Robust techniques for signal processing: A survey

Abstract

SUBJECT TERMS (Continue on reverse if necessary and identify by block number)S ta tis tic a l signal processing; robustness; uncertain s ta tis tic a l environments; detection and estimation; minimax design; non-Gaussion modelsIn recent years there has been much interest in robustness issues in general and in robust signal processing schemes in particular. Robust schemes are useful in situations phere imprecise Cl p tiio fii knowledge of input characteristics make the s e n s itiv ity of per-'ormance to deviations from assumed conditions an important factor in the design of good "signal processing schemes. In this survey we discuss the m^iyilmax approach for the design if robust methods for signal processing. This has proven to be a very useful approach lecause it leads to constructive procedures fo r designing robust schemes. Our emphasis is m the contributions which have been made in robust signal processing, although key results of other robust s ta tis tic a l procedures are also considered. Most of the results we survey lave been obtained in the past ten years, although some interesting e a rlie r ideas for linimax signal processing are also mentioned. This survey is organized in fiv e main parts, which deal separately with robust linear f i l t e r s for signal estimation, robust linear f i l t e r s fo r signal detection and related (Continued on reverse) Unclassif ied SECURITY CLASSIFICATION OF THIS PAGE•Unclassified 19. ABSTRACT (con t.) related applications, nonlinear methods for robust signal detection, nonlinear methods fo r robust estimation, and robust data quantization. The inter-relationships among many of these results are also discussed in the survey. S E C U R IT Y C L A S S IF IC A T IO N O F T H IS P ROBUST TECHNIQUES FOR SIGNAL PROCESSING; A SURVEY Invited PaperSaleem A . Kassam, Senior Member, IEEE, and H. Vincent Poor, Senior Member, IEEE AbstractIn recent years there has been much interest in robustness issues in general and in robust signal processing schemes in particular. Robust schemes are useful in situations where imprecise a priori knowledge o f input characteristics make the sensitivity o f performance to deviations from assumed conditions an important factor in the design o f good signal processing schemes. In this survey we discuss the mznimax approach for the design o f robust methods for signal processing. This has proven to be a very useful approach because it leads to constructive procedures for designing robust schemes. Our emphasis is on the contributions which have been made in robust signal processing, although key results of other robust statistical procedures are also considered. Most o f the results we survev have been obtained in the past ten years, although some interesting earlier ideas for minimay signal processing are also mentioned.This survey is organized in five main parts, which deal separately with robust linear filters for signal estimation, robust linear filters for signal detection and related applications, nonlinear methods for robust signal detection,...

Robust SINR-constrained MISO downlink beamforming: when is semidefinite programming relaxation tight?

“…In this case, the design goal of transmit beamforming is to minimize transmission power while ensuring that all the receive SINR requirements are satisfied with high probability. In contrast, the worst-case robust beamforming design (see [14][15][16][17][18][19][20]) assumes that the CSIT errors belong to some known bounded uncertainty sets. The robust beamforming vectors are designed to satisfy the SINR requirements for all possible channel realizations in the uncertainty regions.…”

Robust Chebyshev FIR Equalization

“…where F m is given by The condition λ ≥ 0 is also enforced by the matrix inequality and thus can be dropped to obtain (10), which completes the proof.…”

“…Robust equalization has been studied in the literature since the 1980's [10], [11]. More recently, ideas from the (worstcase) robust optimization [12]- [14] have been applied to least-squares equalization [15,Sec.…”

Technical challenges for cognitive radio in the TV white space spectrum

“…The spectrum sensing problem may be most effectively solved from a pattern recognition perspective; alternatively, the theory of robust detection [8] may prove to be another useful tool. It is still unclear how the challenge will be settled eventually, or if there exist any fundamental performance limits.…”