NDLI: A new class of high-resolution and robust multi-dimensional spectral estimation algorithms

Content Provider	IEEE Xplore Digital Library
Author	Nikias, C. Raghuveer, M.
Copyright Year	1983
Description	Author affiliation: University of Connecticut, Storrs, CT (Nikias, C.)
Abstract	A new class of multi-dimensional (m-D,m=3,4) spectral estimation algorithms is introduced based on the minimum variance representations (MVR) of m-D (m=3,4) data fields. These representations are defined in the framework of linear prediction where it is shown that they may be classed into general categories depending upon the geometry of the prediction space. For example, in the 3-D case it is shown that there are four possible models: causal, semicausal I, semicausal II, and non-causal. The m-D (m=3,4) model formalisms and their linear-predictive and spectral interpretations are derived. The admissibility conditions of the spectral density function are also discussed. To obtain high-resolution spectral estimates from finite length m-D (m=3,4) data fields, the models are fitted to the data optimally in the sense of minimizing the covariance recursion errors within the prediction space considered. Computer-simulated short data fields consisting of two travelling waves embedded in noise are employed to demonstrate experimentally that the class of algorithms developed in this paper improves on the standard techniques for high-resolution and robustness in the presence of nonstationarities, such as envelope modulation.
Starting Page	859
Ending Page	862
File Size	130007
Page Count	4
File Format	PDF
DOI	10.1109/ICASSP.1983.1172045
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1983-04-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Geometry Predictive models Yield estimation Mathematical model Density functional theory Recursive estimation Computer errors Space exploration Embedded computing
Content Type	Text
Resource Type	Article

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