Efficient Model Selection in Switching Linear Dynamic Systems by Graph Clustering

Parisa Karimi, Mark D. Butala, Zhizhen Zhao, Farzad Kamalabadi

Research output: Contribution to journalArticlepeer-review


The computation required for a switching Kalman Filter (SKF) increases exponentially with the number of system operation modes. In this paper, a computationally tractable graph representation is proposed for a switching linear dynamic system (SLDS) along with the solution of a minimum-sum optimization problem for clustering to reduce the switching mode cardinality offline, before collecting measurements. It is shown that upon perfect mode detection, the induced error caused by mode clustering can be quantified exactly in terms of the dissimilarity measures in the proposed graph structure. Numerical results verify that clustering based on the proposed framework effectively reduces model complexity given uncertain mode detection and that the induced error can be well approximated if the underlying assumptions are satisfied.

Original languageEnglish (US)
Pages (from-to)2482-2486
Number of pages5
JournalIEEE Signal Processing Letters
StatePublished - 2022


  • Dynamic systems
  • Graph clustering
  • Kalman filter
  • Model complexity
  • Model mismatch
  • Recursive estimation
  • model mismatch
  • recursive estimation
  • graph clustering
  • model complexity

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering


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