Fractional difference co-array perspective for wideband signal DOA estimation
In recent years, much attention has been focused on difference co-array perspective in DOA estimation field due to its ability to increase the degrees of freedom and to detect more sources than sensors. In this article, a fractional difference co-array perspective (FrDCA) is proposed by vectorizing...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/88040 http://hdl.handle.net/10220/46892 |
| Summary: | In recent years, much attention has been focused on difference co-array perspective in DOA estimation field due to its ability to increase the degrees of freedom and to detect more sources than sensors. In this article, a fractional difference co-array perspective (FrDCA) is proposed by vectorizing structured second-order statistics matrices instead of conventional zero-lag covariance matrix. As a result, not only conventional virtual sensors but also the fractional ones can be utilized to further increase the degrees of freedom. In a sense, the proposed perspective can be viewed as an extended structured model to generate virtual sensors. Then, as a case study, four DOA estimation algorithms for wideband signal based on the FrDCA perspective are specifically presented. The fractional virtual sensors can be generated by dividing the wideband signal into many sub-band signals. Accordingly, the degree of freedom and the maximum number of resolvable sources are increased. The corresponding numerical simulation results validate the advantages and the effectiveness of the proposed perspective. |
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