TDARMA Model Estimation Using the MLS and the TF Distribution

10 pages•Published: March 13, 2019

Abstract

An approach for modeling linear time-dependent auto-regressive moving-average (TDARMA) systems using the time-frequency (TF) distribution is presented. The proposed method leads to an extension of several well-known techniques of linear time- invariant (LTI) systems to process the linear, time-varying (LTV) case. It can also be applied in the modeling of non-stationary signals. In this paper, the well-known modified least square (MLS) and the Durbin's approximation methods are adapted to this non- stationary context. A simple relationship between the generalized transfer function and the time-dependent parameters of the LTV system is derived and computer simulation illustrating the effectiveness of our method is presented, considering that the output of the LTV system is corrupted by additive noise.

Keyphrases: non stationary signal, tdarma modeling, time frequency distribution, tv coefficients

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 282-291.

Links:	https://easychair.org/publications/paper/JRb4
	https://doi.org/10.29007/64fl

BibTeX entry

@inproceedings{CATA2019:TDARMA_Model_Estimation_Using,
  author    = {Abdullah Al-Shoshan},
  title     = {TDARMA Model Estimation Using the MLS and the TF Distribution},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/JRb4},
  doi       = {10.29007/64fl},
  pages     = {282-291},
  year      = {2019}}

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