Improved Capon Estimator for High-Resolution DOA Estimation and Its Statistical Analysis

DOA estimation based on sparse Bayesian learning with moving synthetic virtual array

“…Consider a uniform linear array (ULA) that comprises $$M$$ elements, with a spacing of $$d$$ between adjacent elements. The output model for capturing far‐field signals using a ULA can be expressed as [7]: $$$$\begin{equation} {{\mathbf {Y}}_{ini}}{\left(\mathbf {t} \right)}={{\mathbf {A}}_{ini}}\mathbf {S}+\mathbf {N}, \end{equation}$$$$where $${{\mathbf {A}}_{ini}} = [ {{{\mathbf {a}}_{ini}}({{\theta _1}}), \ldots,{{\mathbf {a}}_{ini}}({{\theta _K}})}] \in {C^{M \times K}}$$ is the ULA steering vector matrix, $${{\mathbf {a}}_{ini}}\left({{\theta _k}} \right) = {\left[ {1{\text{ }}{e^{{\text{j}}2\pi \frac{{d\sin {\theta _k}}}{\lambda }}}, \ldots ,{e^{{\text{j}}2\pi \frac{{(M - 1)d\sin {\theta _k}}}{\lambda }}}} \right]^{\text{T}}}$$ is the ULA steering vector of the $$k\text{th}$$ signal source [8], …”