Computationally Efficient Method of Signal Subspace Fitting for Direction-of-Arrival Estimation

Lei HUANG; Dazheng FENG; Linrang ZHANG; Shunjun WU

doi:10.1093/ietcom/e88-b.8.3408

Computationally Efficient Method of Signal Subspace Fitting for Direction-of-Arrival Estimation

Lei HUANG, Dazheng FENG, Linrang ZHANG, Shunjun WU

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It is interesting to resolve coherent signals impinging upon a linear sensor array with low computational complexity in array signal processing. In this paper, a computationally efficient method of signal subspace fitting (SSF) for direction-of-arrival (DOA) estimation is developed, based on the multi-stage wiener filter (MSWF). To find the new signal subspace, the proposed method only needs to compute the matched filters in the forward recursion of the MSWF, does not involve the estimate of an array covariance matrix or any eigendecomposition, thus implying that the proposed method is computationally efficient. Numerical results show that the proposed method provides the comparable estimation accuracy with the classical weighted subspace fitting (WSF) method for uncorrelated signals at reasonably high SNR and reasonably large samples, and surpasses the latter for coherent signals in the case of low SNR and small samples. When SNR is low and the samples are small, the proposed method is less accurate than the classical WSF method for uncorrelated signals. This drawback is balanced by the computational advantage of the proposed method.

Publication: IEICE TRANSACTIONS on Communications Vol.E88-B No.8 pp.3408-3415

Publication Date: 2005/08/01

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DOI: 10.1093/ietcom/e88-b.8.3408

Type of Manuscript: PAPER

Category: Antennas and Propagation

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Lei HUANG, Dazheng FENG, Linrang ZHANG, Shunjun WU, "Computationally Efficient Method of Signal Subspace Fitting for Direction-of-Arrival Estimation" in IEICE TRANSACTIONS on Communications, vol. E88-B, no. 8, pp. 3408-3415, August 2005, doi: 10.1093/ietcom/e88-b.8.3408.
Abstract: It is interesting to resolve coherent signals impinging upon a linear sensor array with low computational complexity in array signal processing. In this paper, a computationally efficient method of signal subspace fitting (SSF) for direction-of-arrival (DOA) estimation is developed, based on the multi-stage wiener filter (MSWF). To find the new signal subspace, the proposed method only needs to compute the matched filters in the forward recursion of the MSWF, does not involve the estimate of an array covariance matrix or any eigendecomposition, thus implying that the proposed method is computationally efficient. Numerical results show that the proposed method provides the comparable estimation accuracy with the classical weighted subspace fitting (WSF) method for uncorrelated signals at reasonably high SNR and reasonably large samples, and surpasses the latter for coherent signals in the case of low SNR and small samples. When SNR is low and the samples are small, the proposed method is less accurate than the classical WSF method for uncorrelated signals. This drawback is balanced by the computational advantage of the proposed method.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e88-b.8.3408/_p

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@ARTICLE{e88-b_8_3408,
author={Lei HUANG, Dazheng FENG, Linrang ZHANG, Shunjun WU, },
journal={IEICE TRANSACTIONS on Communications},
title={Computationally Efficient Method of Signal Subspace Fitting for Direction-of-Arrival Estimation},
year={2005},
volume={E88-B},
number={8},
pages={3408-3415},
abstract={It is interesting to resolve coherent signals impinging upon a linear sensor array with low computational complexity in array signal processing. In this paper, a computationally efficient method of signal subspace fitting (SSF) for direction-of-arrival (DOA) estimation is developed, based on the multi-stage wiener filter (MSWF). To find the new signal subspace, the proposed method only needs to compute the matched filters in the forward recursion of the MSWF, does not involve the estimate of an array covariance matrix or any eigendecomposition, thus implying that the proposed method is computationally efficient. Numerical results show that the proposed method provides the comparable estimation accuracy with the classical weighted subspace fitting (WSF) method for uncorrelated signals at reasonably high SNR and reasonably large samples, and surpasses the latter for coherent signals in the case of low SNR and small samples. When SNR is low and the samples are small, the proposed method is less accurate than the classical WSF method for uncorrelated signals. This drawback is balanced by the computational advantage of the proposed method.},
keywords={},
doi={10.1093/ietcom/e88-b.8.3408},
ISSN={},
month={August},}

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TY - JOUR
TI - Computationally Efficient Method of Signal Subspace Fitting for Direction-of-Arrival Estimation
T2 - IEICE TRANSACTIONS on Communications
SP - 3408
EP - 3415
AU - Lei HUANG
AU - Dazheng FENG
AU - Linrang ZHANG
AU - Shunjun WU
PY - 2005
DO - 10.1093/ietcom/e88-b.8.3408
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E88-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2005
AB - It is interesting to resolve coherent signals impinging upon a linear sensor array with low computational complexity in array signal processing. In this paper, a computationally efficient method of signal subspace fitting (SSF) for direction-of-arrival (DOA) estimation is developed, based on the multi-stage wiener filter (MSWF). To find the new signal subspace, the proposed method only needs to compute the matched filters in the forward recursion of the MSWF, does not involve the estimate of an array covariance matrix or any eigendecomposition, thus implying that the proposed method is computationally efficient. Numerical results show that the proposed method provides the comparable estimation accuracy with the classical weighted subspace fitting (WSF) method for uncorrelated signals at reasonably high SNR and reasonably large samples, and surpasses the latter for coherent signals in the case of low SNR and small samples. When SNR is low and the samples are small, the proposed method is less accurate than the classical WSF method for uncorrelated signals. This drawback is balanced by the computational advantage of the proposed method.
ER -