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arxiv: 1703.08115 · v1 · pith:X3OMCS6Vnew · submitted 2017-03-23 · 💻 cs.SY · cs.SY

Relaxed Bi-quadratic Optimization for Joint Filter-Signal Design in Signal-Dependent STAP

classification 💻 cs.SY cs.SY
keywords problemoptimizationrelaxedjointalternativeanalyticallybi-quadraticbiquadratic
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We investigate an alternative solution method to the joint signal-beamformer optimization problem considered by Setlur and Rangaswamy[1]. First, we directly demonstrate that the problem, which minimizes the received noise, interference, and clutter power under a minimum variance distortionless response (MVDR) constraint, is generally non-convex and provide concrete insight into the nature of the nonconvexity. Second, we employ the theory of biquadratic optimization and semidefinite relaxations to produce a relaxed version of the problem, which we show to be convex. The optimality conditions of this relaxed problem are examined and a variety of potential solutions are found, both analytically and numerically.

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