Abstract
While the two-dimensional (2D) spectral peak search suffers from expensive computational burden in direction of arrival (DOA) estimation, we propose a reduced-dimensional root-MUSIC (RD-Root-MUSIC) algorithm for 2D DOA estimation with coprime planar array (CPA), which is computationally efficient and ambiguity-free. Different from the conventional 2D DOA estimation algorithms based on subarray decomposition, we exploit the received data of the two subarrays jointly by mapping CPA to the full array of the CPA (FCPA), which contributes to the enhanced degrees of freedom (DOFs) and improved estimation performance. In addition, due to the ambiguity-free characteristic of the FCPA, the extra ambiguity elimination operation can be avoided. Furthermore, we convert the 2D spectral search process into 1D polynomial rooting via reduced-dimension transformation, which substantially reduces the computational complexity while preserving the estimation accuracy. Finally, numerical simulations demonstrate the superiority of the proposed algorithm.
Highlights
Two-dimensional direction of arrival (2D DOA) estimation has been extensively utilized in radar, sonar, wireless communication, and other fields [1,2,3]
We summarize the major contributions of our work below: (1) We construct the full array of the CPA (FCPA) corresponding to coprime planar array (CPA), which processes ambiguity-free characteristic and thereby extra ambiguity elimination operation can be avoided
The autopaired 2D DOA estimates can be obtained via performing spectral search on (12), it suffers from tremendously expensive computational cost
Summary
Two-dimensional direction of arrival (2D DOA) estimation has been extensively utilized in radar, sonar, wireless communication, and other fields [1,2,3]. In the above research studies for 2D DOA estimation methods with CPA, they either treat the two subarrays as individual arrays, which suffers performance degradation due to the loss of mutual information, or 2D spectrum search is required leading to expensive computational cost, or extra ambiguity elimination process is involved To address these issues, we propose a computationally efficient ambiguity-free algorithm via reduced-dimensional polynomial rooting technique. (1) We construct the FCPA corresponding to CPA, which processes ambiguity-free characteristic and thereby extra ambiguity elimination operation can be avoided (2) We exploit the received data of the two subarrays jointly, where improved DOA estimation performance as well as enhanced achievable DOFs can be achieved (3) We propose a reduced-dimensional polynomial root-finding algorithm with CPA for 2D DOA estimation, which transforms the 3D spectrum search into 1D polynomial rooting and reduces the complexity significantly while preserving the estimation accuracy. Rank (·) means the rank of the matrix. angle(·) represents the phase operator. det(·) denotes the determinant of the matrix
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