In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.
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Posted 15 Mar, 2021
Received 09 Apr, 2021
Received 05 Apr, 2021
Received 04 Apr, 2021
On 18 Mar, 2021
On 12 Mar, 2021
On 12 Mar, 2021
On 11 Mar, 2021
Received 11 Mar, 2021
Invitations sent on 09 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
On 25 Feb, 2021
Posted 15 Mar, 2021
Received 09 Apr, 2021
Received 05 Apr, 2021
Received 04 Apr, 2021
On 18 Mar, 2021
On 12 Mar, 2021
On 12 Mar, 2021
On 11 Mar, 2021
Received 11 Mar, 2021
Invitations sent on 09 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
On 08 Mar, 2021
On 25 Feb, 2021
In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
This preprint is available for download as a PDF.
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