Quasi-optimal linear recursive DOA tracking of moving acoustic source for cognitive robot auditory system

Seul Ki Han, Won Sang Ra, Ick Ho Whang, Jin Bae Park

Research output: Contribution to journalArticle

Abstract

This paper proposes a quasi-optimal linear DOA (Direction-of-Arrival) estimator which is necessary for the development of a real-time robot auditory system tracking moving acoustic source. It is well known that the use of conventional nonlinear filtering schemes may result in the severe performance degradation of DOA estimation and not be preferable for real-time implementation. These are mainly due to the inherent nonlinearity of the acoustic signal model used for DOA estimation. This motivates us to consider a new uncertain linear acoustic signal model based on the linear prediction relation of a noisy sinusoid. Using the suggested measurement model, it is shown that the resultant DOA estimation problem is cast into the NCRKF (Non-Conservative Robust Kalman Filtering) problem [12]. NCRKF-based DOA estimator provides reliable DOA estimates of a fast moving acoustic source in spite of using the noise-corrupted measurement matrix in the filter recursion and, as well, it is suitable for real-time implementation because of its linear recursive filter structure. The computational efficiency and DOA estimation performance of the proposed method are evaluated through the computer simulations.

Original languageEnglish
Pages (from-to)211-217
Number of pages7
JournalJournal of Institute of Control, Robotics and Systems
Volume17
Issue number3
DOIs
Publication statusPublished - 2011 Mar 1

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Direction of Arrival
Direction of arrival
Acoustics
Robot
Robots
Robust Filtering
Kalman Filtering
Real-time
Filter
Estimator
Nonlinear filtering
Linear Prediction
Nonlinear Filtering
Control nonlinearities
Tracking System
Computational efficiency
Acoustic noise
Recursion
Computational Efficiency
Degradation

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Applied Mathematics

Cite this

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abstract = "This paper proposes a quasi-optimal linear DOA (Direction-of-Arrival) estimator which is necessary for the development of a real-time robot auditory system tracking moving acoustic source. It is well known that the use of conventional nonlinear filtering schemes may result in the severe performance degradation of DOA estimation and not be preferable for real-time implementation. These are mainly due to the inherent nonlinearity of the acoustic signal model used for DOA estimation. This motivates us to consider a new uncertain linear acoustic signal model based on the linear prediction relation of a noisy sinusoid. Using the suggested measurement model, it is shown that the resultant DOA estimation problem is cast into the NCRKF (Non-Conservative Robust Kalman Filtering) problem [12]. NCRKF-based DOA estimator provides reliable DOA estimates of a fast moving acoustic source in spite of using the noise-corrupted measurement matrix in the filter recursion and, as well, it is suitable for real-time implementation because of its linear recursive filter structure. The computational efficiency and DOA estimation performance of the proposed method are evaluated through the computer simulations.",
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Quasi-optimal linear recursive DOA tracking of moving acoustic source for cognitive robot auditory system. / Han, Seul Ki; Ra, Won Sang; Whang, Ick Ho; Park, Jin Bae.

In: Journal of Institute of Control, Robotics and Systems, Vol. 17, No. 3, 01.03.2011, p. 211-217.

Research output: Contribution to journalArticle

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