3 research outputs found
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation
In this paper, we introduce a novel algorithm that can dramatically reduce
the number of antenna elements needed to accurately predict the direction of
arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed
algorithm predicts the received signal of a large antenna setup using reduced
number of antenna by using coupled dictionary learning. Hence, this enables the
MIMO radar to resolve more paths, which could not be resolved by the fewer
antennas. Specifically, we overcome the problem of inaccurate DOA estimation
due to a small virtual array setup. For example, we can use dictionary learning
to predict 100 virtual array elements using only 25. To evaluate our algorithm,
we used multiple signal classification (MUSIC) as a DOA estimation technique to
estimate the DOA for non coherent multiple targets. The results show that using
the predicted received signal, the proposed algorithm could resolve all the
targets in the scene, which could not been resolved using only the received
signal from the reduced antenna setup.Comment: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring