Joint Azimuth and Elevation Angle Estimation Using Matrix Completion Method

Tan, Peixiang; Wu, Yuntao; Ge, Yan; Deng, Jieyi

doi:10.2528/pierm17030402

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…However, it can use only consecutive and non-consecutive virtual sensors generated by original sparse array but do nothing to the missing ones. To this end, the paper also proposes a method based on matrix completion theory [24]- [26] which constructs a larger aperture and a higher DOF with fewer sensors while compared with non-sparse arrays.…”

Section: Introductionmentioning

confidence: 99%

Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

et al. 2019

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Direction-of-arrival (DOA) estimation using nested linear array ignores the information from repeated sensors, thus involves the problem of losing accuracy. Moreover, non-circular feature of signals is rarely considered and it results in discontinuity of the virtual array, namely, holes appear. This paper first provide an improved data averaging method to increase the accuracy of DOA estimation by fusing data from covariance and elliptic covariance. And then an algorithm based on matrix completion theory in nested array that greatly extends the degrees of freedom (DOF) and is able to find more sources than number of physical sensors is presented. The algorithm reconstructs the covariance matrix of the virtual linear array, which exactly has the same shift invariance as the uniform linear array but a higher aperture. Considering about the missing elements of the virtual array covariance matrix, we apply matrix completion theory to solve the problem. Finally, true DOAs of multiple signals can be obtained through subspace algorithms. Numerical results demonstrate that the proposed algorithms can obtain high accuracy while underdetermined DOA estimation is realized.INDEX TERMS Direction-of-arrival, nested array, matrix completion theory, data averaging.

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Section: Introductionmentioning

confidence: 99%