In this paper, we propose a fiber-missing tensor completion-based method for direction-of-
arrival (DOA) estimation in sensor failure scenario, where the completion of a three …

Array sensor failure poses a serious challenge to robust direction-of-arrival (DOA) estimation
in complicated environments. Although existing matrix completion methods can successfully …

The research on coherent direction of arrival (DOA) estimation is of great significance for
high-precision DOA estimation in complex electromagnetic environments. However, existing …

Augmented coarrays can be derived from spatially undersampled signals of sparse arrays
for underdetermined direction-of-arrival (DOA) estimation. With the extended dimension of …

Utilizing low-rank tensor completion (LRTC) provides great success in the recovery of
missing entries by exploiting the multidimensional structure. However, the assumption of …

Exploring the inherent multidimensional structures has great potential to obtain more latent
information for reconstructing the missing data resulting from array sensor failures in multiple …

Existing matrix-based neural network for direction-of-arrival (DOA) estimation has to train a
large amount of parameters proportional to the length of vectorized signal statistics, resulting …

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed
to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual …

The tensor‐based spatial smoothing is a good algorithm for the direction of arrival (DOA)
estimation, but it cannot make good use of the physical aperture of the array, so the paper …

Existing tensor-based coherent direction-of-arrival (DOA) estimation methods adopting
spatial smoothing to decorrelate the coherent tensor statistics usually lead to a poor …