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Three-Dimensional Force System:Problem Solving

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Related Experiment Video

Updated: Jun 23, 2026

Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations

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Published on: December 6, 2024

Two-dimensional ARMA model order determination.

Mahdiye Sadat Sadabadi¹, Masoud Shafiee, Mehdi Karrari

¹Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran. sadabadi@iust.ac.ir

ISA Transactions

|May 12, 2009

Summary

This study introduces a new method for determining the order of two-dimensional (2-D) Gaussian Autoregressive Moving Average (ARMA) models. The approach independently identifies AR and MA orders for dynamic system modeling.

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Area of Science:

Signal Processing
Dynamic Systems Modeling
Statistical Modeling

Background:

Model order determination is crucial for accurately modeling dynamic systems.
Two-dimensional (2-D) processes present unique challenges in system identification.
Existing methods may not fully address the complexities of 2-D Gaussian ARMA models.

Purpose of the Study:

To propose a novel method for determining the order of 2-D Gaussian ARMA models.
To independently determine Autoregressive (AR) and Moving Average (MA) orders.
To provide a procedure for complete 2-D ARMA model order determination.

Main Methods:

Independent determination of AR and MA orders.
Development of a new procedure for 2-D Gaussian ARMA model order determination.
Assumption of causal, stable, linear, and spatial shift-invariant models with quarter-plane (QP) support.

Main Results:

The proposed method effectively determines the order of 2-D Gaussian ARMA models.
Numerical simulations validate the efficacy of the new approach.
Independent order determination simplifies the overall modeling process.

Conclusions:

The novel method offers an effective solution for 2-D Gaussian ARMA model order determination.
This approach enhances the accuracy and efficiency of dynamic system modeling.
The findings contribute to advancements in statistical signal processing and system identification.