English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For estimating the direction of arrival (DOA)s of non-stationary source signals such as speech and audio, a constrained optimization problem (COP) that exploits the spatial diversity provided by an array of sensors is formulated in terms of a noise-eliminated local 2&#x03C1;th-order cumulant matrix. The COP solution provides a weight vector to the look direction such that it is constrained to the 2&#x03C1;th-order source-signal subspace when the look direction is in alignment with the true DOA; otherwise, it is constrained to the 2&#x03C1;th-order noise subspace. This weight vector is incorporated into the spatial spectrum to determine the degree of orthogonality between itself and either the 2&#x03C1;th-order source-signal subspace when the number of sources is unknown, or the 2&#x03C1;th-order noise subspace when the number of sources is known. For a uniform linear array (ULA) of M sensors, the spatial spectrum for known number of sources can theoretically be shown to identify up to 2&#x03C1;(M-1) sources. Realizing the difficulty in identifying stationarity in the received sensor signals, the estimate of the noise-eliminated local 2&#x03C1;th-order cumulant matrix is marginalized over various possible stationary segmentations, for a more robust DOA estimation. In this paper, we focus on the use of local second and fourth order cumulants ( &#x03C1; = 1, 2), and the proposed algorithms when &#x03C1; = 1 outperformed the KR subspace-based algorithms and also the 4-MUSIC for globally non-stationary, non-Gaussian synthetic data and also for speech/audio in various adverse environments. We verified that the identifiability for &#x03C1; = 2 is improved by two-folds compared to that for &#x03C1; = 1 with an ULA.

ieee transactions on signal processing

Underdetermined High-Resolution DOA Estimation: A <formula formulatype="inline"><tex Notation="TeX">$2\rho$</tex> </formula>th-Order Source-Signal/Noise Subspace Constrained Optimization

Underdetermined High-Resolution DOA Estimation: A $2\rho$ th-Order Source-Signal/Noise Subspace Constrained Optimization