Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation
원문 초록
Despite many advantages of direction-of-arrivals (DOAs) in sparse representation domain, they have high computational complexity. This paper presents a new method for real-valued 2-D DOAs estimation of sources in a uniform circular array configuration. This method uses a transformation based on phase mode excitation in uniform circular arrays which called real beamspace L1-SVD (RB-L1SVD). This unitary transformation converts complex manifold matrix to real one, so that the computational complexity is decreased with respect to complex valued computations,its computation, at least, is one,fourth of the complex-valued case; moreover, some benefits from using this transformation are robustness to array imperfections, a better noise suppression because of exploiting an additional real structure, and etc. Numerical results demonstrate the better performance of the proposed approach over previous techniques such as C-L1SVD, RB-ESPRIT, and RB-MUSIC, especially in low signal-to-noise ratios.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
T-Rex: Fitting a Robust Factor Model via Expectation-Maximization
Reverberation-based Features for Sound Event Localization and Detection with Distance Estimation
Circulant ADMM-Net for Fast High-resolution DoA Estimation
Toeplitz-Hermitian ADMM-Net for DoA Estimation
PSELDNets: Pre-trained Neural Networks on Large-scale Synthetic Datasets for Sound Event Localization and Detection
AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.