Minimal Number of Sensor Nodes for Distributed Kalman Filtering

被引:8
作者
Li, Wangyan [1 ,2 ]
Yang, Fuwen [1 ]
Thiel, David, V [1 ]
Wei, Guoliang [3 ]
机构
[1] Griffith Univ, Sch Engn & Built Environm, Gold Coast Campus, Gold Coast, Qld 4222, Australia
[2] Univ New South Wales, Sch Chem Engn, Sydney, NSW 2052, Australia
[3] Univ Shanghai Sci & Technol, Coll Sci, Shanghai 200093, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 03期
基金
澳大利亚研究理事会;
关键词
Observability; Kalman filters; Stability criteria; Task analysis; Gold; Time-varying systems; Laser stability; Distributed Kalman filtering; minimal nodes uniform observability (MNUO); sensor networks; time-varying systems; CONSENSUS; SYNCHRONIZATION; NONLINEARITIES; OBSERVABILITY; NETWORKS; SYSTEMS;
D O I
10.1109/TSMC.2020.3034732
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Finding and identifying the minimal number of sensor nodes for a sensor network is one of the most basic problems for the implementation of distributed state estimators. Despite a plethora of research studied sensor networks, most of them ignored this problem or assumed the considered sensor network comes with an ideal number of sensor nodes. We revisit this problem in the current paper. To this end, the minimal number of sensor nodes problem is first formalized and a novel observability condition, namely, minimal nodes uniform observability (MNUO), is then proposed. Next, this MNUO is applied to study the stability issues of the distributed Kalman filtering algorithm. In what follows, under the condition of MNUO, conditions to ensure its stability are given and the results about the relation of the filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.
引用
收藏
页码:1778 / 1786
页数:9
相关论文
共 36 条
  • [1] DETECTABILITY AND STABILIZABILITY OF TIME-VARYING DISCRETE-TIME LINEAR-SYSTEMS
    ANDERSON, BDO
    MOORE, JB
    [J]. SIAM JOURNAL ON CONTROL AND OPTIMIZATION, 1981, 19 (01) : 20 - 32
  • [2] [Anonymous], 1990, Correlation methods
  • [3] [Anonymous], 2012, CVX: MATLAB Software for Disciplined Convex Programming Version 2.0
  • [4] A direct algebraic approach to observer design under switching measurement equations
    Babaali, M
    Egerstedt, M
    Kamen, EW
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2004, 49 (11) : 2044 - 2049
  • [5] Consensus-Based Linear and Nonlinear Filtering
    Battistelli, G.
    Chisci, L.
    Mugnai, G.
    Farina, A.
    Graziano, A.
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2015, 60 (05) : 1410 - 1415
  • [6] Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability
    Battistelli, Giorgio
    Chisci, Luigi
    [J]. AUTOMATICA, 2014, 50 (03) : 707 - 718
  • [7] Distributed Robust Fusion Estimation With Application to State Monitoring Systems
    Chen, Bo
    Hu, Guoqiang
    Ho, Daniel W. C.
    Zhang, Wen-An
    Yu, Li
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (11): : 2994 - 3005
  • [8] Chen GR, 2017, INT J AUTOM COMPUT, V14, P1, DOI [10.1007/s11633-016-1052-9, 10.1080/15623599.2017.1354514]
  • [9] CONDITIONS FOR ASYMPTOTIC STABILITY OF DISCRETE MINIMUM-VARIANCE LINEAR ESTIMATOR
    DEYST, JJ
    PRICE, CF
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1968, AC13 (06) : 702 - &
  • [10] Distributed H∞ estimation with stochastic parameters and nonlinearities through sensor networks: The finite-horizon case
    Ding, Derui
    Wang, Zidong
    Dong, Hongli
    Shu, Huisheng
    [J]. AUTOMATICA, 2012, 48 (08) : 1575 - 1585