF Neural Networks
    Fan, Guochuang
    Dai, Yaping
    Wang, Hongyan
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 3071 - 3076
  • [38] DISTRIBUTED DECORRELATION IN SENSOR NETWORKS WITH APPLICATION TO DISTRIBUTED PARTICLE FILTERING
    Moldaschl, Michael
    Gansterer, Wilfried N.
    Hlinka, Ondrej
    Meyer, Florian
    Hlawatsch, Franz
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [39] Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
    Besada-Portas, Eva
    Plis, Sergey M.
    de la Cruz, Jesus M.
    Lane, Terran
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I, 2009, 5781 : 131 - +
  • [40] A robust scheme for distributed particle filtering in wireless sensors networks
    Vazquez, Manuel A.
    Miguez, Joaquin
    SIGNAL PROCESSING, 2017, 131 : 190 - 201