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Multi-Sensor Recursive EM Algorithm for Robust Identification of ARX Models.

Xin Chen1, Jiale Li1

  • 1School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm for autoregressive eXogenous (ARX) models. The RMSREM algorithm enhances system identification by handling heavy-tailed noise and fusing multi-sensor data effectively.

Area of Science:

  • Control Engineering
  • Signal Processing
  • Statistical Modeling

Background:

  • Industrial processes often face challenges with measurement noise, including outliers and the need for real-time dynamic system identification.
  • Processing information from multiple sensors simultaneously presents difficulties, especially when dealing with varying noise levels and potential sensor inaccuracies.

Purpose of the Study:

  • To develop a robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm for autoregressive eXogenous (ARX) models.
  • To address challenges posed by heavy-tailed noise and the simultaneous processing of multi-sensor information in dynamic system identification.

Main Methods:

  • Introduced Student's t-distribution to model heavy-tailed measurement noise, enhancing robustness against outliers.
  • Integrated a recursive framework into the Expectation-Maximization (EM) algorithm for real-time parameter updates and adaptation to time-varying systems.
Keywords:
ARX modelmulti-sensor data fusionrecursive EM algorithmrobust system identificationstudent’s t-distribution

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  • Designed a multi-sensor information fusion mechanism that adaptively weights sensor data based on noise variances.

    • Main Results:

    • The proposed RMSREM algorithm demonstrated robustness in the presence of heavy-tailed noise.
    • Real-time adaptation to time-varying system characteristics was achieved through the recursive update scheme.
    • Effective fusion of multi-sensor data mitigated the impact of single-sensor failures or inaccuracies.

    Conclusions:

    • The RMSREM algorithm is a valid and effective approach for robust dynamic system identification using multi-sensor data.
    • The method shows promise for applications in industrial processes, such as the continuous stirred-tank reactor (CSTR).