Abstract
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.
Highlights
Direction-of-arrival estimation is a fundamental problem in array processing [1] and is significant in many applications such as MIMO radar [2], [3], mobile communication [4], indoor positioning [5] and underwater acoustics [6]
In this part we develop a hybrid approach of DOA estimation based on received data averaging and matrix completion, and we shall present relations to other work
SIMULATION RESULTS This section presents numerical results that illustrate performance of the proposed algorithms compared to methods in [10], [36] and [37]
Summary
Direction-of-arrival estimation is a fundamental problem in array processing [1] and is significant in many applications such as MIMO radar [2], [3], mobile communication [4], indoor positioning [5] and underwater acoustics [6]. In order to solve the problem presented above, this paper first prepares a preprocess of data averaging to utilize the information from every single sensor, as is mentioned in [20] which considers circular situation. It may not effectively improve the performance using only a single statistic (covariance). An algorithm based on compressive sensing [23] was proposed for non-circular signals It can use only consecutive and non-consecutive virtual sensors generated by original sparse array but do nothing to the missing ones.
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