Practical direction of arrival estimator using constrained robust Kalman filtering

Seul Ki Han, Won Sang Ra, Jin Bae Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a linear estimation theory based direction of arrival (DOA) estimator to guarantee high-performance and computational efficiency. To do this, state-space system is derived from the linear prediction relation of the sinusoidal acoustic signal. Since it contains uncertain measurement matrix, the recently developed non-conservative robust Kalman filter (NCRKF) can be applied to compensate the performance degradation by the uncertain measurement matrix. However, unfortunately, the statistical information used in NCRKF scheme may not be precise in actual situation and it leads to the performance degradation. Therefore, in this paper, constrained NCRKF (CNCRKF) is presented to develop practical DOA estimator. It adopts constraint condition derived from the relation between target states to solve the performance degradation problem by the incorrect statistical information. The performance of the proposed solution is demonstrated by the computer simulation.

Original languageEnglish
Title of host publicationICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems
Pages1284-1287
Number of pages4
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 13th International Conference on Control, Automation and Systems, ICCAS 2013 - Gwangju, Korea, Republic of
Duration: 2013 Oct 202013 Oct 23

Other

Other2013 13th International Conference on Control, Automation and Systems, ICCAS 2013
CountryKorea, Republic of
CityGwangju
Period13/10/2013/10/23

Fingerprint

Direction of arrival
Kalman filters
Degradation
Computational efficiency
Acoustics
Computer simulation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Han, S. K., Ra, W. S., & Park, J. B. (2013). Practical direction of arrival estimator using constrained robust Kalman filtering. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems (pp. 1284-1287). [6704149] https://doi.org/10.1109/ICCAS.2013.6704149
Han, Seul Ki ; Ra, Won Sang ; Park, Jin Bae. / Practical direction of arrival estimator using constrained robust Kalman filtering. ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. pp. 1284-1287
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Han, SK, Ra, WS & Park, JB 2013, Practical direction of arrival estimator using constrained robust Kalman filtering. in ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems., 6704149, pp. 1284-1287, 2013 13th International Conference on Control, Automation and Systems, ICCAS 2013, Gwangju, Korea, Republic of, 13/10/20. https://doi.org/10.1109/ICCAS.2013.6704149

Practical direction of arrival estimator using constrained robust Kalman filtering. / Han, Seul Ki; Ra, Won Sang; Park, Jin Bae.

ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. p. 1284-1287 6704149.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - This paper proposes a linear estimation theory based direction of arrival (DOA) estimator to guarantee high-performance and computational efficiency. To do this, state-space system is derived from the linear prediction relation of the sinusoidal acoustic signal. Since it contains uncertain measurement matrix, the recently developed non-conservative robust Kalman filter (NCRKF) can be applied to compensate the performance degradation by the uncertain measurement matrix. However, unfortunately, the statistical information used in NCRKF scheme may not be precise in actual situation and it leads to the performance degradation. Therefore, in this paper, constrained NCRKF (CNCRKF) is presented to develop practical DOA estimator. It adopts constraint condition derived from the relation between target states to solve the performance degradation problem by the incorrect statistical information. The performance of the proposed solution is demonstrated by the computer simulation.

AB - This paper proposes a linear estimation theory based direction of arrival (DOA) estimator to guarantee high-performance and computational efficiency. To do this, state-space system is derived from the linear prediction relation of the sinusoidal acoustic signal. Since it contains uncertain measurement matrix, the recently developed non-conservative robust Kalman filter (NCRKF) can be applied to compensate the performance degradation by the uncertain measurement matrix. However, unfortunately, the statistical information used in NCRKF scheme may not be precise in actual situation and it leads to the performance degradation. Therefore, in this paper, constrained NCRKF (CNCRKF) is presented to develop practical DOA estimator. It adopts constraint condition derived from the relation between target states to solve the performance degradation problem by the incorrect statistical information. The performance of the proposed solution is demonstrated by the computer simulation.

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Han SK, Ra WS, Park JB. Practical direction of arrival estimator using constrained robust Kalman filtering. In ICCAS 2013 - 2013 13th International Conference on Control, Automation and Systems. 2013. p. 1284-1287. 6704149 https://doi.org/10.1109/ICCAS.2013.6704149