5 papers
Direction Finding with Sparse Arrays Based on Variable Window Size Spatial Smoothing
Wesley S. Leite, Rodrigo C. de Lamare, Yuriy Zakharov +2
In this work, we introduce a variable window size (VWS) spatial smoothing framework that enhances coarray-based direction of arrival (DOA) estimation for sparse linear arrays. By c…
Direction of Arrival Estimation with Sparse Subarrays
W. Leite, R. C. de Lamare, Y. Zakharov +2
This paper proposes design techniques for partially-calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array ar…
Analysis of Partially-Calibrated Sparse Subarrays for Direction Finding with Extended Degrees of Freedom
W. S. Leite, R. C. de Lamare
This paper investigates the problem of direction-of-arrival (DOA) estimation using multiple partially-calibrated sparse subarrays. In particular, we present the Generalized Coarray…
Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery
W. Leite, R. C. de Lamare
This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector…
Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays
W. S. Leite, R. C. de Lamare
This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood Orthogonal Matching Pursui…