Abstract
Direction of arrival (DOA) estimation using a uniform linear array (ULA) is a classical problem in array signal processing. In this paper, we focus on DOA estimation based on the maximum likelihood (ML) criterion, transform the estimation problem into a novel formulation, named as sum-of-squares (SOS), and then solve it using semidefinite programming (SDP). We first derive the SOS and SDP method for DOA estimation in the scenario of a single source and then extend it under the framework of alternating projection for multiple DOA estimation. The simulations demonstrate that the SOS- and SDP-based algorithms can provide stable and accurate DOA estimation when the number of snapshots is small and the signal-to-noise ratio (SNR) is low. Moveover, it has a higher spatial resolution compared to existing methods based on the ML criterion.
Highlights
Estimating the direction of arrivals (DOAs) of multiple plane waves using passive arrays is one of the central problems in radar, sonar, radio astronomy, and wireless communication
The parameters of SOS-semidefinite programming (SDP) in Algorithm 2 are chosen as e = 10−4 and the maximum number of iterations is K = 10
Based on the results shown in the four figures, we can conclude that the proposed method can provide a stable and accurate DOA estimation with a small number of snapshots and at low signal-to-noise ratio (SNR)
Summary
Estimating the direction of arrivals (DOAs) of multiple plane waves using passive arrays is one of the central problems in radar, sonar, radio astronomy, and wireless communication. To reduce the computational complexity, the subspace-based methods, such as multiple signal classification (MUSIC) [4], estimation of signal parameters via rotational invariance technique (ESPRIT) [5], and RootMUSIC [2], are proposed. These methods are efficient and can approach the optimal performance asymptotically. Compared with the existing methods, the proposed method can provide more stable and accurate DOA estimates when the SNR is low and/or the number of snapshots is small R M× N the set of M × N real matrices
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