Projects per year
The robust adaptive beamforming (RAB) problem for general-rank signal model with an uncertainty set defined through a matrix induced norm is considered. The worst-case signal-to-interference-plus-noise ratio (SINR) maximization RAB problem is formulated. First, the closed-form optimal value for a minimization problem of the least-squares residual over the matrix errors with an induced lp,q-norm constraint is derived. Then, the maximization problem is reformulated into the maximization of the difference between an l2-norm function and an lq-norm function, with a convex quadratic constraint. It is shown that for any q≥1 in the set of rational numbers, the maximization problem can be approximated by a sequence of second-order cone programming problems, with the ascent optimal values. The resultant beamvector for some q in the set with the maximal actual array output SINR, is treated as the candidate making the RAB design improved the most. In addition, a generalized RAB problem of maximizing the difference between an lp-norm function and an lq-norm function with the convex quadratic constraint is studied, and the actual array output SINR is further enhanced by properly selecting p and q. Simulation examples are presented to demonstrate the improved performance of the robust beamformers for certain matrix induced lp,q-norms.
- General-rank signal model
- Improved array output performance
- Matrix induced norms
- Robust adaptive beamforming
- Worst-case SINR maximization
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- 2 Finished
01/01/2019 → 31/12/2021
Project: Academy of Finland: Other research funding