Design of Software-Based Optimal Signals for System Identification

Md. Tanjil Sarker, Ai Hui Tan, Timothy Tzen Vun Yap

Research output: Contribution to journalArticlepeer-review


Two software-based optimal signal designs, namely, the model-based optimal signal excitation 2 (MOOSE2) signal from the MOOSE2 program and the optexcit signal from the Frequency Domain System Identification Toolbox, are compared, along with the flat spectrum signal as a benchmark. The work is motivated by the lack of existing comparison and the need to provide recommendations to aid industry practitioners and applied researchers in selecting the most suitable signal for system identification. The effectiveness of the signals in combination with the choice of model structure is evaluated based on the determinant of the covariance matrix of the estimated parameters and the mean and maximum errors in the frequency response. Based on the probabilities of each combination of signal and model giving the highest estimation accuracy for lowpass and bandpass systems of various orders, recommendations are given on their selection. The feasibility of the proposed recommendations is illustrated through an application example on a modular tray oven. Results from this work are significant in bridging the current gap between theory and practice.

Original languageEnglish
Article number3001810
JournalIEEE Transactions on Instrumentation and Measurement
Early online date3 Jul 2023
Publication statusPublished - 2023


  • Estimation
  • Frequency responses
  • Harmonic analysis
  • Mathematical models
  • optimal signals
  • Perturbation methods
  • perturbation signals
  • Signal design
  • signal design
  • Software
  • software
  • System identification
  • system identification

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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