Focussing matrices for coherent signal-subspace processing

Hung, H. M. James; Kaveh, M.

doi:10.1109/29.1655

Cited by 444 publications

(310 citation statements)

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“…The focusing matrices are non-unique and several discussions on construction of these focusing matrices can be found in [2], [3], [7]. The most popular way to generate these focusing matrices is to construct a unitary focusing matrix by minimizing the Frobenius norm of the manifold mismatches, viz.,…”

Section: B Review Of Coherent and Incoherent Methods Of Wideband Doamentioning

confidence: 99%

“…This class of focusing matrices is called RSS (rotational signalsubspace) matrices [3] and the matrix is required to be unitary in order to preserve the SNR before and after focusing. The focusing matrices are further discussed in [2] where it is shown that the focusing matrices (including the RSS matrices) belong to the broader class of matrices called SST (signal-subspace transformation) matrices.…”

Section: B Review Of Coherent and Incoherent Methods Of Wideband Doamentioning

confidence: 99%

“…Thus it completely avoids the need for preprocessing. (ii) The focusing matrix T auto (f i ) is a unitary matrix by construction, and hence, according to [2], [3], it results in no focusing loss. Also, it preserves the spatial whiteness of the noise, so that the coherently combined noise autocorrelation matrix remains diagonal.…”

Section: Advantages Of the Proposed Autofocusing Techniquementioning

confidence: 99%

“…In each segment the 256 samples are converted to frequency domain by a 256-point DFT which are then processed using four different algorithms: i) The proposed autofocusing method. ii) CSSM using RSS focusing matrices [3], [7] with perfect intial DOA estimates (named CSSM I) iii) CSSM using RSS focusing matrices with intial DOA estimates obatined by delay-andsum beamforming (named CSSM II), and iv) the recently proposed TOPS [9] algorithm. The RSS focusing matrices are constructed following [7] and the focusing frequency f 0 for both the CSSM methods and the proposed method is chosen to be the middle frequency of the band.…”

Section: Simulation Examplesmentioning

confidence: 99%

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A novel autofocusing approach for estimating directions-of-arrival of wideband signals

Pal

Vaidyanathan

2009

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers

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Abstract-We propose a novel approach to the design of focusing matrices which play important role in the coherent methods for wideband direction-of-arrival estimation. We call this 'autofocusing' because unlike the conventional methods, our technique constructs the focusing matrices entirely by processing the received signal and does not require any preliminary DOA estimates. In this way, it overcomes the major drawback of the coherent methods which otherwise possess many desirable properties. Through computer simulations, the proposed method is found to exhibit satisfactory performance in comparison to the existing wideband DOA estimation algorithms. 1

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Section: B Review Of Coherent and Incoherent Methods Of Wideband Doamentioning

confidence: 99%

Section: B Review Of Coherent and Incoherent Methods Of Wideband Doamentioning

confidence: 99%

Section: Advantages Of the Proposed Autofocusing Techniquementioning

confidence: 99%

Section: Simulation Examplesmentioning

confidence: 99%

See 2 more Smart Citations

A novel autofocusing approach for estimating directions-of-arrival of wideband signals

Pal

Vaidyanathan

2009

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers

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“…In fact, poor initial values can lead to biased estimates [11]. Many methods have been published on how to estimate focusing matrices as in [7,12,13]. However every one of these methods assumes a specific sources arrangement.…”

Section: Introductionmentioning

confidence: 99%

Improved finite samples resampling for unbiased wide-band direction of arrival estimation

Desoki

Takada

Hagiwara

2009

Acoust. Sci. & Tech.

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This paper considers wide-band DOA estimation using the spatial resampling method for coherent averaging. In this paper a rough estimate of the resampling error distribution is obtained. Careful examination of this distribution yields an improved resampling formula suitable for, but not limited to, arrays with finite sensors. It is shown that the proposed formula yields resampling error much less than that of conventional resampling. Moreover it is shown how the resampling error can be more reduced by proper choice of the focusing frequency. Based on the this resampling formula a perfect focusing scheme is then proposed and its performance is evaluated and compared to other wide-band methods through simulations. Simulations showed quite satisfactory and robust performance of the proposed scheme. It was shown that it succeeds in situations where all considered wideband methods fail. In addition it is bias-free and can be implemented quite efficiently. Hence it is quite suitable for reliable real-time DOA estimation in reverberant environments.

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Sound Source Localization and Separation

Nakadai

Nakamura

2015

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article presents common methods for sound source localization and separation, and their applications to robots. Sound source localization is an estimate of the location or direction of a sound source, whereas sound source separation extracts each sound source from a mixture of sound sources. Two approaches are possible for both technologies: binaural and microphone array processing. Sound source localization and separation have been widely studied in the fields of psychoacoustics, signal processing, and robotics. In binaural research, the human auditory capability is explored to solve both localization and separation problems. However, this approach is limited in terms of performance. Thus, microphone array processing is also being investigated as a primary topic in signal processing. Since robots are among the most promising applications of sound source localization and separation, robot audition is introduced and real‐time methods (such as the software platform for robot audition called Honda Research Institute Japan Audition for Robot with Kyoto University (HARK)) to deal with real‐world problems are explained.

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Focussing matrices for coherent signal-subspace processing

Cited by 444 publications

References 10 publications

A novel autofocusing approach for estimating directions-of-arrival of wideband signals

A novel autofocusing approach for estimating directions-of-arrival of wideband signals

Improved finite samples resampling for unbiased wide-band direction of arrival estimation

Sound Source Localization and Separation

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