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

This paper proposes a new sparse array source enumeration algorithm forunderdetermined scenarios with more sources than sensors. The proposedalgorithm decomposes the wideband signals into multiple uncorrelated frequencybands, computes the narrowband spatial periodograms and then averagesperiodograms to reinforce the sources' spectral information. The inversespatial Fourier transform of the wideband periodogram estimates the spatialcorrelation function, which then populates the diagonals of a HermitianToeplitz augmented covariance matrix (ACM) after lag redundancy averaging. Amodified minimum description length (MDL) criteria, termed MDLgap, is proposedfor source enumeration using the eigenvalues of the constructed ACM. MDLgapprovably never overestimates the number of sources present, and isasymptotically consistent when the signals present span a limited dynamicrange. Numerical simulations show that the proposed MDLgap algorithm achievesimproved performance over existing approaches for underdetermined sourceenumeration, especially in low snapshot scenarios.

Wideband Source Enumeration Using Sparse Array Periodogram Averaging in Low Snapshot Scenarios