Fast 2D Weighted Generalized Sparse Iterative Covariance-Based Estimation for Scanning Radar

The generalized sparse iterative covariance-based estimation (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-SPICE) algorithm has significant capability to enhance angular resolution in scanning radar applications. However, two drawbacks remain: one is that it focuses solely on the azimuth dimension and ignores the pitch dimension, and the other is that its computational complexity grows exponentially with increasing data size. To address these, a 2D weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-SPICE (W<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-SPICE) is proposed in this letter, which enables rapid azimuth-pitch sparse reconstruction of the scanning radar. The proposed algorithm is an extension of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-SPICE within the framework of weighted SPICE (WSPICE), and it is also a generalized version of the recent 2D WSPICE algorithm. Moreover, a selection criterion for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> is provided. Experiment results based on simulated and real radar data illustrate the superiority of the proposed method over its counterparts in the task of 2D resolution enhancement for the scanning radar.