Abstract
Many modulated signals exhibit a cyclostationarity property, which can be exploited in direction-of-arrival (DOA) estimation to effectively eliminate interference and noise. In this paper, our aim is to integrate the cyclostationarity with the spatial domain and enable the algorithm to estimate more sources than sensors. However, DOA estimation with a sparse array is performed in the coarray domain and the holes within the coarray limit the usage of the complete coarray information. In order to use the complete coarray information to increase the degrees-of-freedom (DOFs), sparsity-aware-based methods and the difference coarray interpolation methods have been proposed. In this paper, the coarray interpolation technique is further explored with cyclostationary signals. Besides the difference coarray model and its corresponding Toeplitz completion formulation, we build up a sum coarray model and formulate a Hankel completion problem. In order to further improve the performance of the structured matrix completion, we define the spatial spectrum sampling operations and the derivative (conjugate) correlation subspaces, which can be exploited to construct orthogonal constraints for the autocorrelation vectors in the coarray interpolation problem. Prior knowledge of the source interval can also be incorporated into the problem. Simulation results demonstrate that the additional constraints contribute to a remarkable performance improvement.
Highlights
Direction-of-arrival (DOA) estimation has been a popular research field in array processing for several decades
In order to further improve the performance of the coarray interpolation, we explore the additional structure of the autocorrelation vectors, proposing definitions of spatial spectrum sampling operations and the derivative correlation subspaces, and integrate the derivative orthogonal constraints into the coarray interpolation algorithms
We integrate the cyclostationarity with the spatial domain and performed DOA estimation signals
Summary
Direction-of-arrival (DOA) estimation has been a popular research field in array processing for several decades. This problem aims at retrieving the directional information of sources from the array of received signals, and plays an important role in a variety of practical scenarios [1,2,3]. Many modulated signals used in communication, radar, and sonar systems exhibit a cyclostationarity property. These cyclostationary signals are not periodic with respect to time, but their statistical characteristics vary periodically with time.
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