Abstract
This letter considers the dimension reduction for the space-time adaptive processing (STAP). An angle-Doppler channel selection strategy to maximize the lower bound of the output signal-to-clutter-plus-noise ratio (SCNR) is proposed. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form based on the majorization-minimization (MM) algorithm and solve it in an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance, especially when the number of samples are limited.
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