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

Ordinal regression (OR) is a crucial in machine learning. Usual assumption is that all
training instances are perfectly denoted. However, when this assumption does not hold, the …

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

Adaptive beamforming using sparse arrays can alleviate system burden with a sub-Nyquist
sampling rate while achieving high resolution. To process multi-dimensional signals without …

This letter considered the passive sensing problem and proposed a new direction-of-arrival
(DOA) estimation algorithm with joint squared-sine function (SS) and atomic norm …

Sparse linear arrays (SLAs) can be designed in a systematic way, with the ability for
underdetermined DOA estimation where a greater number of sources can be detected than …

Direction-of-arrival (DOA) estimation using sub-Nyquist tensor signals benefits from
enhanced performance by extracting structural angular information with multi-dimensional …

In this paper, we consider monostatic sparse MIMO radar with the transmit and receive
arrays mounted on a moving platform and exploit array motions to increase the number of …

Direction-of-arrival (DOA) estimation using deep neural networks has shown great potential
for applications in complicated environments. However, conventional matrix-based deep …

The orthogonal frequency-division multiplexing (OFDM) is widely used in modern radio
communications because of its efficient spectrum utilization. As we know, the adaptive …