Abstract
Wideband sparse spectral estimation is generally formulated as a multi-dictionary/multi-measurement (MD/MM) problem which can be solved by using group sparsity techniques. In this paper, the MD/MM problem is reformulated as a single sparse indicative vector (SIV) recovery problem at the cost of introducing an additional system error. Thus, the number of unknowns is reduced greatly. We show that the system error can be neglected under certain conditions. We then present a new subband information fusion (SIF) method to estimate the SIV by jointly utilizing all the frequency bins. With orthogonal matching pursuit (OMP) leveraging the binary property of SIV’s components, we develop a SIF-OMP algorithm to reconstruct the SIV. The numerical simulations demonstrate the performance of the proposed method.
Highlights
Wideband direction-of-arrival (DOA) estimation has been a popular area of research due to the various applications in radar, sonar, seismology, communications, astrophysics, and many other fields [1–3]
The subband information fusion (SIF) algorithm utilizes all frequency bin information to recover a sparse indicative vector (SIV) at the expense of introducing an additional system error
We show that the additional system error can be neglected under certain conditions
Summary
Wideband direction-of-arrival (DOA) estimation has been a popular area of research due to the various applications in radar, sonar, seismology, communications, astrophysics, and many other fields [1–3]. A class of sparse signal representation (SSR) methods provide a new perspective for wideband DOA estimation [8–11]. The DOA estimation problem can be formulated as recovering a spatial sparse signal vector or matrix by minimizing the residual norm under sparsity constraint. It should be mentioned that the above SSR methods in frequency domain are generally formulated as a multi-dictionary/multi-measurement (MD/MM) joint optimization problem. These techniques usually do not use all the subband information to estimate DOAs with the aim of reducing unknown variables. Instead of estimating matrix V , we solve the DOA estimation problem efficiently by recovering a SIV which is used to represent the location of sources
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