Identifiability Analysis of Noise Covariances for LTI Stochastic Systems With Unknown Inputs (Biqiang Mu)

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09 20, 2023

  Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as Q and R, respectively) of systems with unknown inputs. This article considers the identifiability of Q / R using the correlation-based measurement difference approach. More specifically, we establish 1) necessary conditions under which Q and R can be uniquely jointly identified; 2) necessary and sufficient conditions under which Q can be uniquely identified, when R is known; 3) necessary conditions under which R can be uniquely identified, when Q is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus.



  IEEE Transactions on Automatic Control, Volume: 68, Issue: 7, July 2023



  He Kong

  Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China

  Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China


  Salah Sukkarieh

  Sydney Institute for Robotics and Intelligent Systems, The University of Sydney, Sydney, NSW, Australia


  Travis J. Arnold


  Tianshi Chen

  School of Data Science and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, China


  Biqiang Mu

  Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China



  Wei Xing Zheng

  School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia



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