In recent years, much attention has been focused on difference co-array perspective in DOA estimation field due to\nits ability to increase the degrees of freedom and to detect more sources than sensors. In this article, a fractional\ndifference co-array perspective (FrDCA) is proposed by vectorizing structured second-order statistics matrices instead\nof conventional zero-lag covariance matrix. As a result, not only conventional virtual sensors but also the fractional\nones can be utilized to further increase the degrees of freedom. In a sense, the proposed perspective can be viewed\nas an extended structured model to generate virtual sensors. Then, as a case study, four DOA estimation algorithms\nfor wideband signal based on the FrDCA perspective are specifically presented. The fractional virtual sensors can be\ngenerated by dividing the wideband signal into many sub-band signals. Accordingly, the degree of freedom and the\nmaximum number of resolvable sources are increased. The corresponding numerical simulation results validate the\nadvantages and the effectiveness of the proposed perspective.
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