A New Atomic Norm for DOA Estimation With Gain-Phase Errors
arXiv:1910.02207 · doi:10.1109/TSP.2020.3010749
Abstract
The problem of direction of arrival (DOA) estimation has been studied for decades as an essential technology in enabling radar, wireless communications, and array signal processing related applications. In this paper, the DOA estimation problem in the scenario with gain-phase errors is considered, and a sparse model is formulated by exploiting the signal sparsity in the spatial domain. By proposing a new atomic norm, named as GP-ANM, an optimization method is formulated via deriving a dual norm of GP-ANM. Then, the corresponding semidefinite program (SDP) is given to estimate the DOA efficiently, where the SDP is obtained based on the Schur complement. Moreover, a regularization parameter is obtained theoretically in the convex optimization problem. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and sparse-based methods in the scenario with gain-phase errors.
15 pages, 16 figures
References in corpus (4)
- Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
- Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays
- Quantized Spectral Compressed Sensing: Cramer-Rao Bounds and Recovery Algorithms
- Estimation of Angles of Arrival Through Superresolution -- A Soft Recovery Approach for General Antenna Geometries
Cited by in corpus (5)
- Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm
- Reconfigurable Intelligent Surface Aided Sparse DOA Estimation Method With Non-ULA
- Gridless DOA Estimation with Multiple Frequencies
- DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS
- NoncovANM: Gridless DOA Estimation for LPDF System