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

A robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-sensor information. First, for the potential...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 22; p. 7060
Main Authors: Chen, Xin, Li, Jiale
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 19.11.2025
Subjects:
Accuracy
Algorithms
Analysis
ARX model
Engineering
Identification
multi-sensor data fusion
Normal distribution
Parameter estimation
recursive EM algorithm
robust system identification
Sensors
student’s t-distribution
United Kingdom
multi-sensor data fusion
robust system identification
ARX model
recursive EM algorithm
student’s t-distribution
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:A robust multi-sensor recursive Expectation-Maximization (RMSREM) algorithm is proposed in this paper for autoregressive eXogenous (ARX) models, addressing the challenges of heavy-tailed noise, as well as the difficulty in simultaneously processing multi-sensor information. First, for the potential outliers in industrial processes, the Student’s t-distribution is introduced to model the statistical characteristics of measurement noise, whose heavy-tailed property enhances the algorithm’s robustness. Second, a recursive framework is integrated into the Expectation-Maximization (EM) algorithm to satisfy the real-time requirement of dynamic system identification. Through a recursive scheme of the Q-function and sufficient statistics, model parameters are updated in real-time, allowing them to adapt to time-varying system characteristics. Finally, by exploiting the redundancy and complementarity of multi-sensor data, a multi-sensor information fusion mechanism is designed that adaptively calculates the weight of each sensor based on the noise variances. This mechanism effectively fuses multi-source observation information and mitigates the impact of single-sensor failure or inaccuracy on identification performance. Numerical examples and simulations of the continuous stirred-tank reactor (CSTR) demonstrate the validity of the proposed RMSREM algorithm.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25227060