Sparse Spatial Smoothing: Reduced Complexity and Improved Beamforming Gain via Sparse Sub-Arrays
arxiv(2024)
摘要
This paper addresses the problem of single snapshot Direction-of-Arrival
(DOA) estimation, which is of great importance in a wide-range of applications
including automotive radar. A popular approach to achieving high angular
resolution when only one temporal snapshot is available is via subspace methods
using spatial smoothing. This involves leveraging spatial shift-invariance in
the antenna array geometry, typically a uniform linear array (ULA), to
rearrange the single snapshot measurement vector into a spatially smoothed
matrix that reveals the signal subspace of interest. However, conventional
approaches using spatially shifted ULA sub-arrays can lead to a prohibitively
high computational complexity due to the large dimensions of the resulting
spatially smoothed matrix. Hence, we propose to instead employ judiciously
designed sparse sub-arrays, such as nested arrays, to reduce the computational
complexity of spatial smoothing while retaining the aperture and
identifiability of conventional ULA-based approaches. Interestingly, this idea
also suggests a novel beamforming method which linearly combines multiple
spatially smoothed matrices corresponding to different sets of shifts of the
sparse (nested) sub-array. This so-called shift-domain beamforming method is
demonstrated to boost the effective SNR, and thereby resolution, in a desired
angular region of interest, enabling single snapshot low-complexity DOA
estimation with identifiability guarantees.
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