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Design and optimization of a novel fast distributed Kalman consensus filtering algorithm
Author(s) -
Fan Sha,
Yan Huaicheng,
Huang Lingyun,
Yang Chunxi
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2637
Subject(s) - kalman filter , control theory (sociology) , stability (learning theory) , convergence (economics) , algorithm , pid controller , computer science , combing , rate of convergence , lyapunov stability , mathematics , mathematical optimization , control (management) , control engineering , engineering , artificial intelligence , key (lock) , machine learning , temperature control , cartography , computer security , geography , economics , economic growth
Summary A new type of fast distributed Kalman consensus filtering algorithm based on local information feedback is presented to tackle filtering problems in wireless sensor networks. First, this fast filtering issues are transformed into a stochastic stability problem of the dynamic estimation errors, which can be solved by Lyapunov's second method and matrix theory. Then, two sufficient conditions about the proportional‐like feedback (double gains regulation) method and incremental Proportional‐Integral‐Derivative (PID) feedback method for the asymptotical stability of the systems are presented, respectively. Moreover, to achieve a faster convergence rate, a novel optimal method is given by combing a genetic algorithm and incremental PID. Finally, an illustrative example is presented to give a comparison of the convergence speed between the three filtering algorithms in the same condition, and verify the effectiveness and advantage of the proposed theoretical results in this article.