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Spherically Invariant Random Processes: Theory and Applications

  • Kung Yao
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 712)

Abstract

The theory and application of the class of spherically invariant random processes (SIRPs) are reviewed. An SIRP is a random process whose finite-dimensional distributions are scalar functions of quadratic forms in the corresponding sampled variables. Stochastic and system-theoretic properties are discussed, including a basic representation theorem which gives rise to a number of interesting properties concerning the detection, estimation, and interpretation of SIRPs. Applications are reviewed as well, including the use of SIRPs to model speech waveforms, radar clutter returns, and various radio propagation channel disturbances. The use of SIRP modeling in channel equalization and array processing is also described. Finally, some issues concerning the generation and simulation of SIRPs are discussed.

Keywords

Gaussian Process Array Processing Gaussian Random Process Gaussian Random Vector Atmospheric Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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SIRP Modeling in Equalization and Array Processing

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Generation and Simulation of SIRP

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Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Kung Yao
    • 1
  1. 1.Electrical Engineering DepartmentUniversity of CaliforniaLos AngelesUSA

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