Ma, Y., Zeng, Y., & Sun, S. (2022). A Deep Learning Based Super Resolution DoA Estimator With Single Snapshot MIMO Radar Data. IEEE Transactions on Vehicular Technology, 71(4), 4142–4155. https://doi.org/10.1109/tvt.2022.3151674
Abstract:
In this article, we study the radar angular resolution,
the capability of distinguishing multiple targets in different
directions of arrivals (DoA). We present a deep learning-based
super resolution DoA estimator for multiple input multiple output
(MIMO) radar with single snapshot data. The estimator consists
of a deep learning DoA classifier (DLDC) in the central bearing
angle zone, which can simultaneously detect up to 11 targets in
[−2◦, 2◦] with 32 virtual antenna elements, and a spatial filtering
pre-rotator (SFPR) that makes the DLDC supporting any bearing
angle location in a wider radar field of view (FoV).We present the
DLDC-SFPR structure and provide detailed parameter setting. In
the performance evaluation, the resolution of proposed method is
first compared with the theoretical angular resolution limit (ARL).
It is shown that the proposed estimator reaches the Chernoff
ARL. The probability of resolution (PoR) is then investigated and
compared with typical and state-of-the-art DoA estimators in various
conditions. The numerical results show that the DLDC-SFPR
achieves 0.4° resolution with 90% PoR to distinguish 2 targets at
signal to noise ratio (SNR) of 7 dB if the targets are on-gird. In the
case of any-angle, the same resolution and probability are achieved
atSNR=12 dB.The proposed method is robust to multi-target concurrent
cases, and outperforms the existing reported approaches.
Two procedures, uniform scanning and progressive scanning, are
proposed for wider FoV applications.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A∗STAR) - GAPs
Grant Reference no. : I21D1AG032